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• Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

  - by Pinal Dave

  Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• PC to USB transfer slow

  - by Vipin Ms

  I'm having trouble with USB transfer,not with external hard disk. Transfer starts with like, for the transfer of 700MB file it starts with 30mb/s and towards the end it stops at 0s and stays put for like 3-4 mins to transfer the last bit. I have tried different USB devices, but no luck. Is it a bug? Another important point is, in Kubuntu there is no such issue. So is it something related to Gnome? I'm using Ubuntu 11.10 64bit. Somebody please help, it's really annoying. Here are the details. PC all of my drives are in ext4. USB I tried ext3,ntfs and fat32. All having the same problem. Here are my USB controllers details: root@LAB:~# lspci|grep USB 00:1a.0 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #4 (rev 03) 00:1a.1 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #5 (rev 03) 00:1a.2 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #6 (rev 03) 00:1a.7 USB Controller: Intel Corporation 82801I (ICH9 Family) USB2 EHCI Controller #2 (rev 03) 00:1d.0 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #1 (rev 03) 00:1d.1 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #2 (rev 03) 00:1d.2 USB Controller: Intel Corporation 82801I (ICH9 Family) USB UHCI Controller #3 (rev 03) 00:1d.7 USB Controller: Intel Corporation 82801I (ICH9 Family) USB2 EHCI Controller #1 (rev 03) Here is an example of one transfer. I connected one of my 4GB usb device. Nov 24 12:01:25 LAB kernel: [ 1175.082175] userif-2: sent link up event. Nov 24 12:01:25 LAB kernel: [ 1695.684158] usb 2-2: new high speed USB device number 3 using ehci_hcd Nov 24 12:01:25 LAB mtp-probe: checking bus 2, device 3: "/sys/devices/pci0000:00/0000:00:1d.7/usb2/2-2" Nov 24 12:01:26 LAB mtp-probe: bus: 2, device: 3 was not an MTP device Nov 24 12:01:26 LAB kernel: [ 1696.132680] usbcore: registered new interface driver uas Nov 24 12:01:26 LAB kernel: [ 1696.142528] Initializing USB Mass Storage driver... Nov 24 12:01:26 LAB kernel: [ 1696.142919] scsi4 : usb-storage 2-2:1.0 Nov 24 12:01:26 LAB kernel: [ 1696.143146] usbcore: registered new interface driver usb-storage Nov 24 12:01:26 LAB kernel: [ 1696.143150] USB Mass Storage support registered. Nov 24 12:01:27 LAB kernel: [ 1697.141657] scsi 4:0:0:0: Direct-Access SanDisk U3 Cruzer Micro 8.02 PQ: 0 ANSI: 0 CCS Nov 24 12:01:27 LAB kernel: [ 1697.168827] sd 4:0:0:0: Attached scsi generic sg2 type 0 Nov 24 12:01:27 LAB kernel: [ 1697.169262] sd 4:0:0:0: [sdb] 7856127 512-byte logical blocks: (4.02 GB/3.74 GiB) Nov 24 12:01:27 LAB kernel: [ 1697.169762] sd 4:0:0:0: [sdb] Write Protect is off Nov 24 12:01:27 LAB kernel: [ 1697.169767] sd 4:0:0:0: [sdb] Mode Sense: 45 00 00 08 Nov 24 12:01:27 LAB kernel: [ 1697.171386] sd 4:0:0:0: [sdb] No Caching mode page present Nov 24 12:01:27 LAB kernel: [ 1697.171391] sd 4:0:0:0: [sdb] Assuming drive cache: write through Nov 24 12:01:27 LAB kernel: [ 1697.173503] sd 4:0:0:0: [sdb] No Caching mode page present Nov 24 12:01:27 LAB kernel: [ 1697.173510] sd 4:0:0:0: [sdb] Assuming drive cache: write through Nov 24 12:01:27 LAB kernel: [ 1697.175337] sdb: sdb1 After that I initiated one transfer. lsof -p 3575|tail -2 mv 3575 root 3r REG 8,8 1719599104 4325379 /media/Misc/The Tree of Life (2011) DVDRip XviD-MAXSPEED/The Tree of Life (2011) DVDRip XviD-MAXSPEED www.torentz.3xforum.ro.avi mv 3575 root 4w REG 8,17 1046347776 15 /media/SREE/The Tree of Life (2011) DVDRip XviD-MAXSPEED/The Tree of Life (2011) DVDRip XviD-MAXSPEED www.torentz.3xforum.ro.avi Here are the total time spent on that transfer. root@LAB:/media/SREE# time mv /media/Misc/The\ Tree\ of\ Life\ \(2011\)\ DVDRip\ XviD-MAXSPEED/ /media/SREE/ real 11m49.334s user 0m0.008s sys 0m5.260s root@LAB:/media/SREE# df -T|tail -2 /dev/sdb1 vfat 3918344 1679308 2239036 43% /media/SREE /dev/sda8 ext4 110110576 60096904 50013672 55% /media/Misc Do you think this is normal?? Approximately 12 minutes for 1.6Gb transfer? Thanks.

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• The Data Scientist

  - by BuckWoody

  A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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• Fraud Detection with the SQL Server Suite Part 2

  - by Dejan Sarka

  This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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• Ancillary Objects: Separate Debug ELF Files For Solaris

  - by Ali Bahrami

  We introduced a new object ELF object type in Solaris 11 Update 1 called the Ancillary Object. This posting describes them, using material originally written during their development, the PSARC arc case, and the Solaris Linker and Libraries Manual. ELF objects contain allocable sections, which are mapped into memory at runtime, and non-allocable sections, which are present in the file for use by debuggers and observability tools, but which are not mapped or used at runtime. Typically, all of these sections exist within a single object file. Ancillary objects allow them to instead go into a separate file. There are different reasons given for wanting such a feature. One can debate whether the added complexity is worth the benefit, and in most cases it is not. However, one important case stands out — customers with very large 32-bit objects who are not ready or able to make the transition to 64-bits. We have customers who build extremely large 32-bit objects. Historically, the debug sections in these objects have used the stabs format, which is limited, but relatively compact. In recent years, the industry has transitioned to the powerful but verbose DWARF standard. In some cases, the size of these debug sections is large enough to push the total object file size past the fundamental 4GB limit for 32-bit ELF object files. The best, and ultimately only, solution to overly large objects is to transition to 64-bits. However, consider environments where: Hundreds of users may be executing the code on large shared systems. (32-bits use less memory and bus bandwidth, and on sparc runs just as fast as 64-bit code otherwise). Complex finely tuned code, where the original authors may no longer be available. Critical production code, that was expensive to qualify and bring online, and which is otherwise serving its intended purpose without issue. Users in these risk adverse and/or high scale categories have good reasons to push 32-bits objects to the limit before moving on. Ancillary objects offer these users a longer runway. Design The design of ancillary objects is intended to be simple, both to help human understanding when examining elfdump output, and to lower the bar for debuggers such as dbx to support them. The primary and ancillary objects have the same set of section headers, with the same names, in the same order (i.e. each section has the same index in both files). A single added section of type SHT_SUNW_ANCILLARY is added to both objects, containing information that allows a debugger to identify and validate both files relative to each other. Given one of these files, the ancillary section allows you to identify the other. Allocable sections go in the primary object, and non-allocable ones go into the ancillary object. A small set of non-allocable objects, notably the symbol table, are copied into both objects. As noted above, most sections are only written to one of the two objects, but both objects have the same section header array. The section header in the file that does not contain the section data is tagged with the SHF_SUNW_ABSENT section header flag to indicate its placeholder status. Compiler writers and others who produce objects can set the SUNW_SHF_PRIMARY section header flag to mark non-allocable sections that should go to the primary object rather than the ancillary. If you don't request an ancillary object, the Solaris ELF format is unchanged. Users who don't use ancillary objects do not pay for the feature. This is important, because they exist to serve a small subset of our users, and must not complicate the common case. If you do request an ancillary object, the runtime behavior of the primary object will be the same as that of a normal object. There is no added runtime cost. The primary and ancillary object together represent a logical single object. This is facilitated by the use of a single set of section headers. One can easily imagine a tool that can merge a primary and ancillary object into a single file, or the reverse. (Note that although this is an interesting intellectual exercise, we don't actually supply such a tool because there's little practical benefit above and beyond using ld to create the files). Among the benefits of this approach are: There is no need for per-file symbol tables to reflect the contents of each file. The same symbol table that would be produced for a standard object can be used. The section contents are identical in either case — there is no need to alter data to accommodate multiple files. It is very easy for a debugger to adapt to these new files, and the processing involved can be encapsulated in input/output routines. Most of the existing debugger implementation applies without modification. The limit of a 4GB 32-bit output object is now raised to 4GB of code, and 4GB of debug data. There is also the future possibility (not currently supported) to support multiple ancillary objects, each of which could contain up to 4GB of additional debug data. It must be noted however that the 32-bit DWARF debug format is itself inherently 32-bit limited, as it uses 32-bit offsets between debug sections, so the ability to employ multiple ancillary object files may not turn out to be useful. Using Ancillary Objects (From the Solaris Linker and Libraries Guide) By default, objects contain both allocable and non-allocable sections. Allocable sections are the sections that contain executable code and the data needed by that code at runtime. Non-allocable sections contain supplemental information that is not required to execute an object at runtime. These sections support the operation of debuggers and other observability tools. The non-allocable sections in an object are not loaded into memory at runtime by the operating system, and so, they have no impact on memory use or other aspects of runtime performance no matter their size. For convenience, both allocable and non-allocable sections are normally maintained in the same file. However, there are situations in which it can be useful to separate these sections. To reduce the size of objects in order to improve the speed at which they can be copied across wide area networks. To support fine grained debugging of highly optimized code requires considerable debug data. In modern systems, the debugging data can easily be larger than the code it describes. The size of a 32-bit object is limited to 4 Gbytes. In very large 32-bit objects, the debug data can cause this limit to be exceeded and prevent the creation of the object. To limit the exposure of internal implementation details. Traditionally, objects have been stripped of non-allocable sections in order to address these issues. Stripping is effective, but destroys data that might be needed later. The Solaris link-editor can instead write non-allocable sections to an ancillary object. This feature is enabled with the -z ancillary command line option. $ ld ... -z ancillary[=outfile] ...By default, the ancillary file is given the same name as the primary output object, with a .anc file extension. However, a different name can be provided by providing an outfile value to the -z ancillary option. When -z ancillary is specified, the link-editor performs the following actions. All allocable sections are written to the primary object. In addition, all non-allocable sections containing one or more input sections that have the SHF_SUNW_PRIMARY section header flag set are written to the primary object. All remaining non-allocable sections are written to the ancillary object. The following non-allocable sections are written to both the primary object and ancillary object. .shstrtab The section name string table. .symtab The full non-dynamic symbol table. .symtab_shndx The symbol table extended index section associated with .symtab. .strtab The non-dynamic string table associated with .symtab. .SUNW_ancillary Contains the information required to identify the primary and ancillary objects, and to identify the object being examined. The primary object and all ancillary objects contain the same array of sections headers. Each section has the same section index in every file. Although the primary and ancillary objects all define the same section headers, the data for most sections will be written to a single file as described above. If the data for a section is not present in a given file, the SHF_SUNW_ABSENT section header flag is set, and the sh_size field is 0. This organization makes it possible to acquire a full list of section headers, a complete symbol table, and a complete list of the primary and ancillary objects from either of the primary or ancillary objects. The following example illustrates the underlying implementation of ancillary objects. An ancillary object is created by adding the -z ancillary command line option to an otherwise normal compilation. The file utility shows that the result is an executable named a.out, and an associated ancillary object named a.out.anc. $ cat hello.c #include <stdio.h> int main(int argc, char **argv) { (void) printf("hello, world\n"); return (0); } $ cc -g -zancillary hello.c $ file a.out a.out.anc a.out: ELF 32-bit LSB executable 80386 Version 1 [FPU], dynamically linked, not stripped, ancillary object a.out.anc a.out.anc: ELF 32-bit LSB ancillary 80386 Version 1, primary object a.out $ ./a.out hello worldThe resulting primary object is an ordinary executable that can be executed in the usual manner. It is no different at runtime than an executable built without the use of ancillary objects, and then stripped of non-allocable content using the strip or mcs commands. As previously described, the primary object and ancillary objects contain the same section headers. To see how this works, it is helpful to use the elfdump utility to display these section headers and compare them. The following table shows the section header information for a selection of headers from the previous link-edit example. Index Section Name Type Primary Flags Ancillary Flags Primary Size Ancillary Size 13 .text PROGBITS ALLOC EXECINSTR ALLOC EXECINSTR SUNW_ABSENT 0x131 0 20 .data PROGBITS WRITE ALLOC WRITE ALLOC SUNW_ABSENT 0x4c 0 21 .symtab SYMTAB 0 0 0x450 0x450 22 .strtab STRTAB STRINGS STRINGS 0x1ad 0x1ad 24 .debug_info PROGBITS SUNW_ABSENT 0 0 0x1a7 28 .shstrtab STRTAB STRINGS STRINGS 0x118 0x118 29 .SUNW_ancillary SUNW_ancillary 0 0 0x30 0x30 The data for most sections is only present in one of the two files, and absent from the other file. The SHF_SUNW_ABSENT section header flag is set when the data is absent. The data for allocable sections needed at runtime are found in the primary object. The data for non-allocable sections used for debugging but not needed at runtime are placed in the ancillary file. A small set of non-allocable sections are fully present in both files. These are the .SUNW_ancillary section used to relate the primary and ancillary objects together, the section name string table .shstrtab, as well as the symbol table.symtab, and its associated string table .strtab. It is possible to strip the symbol table from the primary object. A debugger that encounters an object without a symbol table can use the .SUNW_ancillary section to locate the ancillary object, and access the symbol contained within. The primary object, and all associated ancillary objects, contain a .SUNW_ancillary section that allows all the objects to be identified and related together. $ elfdump -T SUNW_ancillary a.out a.out.anc a.out: Ancillary Section: .SUNW_ancillary index tag value [0] ANC_SUNW_CHECKSUM 0x8724 [1] ANC_SUNW_MEMBER 0x1 a.out [2] ANC_SUNW_CHECKSUM 0x8724 [3] ANC_SUNW_MEMBER 0x1a3 a.out.anc [4] ANC_SUNW_CHECKSUM 0xfbe2 [5] ANC_SUNW_NULL 0 a.out.anc: Ancillary Section: .SUNW_ancillary index tag value [0] ANC_SUNW_CHECKSUM 0xfbe2 [1] ANC_SUNW_MEMBER 0x1 a.out [2] ANC_SUNW_CHECKSUM 0x8724 [3] ANC_SUNW_MEMBER 0x1a3 a.out.anc [4] ANC_SUNW_CHECKSUM 0xfbe2 [5] ANC_SUNW_NULL 0 The ancillary sections for both objects contain the same number of elements, and are identical except for the first element. Each object, starting with the primary object, is introduced with a MEMBER element that gives the file name, followed by a CHECKSUM that identifies the object. In this example, the primary object is a.out, and has a checksum of 0x8724. The ancillary object is a.out.anc, and has a checksum of 0xfbe2. The first element in a .SUNW_ancillary section, preceding the MEMBER element for the primary object, is always a CHECKSUM element, containing the checksum for the file being examined. The presence of a .SUNW_ancillary section in an object indicates that the object has associated ancillary objects. The names of the primary and all associated ancillary objects can be obtained from the ancillary section from any one of the files. It is possible to determine which file is being examined from the larger set of files by comparing the first checksum value to the checksum of each member that follows. Debugger Access and Use of Ancillary Objects Debuggers and other observability tools must merge the information found in the primary and ancillary object files in order to build a complete view of the object. This is equivalent to processing the information from a single file. This merging is simplified by the primary object and ancillary objects containing the same section headers, and a single symbol table. The following steps can be used by a debugger to assemble the information contained in these files. Starting with the primary object, or any of the ancillary objects, locate the .SUNW_ancillary section. The presence of this section identifies the object as part of an ancillary group, contains information that can be used to obtain a complete list of the files and determine which of those files is the one currently being examined. Create a section header array in memory, using the section header array from the object being examined as an initial template. Open and read each file identified by the .SUNW_ancillary section in turn. For each file, fill in the in-memory section header array with the information for each section that does not have the SHF_SUNW_ABSENT flag set. The result will be a complete in-memory copy of the section headers with pointers to the data for all sections. Once this information has been acquired, the debugger can proceed as it would in the single file case, to access and control the running program. Note - The ELF definition of ancillary objects provides for a single primary object, and an arbitrary number of ancillary objects. At this time, the Oracle Solaris link-editor only produces a single ancillary object containing all non-allocable sections. This may change in the future. Debuggers and other observability tools should be written to handle the general case of multiple ancillary objects. ELF Implementation Details (From the Solaris Linker and Libraries Guide) To implement ancillary objects, it was necessary to extend the ELF format to add a new object type (ET_SUNW_ANCILLARY), a new section type (SHT_SUNW_ANCILLARY), and 2 new section header flags (SHF_SUNW_ABSENT, SHF_SUNW_PRIMARY). In this section, I will detail these changes, in the form of diffs to the Solaris Linker and Libraries manual. Part IV ELF Application Binary Interface Chapter 13: Object File Format Object File Format Edit Note: This existing section at the beginning of the chapter describes the ELF header. There's a table of object file types, which now includes the new ET_SUNW_ANCILLARY type. e_type Identifies the object file type, as listed in the following table. NameValueMeaning ET_NONE0No file type ET_REL1Relocatable file ET_EXEC2Executable file ET_DYN3Shared object file ET_CORE4Core file ET_LOSUNW0xfefeStart operating system specific range ET_SUNW_ANCILLARY0xfefeAncillary object file ET_HISUNW0xfefdEnd operating system specific range ET_LOPROC0xff00Start processor-specific range ET_HIPROC0xffffEnd processor-specific range Sections Edit Note: This overview section defines the section header structure, and provides a high level description of known sections. It was updated to define the new SHF_SUNW_ABSENT and SHF_SUNW_PRIMARY flags and the new SHT_SUNW_ANCILLARY section. ... sh_type Categorizes the section's contents and semantics. Section types and their descriptions are listed in Table 13-5. sh_flags Sections support 1-bit flags that describe miscellaneous attributes. Flag definitions are listed in Table 13-8. ... Table 13-5 ELF Section Types, sh_type NameValue . . . SHT_LOSUNW0x6fffffee SHT_SUNW_ancillary0x6fffffee . . . ... SHT_LOSUNW - SHT_HISUNW Values in this inclusive range are reserved for Oracle Solaris OS semantics. SHT_SUNW_ANCILLARY Present when a given object is part of a group of ancillary objects. Contains information required to identify all the files that make up the group. See Ancillary Section. ... Table 13-8 ELF Section Attribute Flags NameValue . . . SHF_MASKOS0x0ff00000 SHF_SUNW_NODISCARD0x00100000 SHF_SUNW_ABSENT0x00200000 SHF_SUNW_PRIMARY0x00400000 SHF_MASKPROC0xf0000000 . . . ... SHF_SUNW_ABSENT Indicates that the data for this section is not present in this file. When ancillary objects are created, the primary object and any ancillary objects, will all have the same section header array, to facilitate merging them to form a complete view of the object, and to allow them to use the same symbol tables. Each file contains a subset of the section data. The data for allocable sections is written to the primary object while the data for non-allocable sections is written to an ancillary file. The SHF_SUNW_ABSENT flag is used to indicate that the data for the section is not present in the object being examined. When the SHF_SUNW_ABSENT flag is set, the sh_size field of the section header must be 0. An application encountering an SHF_SUNW_ABSENT section can choose to ignore the section, or to search for the section data within one of the related ancillary files. SHF_SUNW_PRIMARY The default behavior when ancillary objects are created is to write all allocable sections to the primary object and all non-allocable sections to the ancillary objects. The SHF_SUNW_PRIMARY flag overrides this behavior. Any output section containing one more input section with the SHF_SUNW_PRIMARY flag set is written to the primary object without regard for its allocable status. ... Two members in the section header, sh_link, and sh_info, hold special information, depending on section type. Table 13-9 ELF sh_link and sh_info Interpretation sh_typesh_linksh_info . . . SHT_SUNW_ANCILLARY The section header index of the associated string table. 0 . . . Special Sections Edit Note: This section describes the sections used in Solaris ELF objects, using the types defined in the previous description of section types. It was updated to define the new .SUNW_ancillary (SHT_SUNW_ANCILLARY) section. Various sections hold program and control information. Sections in the following table are used by the system and have the indicated types and attributes. Table 13-10 ELF Special Sections NameTypeAttribute . . . .SUNW_ancillarySHT_SUNW_ancillaryNone . . . ... .SUNW_ancillary Present when a given object is part of a group of ancillary objects. Contains information required to identify all the files that make up the group. See Ancillary Section for details. ... Ancillary Section Edit Note: This new section provides the format reference describing the layout of a .SUNW_ancillary section and the meaning of the various tags. Note that these sections use the same tag/value concept used for dynamic and capabilities sections, and will be familiar to anyone used to working with ELF. In addition to the primary output object, the Solaris link-editor can produce one or more ancillary objects. Ancillary objects contain non-allocable sections that would normally be written to the primary object. When ancillary objects are produced, the primary object and all of the associated ancillary objects contain a SHT_SUNW_ancillary section, containing information that identifies these related objects. Given any one object from such a group, the ancillary section provides the information needed to identify and interpret the others. This section contains an array of the following structures. See sys/elf.h. typedef struct { Elf32_Word a_tag; union { Elf32_Word a_val; Elf32_Addr a_ptr; } a_un; } Elf32_Ancillary; typedef struct { Elf64_Xword a_tag; union { Elf64_Xword a_val; Elf64_Addr a_ptr; } a_un; } Elf64_Ancillary; For each object with this type, a_tag controls the interpretation of a_un. a_val These objects represent integer values with various interpretations. a_ptr These objects represent file offsets or addresses. The following ancillary tags exist. Table 13-NEW1 ELF Ancillary Array Tags NameValuea_un ANC_SUNW_NULL0Ignored ANC_SUNW_CHECKSUM1a_val ANC_SUNW_MEMBER2a_ptr ANC_SUNW_NULL Marks the end of the ancillary section. ANC_SUNW_CHECKSUM Provides the checksum for a file in the c_val element. When ANC_SUNW_CHECKSUM precedes the first instance of ANC_SUNW_MEMBER, it provides the checksum for the object from which the ancillary section is being read. When it follows an ANC_SUNW_MEMBER tag, it provides the checksum for that member. ANC_SUNW_MEMBER Specifies an object name. The a_ptr element contains the string table offset of a null-terminated string, that provides the file name. An ancillary section must always contain an ANC_SUNW_CHECKSUM before the first instance of ANC_SUNW_MEMBER, identifying the current object. Following that, there should be an ANC_SUNW_MEMBER for each object that makes up the complete set of objects. Each ANC_SUNW_MEMBER should be followed by an ANC_SUNW_CHECKSUM for that object. A typical ancillary section will therefore be structured as: TagMeaning ANC_SUNW_CHECKSUMChecksum of this object ANC_SUNW_MEMBERName of object #1 ANC_SUNW_CHECKSUMChecksum for object #1 . . . ANC_SUNW_MEMBERName of object N ANC_SUNW_CHECKSUMChecksum for object N ANC_SUNW_NULL An object can therefore identify itself by comparing the initial ANC_SUNW_CHECKSUM to each of the ones that follow, until it finds a match. Related Other Work The GNU developers have also encountered the need/desire to support separate debug information files, and use the solution detailed at http://sourceware.org/gdb/onlinedocs/gdb/Separate-Debug-Files.html. At the current time, the separate debug file is constructed by building the standard object first, and then copying the debug data out of it in a separate post processing step, Hence, it is limited to a total of 4GB of code and debug data, just as a single object file would be. They are aware of this, and I have seen online comments indicating that they may add direct support for generating these separate files to their link-editor. It is worth noting that the GNU objcopy utility is available on Solaris, and that the Studio dbx debugger is able to use these GNU style separate debug files even on Solaris. Although this is interesting in terms giving Linux users a familiar environment on Solaris, the 4GB limit means it is not an answer to the problem of very large 32-bit objects. We have also encountered issues with objcopy not understanding Solaris-specific ELF sections, when using this approach. The GNU community also has a current effort to adapt their DWARF debug sections in order to move them to separate files before passing the relocatable objects to the linker. The details of Project Fission can be found at http://gcc.gnu.org/wiki/DebugFission. The goal of this project appears to be to reduce the amount of data seen by the link-editor. The primary effort revolves around moving DWARF data to separate .dwo files so that the link-editor never encounters them. The details of modifying the DWARF data to be usable in this form are involved — please see the above URL for details.

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• Why Standards Place Limits on Data Transfer Rates?

  - by Mehrdad

  This is a rather general question about hardware and standards in general: Why do they place limits on data transfer rates, and disallow manufacturers from exceeding those rates? (E.g. 100 Mbit/s for Wireless G, 150 Mbit/s for Wireless N, ...) Why not allow for some sort of handshake protocol, whereby the devices being connected agree upon the maximum throughput that they each support, and use that value instead? Why does there have to be a hard-coded limit, which would require a new standard for every single improvement to a data rate?

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• Big Data – Beginning Big Data Series Next Month in 21 Parts

  - by Pinal Dave

  Big Data is the next big thing. There was a time when we used to talk in terms of MB and GB of the data. However, the industry is changing and we are now moving to a conversation where we discuss about data in Petabyte, Exabyte and Zettabyte. It seems that the world is now talking about increased Volume of the data. In simple world we all think that Big Data is nothing but plenty of volume. In reality Big Data is much more than just a huge volume of the data. When talking about the data we need to understand about variety and volume along with volume. Though Big data look like a simple concept, it is extremely complex subject when we attempt to start learning the same. My Journey I have recently presented on Big Data in quite a few organizations and I have received quite a few questions during this roadshow event. I have collected all the questions which I have received and decided to post about them on the blog. In the month of October 2013, on every weekday we will be learning something new about Big Data. Every day I will share a concept/question and in the same blog post we will learn the answer of the same. Big Data – Plenty of Questions I received quite a few questions during my road trip. Here are few of the questions. I want to learn Big Data – where should I start? Do I need to know SQL to learn Big Data? What is Hadoop? There are so many organizations talking about Big Data, and every one has a different approach. How to start with big Data? Do I need to know Java to learn about Big Data? What is different between various NoSQL languages. I will attempt to answer most of the questions during the month long series in the next month. Big Data – Big Subject Big Data is a very big subject and I no way claim that I will be covering every single big data concept in this series. However, I promise that I will be indeed sharing lots of basic concepts which are revolving around Big Data. We will discuss from fundamentals about Big Data and continue further learning about it. I will attempt to cover the concept so simple that many of you might have wondered about it but afraid to ask. Your Role! During this series next month, I need your one help. Please keep on posting questions you might have related to big data as blog post comments and on Facebook Page. I will monitor them closely and will try to answer them as well during this series. Now make sure that you do not miss any single blog post in this series as every blog post will be linked to each other. You can subscribe to my feed or like my Facebook page or subscribe via email (by entering email in the blog post). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Big Data, PostADay, SQL, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Transfer websites and domains to new server

  - by Albert

  We have currently around 40 websites and 80+ domains/sub-domains in a shared 1&1 hosting package, and we just acquired a managed dedicated server with 1&1 as well. Now it's time to start transferring everything over to the new server. Transferring just the websites and databases wouldn't be a problem, it would take time but it's pretty straight forward. The problem comes when transferring the domains, let me explain why. Many of the websites we have are accessible via sub-domains of a parent domain. Ideally, we would like to transfer the sites one by one, in order to check for each one that everything works fine in the new server. However, since we also need to transfer the domain so it's managed in the new server, once we do that means that all the websites using that domain need to be already in the new server before transferring that domain, thus not allowing the "one by one" philosophy. Another issue is the downtime when transferring the domain, from the moment it stops working in the hosting package and becomes active in the new server. I believe there's nothing we can do here. So my question is if there's any way we can do the "one by one" transferring of the websites (and their corresponding sub-domains) in the circumstances described above. One idea I had would be: 1. Let's say we have website A, which is accessible using subdomain.mydomain.com (and there are many other websites accessible via other sub-domains of mydomain.com) 2. Transfer the files of website A to the new server 3. Point a test domain in the new server to the website A's folder (the new server comes with a "test" domain) 4. Test if website A works with that "test" domain 5. In the old hosting, somehow point the real sub-domain (subdomain.mydomain.com) to the new location of website A, in a way that user always see the same URL as always 6. Repeat 2-5 for every website belonging to the same domain 7. Once all are working in the new server, do the actual transfer of the domain to the new server, and then re-create all the sub-domains and point them to their corresponding website That way, users wouldn't notice that there's been a change (except for a small down time of the websites when doing the domain transfer). The part I'm not sure about is point 5 of the above. Is there any way to do that? I mean do it in a way that users see the original domain all the time in their browser, even for internal pages (so not only for the "home page", which would be sub-domain.mydomain.com, but also for example for the contact page, which would be sub-domain.mydomain.com/contact.php). Is there any way to do this? Or are we SOL and we're going to have to transfer all at the same time?

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• How can I electronically transfer money to another account using Bank Transfer (BACS)

  - by Mooktakim Ahmed

  I'm working on a project where we collect payments from users using credit/debit/PayPal payments. The service is taking payments from users on behalf of a 3rd party organisation. Once we take the payment, minus fees, we want to transfer the amount to the organisations bank account. For now, what we can do is pay the organisation using Online Banking BACS bank transfer. But I would like to know if there is a way to do this automatically using an API. If we need to somehow register the 3rd parties bank account details before making transfers, this is fine. We just want to automate the whole process, since at the moment the transfer is a manual step. Are there any gateways or APIs I can use for this? In the UK?

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• Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is HDFS. In this article we will take a quick look at the importance of the Relational Database in Big Data world. A Big Question? Here are a few questions I often received since the beginning of the Big Data Series - Does the relational database have no space in the story of the Big Data? Does relational database is no longer relevant as Big Data is evolving? Is relational database not capable to handle Big Data? Is it true that one no longer has to learn about relational data if Big Data is the final destination? Well, every single time when I hear that one person wants to learn about Big Data and is no longer interested in learning about relational database, I find it as a bit far stretched. I am not here to give ambiguous answers of It Depends. I am personally very clear that one who is aspiring to become Big Data Scientist or Big Data Expert they should learn about relational database. NoSQL Movement The reason for the NoSQL Movement in recent time was because of the two important advantages of the NoSQL databases. Performance Flexible Schema In personal experience I have found that when I use NoSQL I have found both of the above listed advantages when I use NoSQL database. There are instances when I found relational database too much restrictive when my data is unstructured as well as they have in the datatype which my Relational Database does not support. It is the same case when I have found that NoSQL solution performing much better than relational databases. I must say that I am a big fan of NoSQL solutions in the recent times but I have also seen occasions and situations where relational database is still perfect fit even though the database is growing increasingly as well have all the symptoms of the big data. Situations in Relational Database Outperforms Adhoc reporting is the one of the most common scenarios where NoSQL is does not have optimal solution. For example reporting queries often needs to aggregate based on the columns which are not indexed as well are built while the report is running, in this kind of scenario NoSQL databases (document database stores, distributed key value stores) database often does not perform well. In the case of the ad-hoc reporting I have often found it is much easier to work with relational databases. SQL is the most popular computer language of all the time. I have been using it for almost over 10 years and I feel that I will be using it for a long time in future. There are plenty of the tools, connectors and awareness of the SQL language in the industry. Pretty much every programming language has a written drivers for the SQL language and most of the developers have learned this language during their school/college time. In many cases, writing query based on SQL is much easier than writing queries in NoSQL supported languages. I believe this is the current situation but in the future this situation can reverse when No SQL query languages are equally popular. ACID (Atomicity Consistency Isolation Durability) – Not all the NoSQL solutions offers ACID compliant language. There are always situations (for example banking transactions, eCommerce shopping carts etc.) where if there is no ACID the operations can be invalid as well database integrity can be at risk. Even though the data volume indeed qualify as a Big Data there are always operations in the application which absolutely needs ACID compliance matured language. The Mixed Bag I have often heard argument that all the big social media sites now a days have moved away from Relational Database. Actually this is not entirely true. While researching about Big Data and Relational Database, I have found that many of the popular social media sites uses Big Data solutions along with Relational Database. Many are using relational databases to deliver the results to end user on the run time and many still uses a relational database as their major backbone. Here are a few examples: Facebook uses MySQL to display the timeline. (Reference Link) Twitter uses MySQL. (Reference Link) Tumblr uses Sharded MySQL (Reference Link) Wikipedia uses MySQL for data storage. (Reference Link) There are many for prominent organizations which are running large scale applications uses relational database along with various Big Data frameworks to satisfy their various business needs. Summary I believe that RDBMS is like a vanilla ice cream. Everybody loves it and everybody has it. NoSQL and other solutions are like chocolate ice cream or custom ice cream – there is a huge base which loves them and wants them but not every ice cream maker can make it just right  for everyone’s taste. No matter how fancy an ice cream store is there is always plain vanilla ice cream available there. Just like the same, there are always cases and situations in the Big Data’s story where traditional relational database is the part of the whole story. In the real world scenarios there will be always the case when there will be need of the relational database concepts and its ideology. It is extremely important to accept relational database as one of the key components of the Big Data instead of treating it as a substandard technology. Ray of Hope – NewSQL In this module we discussed that there are places where we need ACID compliance from our Big Data application and NoSQL will not support that out of box. There is a new termed coined for the application/tool which supports most of the properties of the traditional RDBMS and supports Big Data infrastructure – NewSQL. Tomorrow In tomorrow’s blog post we will discuss about NewSQL. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• To sample or not to sample...

  - by [email protected]

  Ideally, we would know the exact answer to every question. How many people support presidential candidate A vs. B? How many people suffer from H1N1 in a given state? Does this batch of manufactured widgets have any defective parts? Knowing exact answers is expensive in terms of time and money and, in most cases, is impractical if not impossible. Consider asking every person in a region for their candidate preference, testing every person with flu symptoms for H1N1 (assuming every person reported when they had flu symptoms), or destructively testing widgets to determine if they are "good" (leaving no product to sell). Knowing exact answers, fortunately, isn't necessary or even useful in many situations. Understanding the direction of a trend or statistically significant results may be sufficient to answer the underlying question: who is likely to win the election, have we likely reached a critical threshold for flu, or is this batch of widgets good enough to ship? Statistics help us to answer these questions with a certain degree of confidence. This focuses on how we collect data. In data mining, we focus on the use of data, that is data that has already been collected. In some cases, we may have all the data (all purchases made by all customers), in others the data may have been collected using sampling (voters, their demographics and candidate choice). Building data mining models on all of your data can be expensive in terms of time and hardware resources. Consider a company with 40 million customers. Do we need to mine all 40 million customers to get useful data mining models? The quality of models built on all data may be no better than models built on a relatively small sample. Determining how much is a reasonable amount of data involves experimentation. When starting the model building process on large datasets, it is often more efficient to begin with a small sample, perhaps 1000 - 10,000 cases (records) depending on the algorithm, source data, and hardware. This allows you to see quickly what issues might arise with choice of algorithm, algorithm settings, data quality, and need for further data preparation. Instead of waiting for a model on a large dataset to build only to find that the results don't meet expectations, once you are satisfied with the results on the initial sample, you can  take a larger sample to see if model quality improves, and to get a sense of how the algorithm scales to the particular dataset. If model accuracy or quality continues to improve, consider increasing the sample size. Sampling in data mining is also used to produce a held-aside or test dataset for assessing classification and regression model accuracy. Here, we reserve some of the build data (data that includes known target values) to be used for an honest estimate of model error using data the model has not seen before. This sampling transformation is often called a split because the build data is split into two randomly selected sets, often with 60% of the records being used for model building and 40% for testing. Sampling must be performed with care, as it can adversely affect model quality and usability. Even a truly random sample doesn't guarantee that all values are represented in a given attribute. This is particularly troublesome when the attribute with omitted values is the target. A predictive model that has not seen any examples for a particular target value can never predict that target value! For other attributes, values may consist of a single value (a constant attribute) or all unique values (an identifier attribute), each of which may be excluded during mining. Values from categorical predictor attributes that didn't appear in the training data are not used when testing or scoring datasets. In subsequent posts, we'll talk about three sampling techniques using Oracle Database: simple random sampling without replacement, stratified sampling, and simple random sampling with replacement.

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• Best way to store a large amount of game objects and update the ones onscreen

  - by user3002473

  Good afternoon guys! I'm a young beginner game developer working on my first large scale game project and I've run into a situation where I'm not quite sure what the best solution may be (if there is a lone solution). The question may be vague (if anyone can think of a better title after having read the question, please edit it) or broad but I'm not quite sure what to do and I thought it would help just to discuss the problem with people more educated in the field. Before we get started, here are some of the questions I've looked at for help in the past: Best way to keep track of game objects Elegant way to simulate large amounts of entities within a game world What is the most efficient container to store dynamic game objects in? I've also read articles about different data structures commonly used in games to store game objects such as this one about slot maps, but none of them are really what I'm looking for. Also, if it helps at all I'm using Python 3 to design the game. It has to be Python 3, if I could I would use C++ or Unityscript or something else, but I'm restricted to having to use Python 3. My game will be a form of side scroller shooter game. In said game the player will traverse large rooms with large amounts of enemies and other game objects to update (think some of the larger areas in Cave Story or Iji). The player obviously can't see the entire room all at once, so there is a viewport that follows the player around and renders only a selection of the room and the game objects that it contains. This is not a foreign concept. The part that's getting me confused has to do with how certain game objects are updated. Some of them are to be updated constantly, regardless of whether or not they can be seen. Other objects however are only to be updated when they are onscreen (for example, an enemy would only be updated to react to the player when it is onscreen or when it is in a certain range of the screen). Another problem is that game objects have to be easily referable by other game objects; something that happens in the player's update() method may affect another object in the world. Collision detection in games is always a serious problem. I need a way of containing the game objects such that it minimizes the number of cases when testing for collisions against one another. The final problem is that of creating and destroying game objects. I think this problem is pretty self explanatory. To store the game objects then I've considered a number of different methods. The original method I had was to simply store all the objects in a hash table by an id. This method was simple, and decently fast as it allows all the objects to be looked up in O(1) complexity, and also allows them to be deleted fairly easily. Hash collisions would not be a major problem; I wasn't originally planning on using computer generated ids to store the game objects I was going to rely on them all using ids given to them by the game designer (such names would be strings like 'Player' or 'EnemyWeapon4'), and even if I did use computer generated ids, if I used a decent hashing algorithm then the chances of collisions would be around 1 in 4 billion. The problem with using a hash table however is that it is inefficient in checking to see what objects are in range of the viewport. Considering the fact that certain game objects move (as well as the viewport itself), the only solution I could think of in order to only update objects that are in the viewport would be to iterate through every object in the hash table and check if it is in the viewport or not, updating only the ones that are in the valid area. This would be incredibly slow in scenarios where the amount of game objects exceeds 500, or even 200. The second solution was to store everything in a 2-d list. The world is partitioned up into cells (a tilemap essentially), where each cell or tile is the same size and is square. Each cell would contain a list of the game objects that are currently occupying it (each game object would be inserted into a cell depending on the center of the object's collision mask). A 2-d list would allow me to take the top-left and bottom-right corners of the viewport and easily grab a rectangular area of the grid containing only the cells containing entities that are in valid range to be updated. This method also solves the problem of collision detection; when I take an entity I can find the cell that it is currently in, then check only against entities in it's cell and the 8 cells around it. One problem with this system however is that it prohibits easy lookup of game objects. One solution I had would be to simultaneously keep a hash table that would contain all the positions of the objects in the 2-d list indexed by the id of said object. The major problem with a 2-d list is that it would need to be rebuilt every single game frame (along with the hash table of object positions), which may be a serious detriment to game speed. Both systems have ups and downs and seem to solve some of each other's problems, however using them both together doesn't seem like the best solution either. If anyone has any thoughts, ideas, suggestions, comments, opinions or solutions on new data structures or better implementations of the existing data structures I have in mind, please post, any and all criticism and help is welcome. Thanks in advance! EDIT: Please don't close the question because it has a bad title, I'm just bad with names!

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• Kipróbálható az ingyenes új Oracle Data Miner 11gR2 grafikus workflow-val

  - by Fekete Zoltán

  Oracle Data Mining technológiai információs oldal. Oracle Data Miner 11g Release 2 - Early Adopter oldal. Megjelent, letöltheto és kipróbálható az Oracle Data Mining, az Oracle adatbányászat új grafikus felülete, az Oracle Data Miner 11gR2. Az Oracle Data Minerhez egyszeruen az SQL Developer-t kell letöltenünk, mivel az adatbányászati felület abból indítható. Az Oracle Data Mining az Oracle adatbáziskezelobe ágyazott adatbányászati motor, ami az Oracle Database Enterprise Edition opciója. Az adatbányászat az adattárházak elemzésének kifinomult eszköze és folyamata. Az Oracle Data Mining in-database-mining elonyeit felvonultatja: - nincs felesleges adatmozgatás, a teljes adatbányászati folyamatban az adatbázisban maradnak az adatok - az adatbányászati modellek is az Oracle adatbázisban vannak - az adatbányászati eredmények, cluster adatok, döntések, valószínuségek, stb. szintén az adatbázisban keletkeznek, és ott közvetlenül elemezhetoek Az új ingyenes Data Miner felület "hatalmas gazdagodáson" ment keresztül az elozo verzióhoz képest. - grafikus adatbányászati workflow szerkesztés és futtatás jelent meg! - továbbra is ingyenes - kibovült a felület - új elemzési lehetoségekkel bovült - az SQL Developer 3.0 felületrol indítható, ez megkönnyíti az adatbányászati funkciók meghívását az adatbázisból, ha épp nem a grafikus felületetet szeretnénk erre használni Az ingyenes Data Miner felület az Oracle SQL Developer kiterjesztéseként érheto el, így az elemzok közvetlenül dolgozhatnak az adatokkal az adatbázisban és a Data Miner grafikus felülettel is, építhetnek és kiértékelhetnek, futtathatnak modelleket, predikciókat tehetnek és elemezhetnek, támogatást kapva az adatbányászati módszertan megvalósítására. A korábbi Oracle Data Miner felület a Data Miner Classic néven fut és továbbra is letöltheto az OTN-rol. Az új Data Miner GUI-ból egy képernyokép: Milyen feladatokra ad megoldási lehetoséget az Oracle Data Mining: - ügyfél viselkedés megjövendölése, prediktálása - a "legjobb" ügyfelek eredményes megcélzása - ügyfél megtartás, elvándorlás kezelés (churn) - ügyfél szegmensek, klaszterek, profilok keresése és vizsgálata - anomáliák, visszaélések felderítése - stb.

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• Importing Multiple Schemas to a Model in Oracle SQL Developer Data Modeler

  - by thatjeffsmith

  Your physical data model might stretch across multiple Oracle schemas. Or maybe you just want a single diagram containing tables, views, etc. spanning more than a single user in the database. The process for importing a data dictionary is the same, regardless if you want to suck in objects from one schema, or many schemas. Let’s take a quick look at how to get started with a data dictionary import. I’m using Oracle SQL Developer in this example. The process is nearly identical in Oracle SQL Developer Data Modeler – the only difference being you’ll use the ‘File’ menu to get started versus the ‘File – Data Modeler’ menu in SQL Developer. Remember, the functionality is exactly the same whether you use SQL Developer or SQL Developer Data Modeler when it comes to the data modeling features – you’ll just have a cleaner user interface in SQL Developer Data Modeler. Importing a Data Dictionary to a Model You’ll want to open or create your model first. You can import objects to an existing or new model. The easiest way to get started is to simply open the ‘Browser’ under the View menu. The Browser allows you to navigate your open designs/models You’ll see an ‘Untitled_1′ model by default. I’ve renamed mine to ‘hr_sh_scott_demo.’ Now go back to the File menu, and expand the ‘Data Modeler’ section, and select ‘Import – Data Dictionary.’ This is a fancy way of saying, ‘suck objects out of the database into my model’ Connect! If you haven’t already defined a connection to the database you want to reverse engineer, you’ll need to do that now. I’m going to assume you already have that connection – so select it, and hit the ‘Next’ button. Select the Schema(s) to be imported Select one or more schemas you want to import The schemas selected on this page of the wizard will dictate the lists of tables, views, synonyms, and everything else you can choose from in the next wizard step to import. For brevity, I have selected ALL tables, views, and synonyms from 3 different schemas: HR SCOTT SH Once I hit the ‘Finish’ button in the wizard, SQL Developer will interrogate the database and add the objects to our model. The Big Model and the 3 Little Models I can now see ALL of the objects I just imported in the ‘hr_sh_scott_demo’ relational model in my design tree, and in my relational diagram. Quick Tip: Oracle SQL Developer calls what most folks think of as a ‘Physical Model’ the ‘Relational Model.’ Same difference, mostly. In SQL Developer, a Physical model allows you to define partitioning schemes, advanced storage parameters, and add your PL/SQL code. You can have multiple physical models per relational models. For example I might have a 4 Node RAC in Production that uses partitioning, but in test/dev, only have a single instance with no partitioning. I can have models for both of those physical implementations. The list of tables in my relational model Wouldn’t it be nice if I could segregate the objects based on their schema? Good news, you can! And it’s done by default Several of you might already know where I’m going with this – SUBVIEWS. You can easily create a ‘SubView’ by selecting one or more objects in your model or diagram and add them to a new SubView. SubViews are just mini-models. They contain a subset of objects from the main model. This is very handy when you want to break your model into smaller, more digestible parts. The model information is identical across the model and subviews, so you don’t have to worry about making a change in one place and not having it propagate across your design. SubViews can be used as filters when you create reports and exports as well. So instead of generating a PDF for everything, just show me what’s in my ‘ABC’ subview. But, I don’t want to do any work! Remember, I’m really lazy. More good news – it’s already done by default! The schemas are automatically used to create default SubViews Auto-Navigate to the Object in the Diagram In the subview tree node, right-click on the object you want to navigate to. You can ask to be taken to the main model view or to the SubView location. If you haven’t already opened the SubView in the diagram, it will be automatically opened for you. The SubView diagram only contains the objects from that SubView Your SubView might still be pretty big, many dozens of objects, so don’t forget about the ‘Navigator‘ either! In summary, use the ‘Import’ feature to add existing database objects to your model. If you import from multiple schemas, take advantage of the default schema based SubViews to help you manage your models! Sometimes less is more!

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• Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• timetable in a jTable

  - by chandra

  I want to create a timetable in a jTable. For the top row it will display from monday to sunday and the left colume will display the time of the day with 2h interval e.g 1st colume (0000 - 0200), 2nd colume (0200 - 0400) .... And if i click a button the timing will change from 2h interval to 1h interval. I do not want to hardcode it because i need to do for 2h, 1h, 30min , 15min, 1min, 30sec and 1 sec interval and it will take too long for me to hardcode. Can anyone show me an example or help me create an example for the 2h to 1h interval so that i know what to do? The data array is for me to store data and are there any other easier or shortcuts for me to store them because if it is in 1 sec interval i got thousands of array i need to type it out. private void oneHour() //1 interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0100", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0100 - 0200", data[2][0], data[2][1], data[2][2], data[2][3], data[2][4], data[2][5], data[2][6]}, {"0200 - 0300", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0300 - 0400", data[6][0], data[6][1], data[6][2], data[6][3], data[6][4], data[6][5], data[6][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0700", data[10][0], data[4][1], data[10][2], data[10][3], data[10][4], data[10][5], data[10][6]}, {"0700 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 0900", data[14][0], data[14][1], data[14][2], data[14][3], data[14][4], data[14][5], data[14][6]}, {"0900 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1100", data[18][0], data[18][1], data[18][2], data[18][3], data[18][4], data[18][5], data[18][6]}, {"1100 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1300", data[22][0], data[22][1], data[22][2], data[22][3], data[22][4], data[22][5], data[22][6]}, {"1300 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1500", data[26][0], data[26][1], data[26][2], data[26][3], data[26][4], data[26][5], data[26][6]}, {"1500 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1700", data[30][0], data[30][1], data[30][2], data[30][3], data[30][4], data[30][5], data[30][6]}, {"1700 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 1900", data[34][0], data[34][1], data[34][2], data[34][3], data[34][4], data[34][5], data[34][6]}, {"1900 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2100", data[38][0], data[38][1], data[38][2], data[38][3], data[38][4], data[38][5], data[38][6]}, {"2100 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2300", data[42][0], data[42][1], data[42][2], data[42][3], data[42][4], data[42][5], data[42][6]}, {"2300 - 2400", data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]}, {"2400 - 0000", data[46][0], data[46][1], data[46][2], data[46][3], data[46][4], data[46][5], data[46][6]}, }, new String [] { "Time/Day", "(Mon)", "(Tue)", "(Wed)", "(Thurs)", "(Fri)", "(Sat)", "(Sun)" } )); } private void twoHour() //2 hour interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0200", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0200 - 0400", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2400",data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]} },

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• Oracle Data Mining a Star Schema: Telco Churn Case Study

  - by charlie.berger

  There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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• Transfer page from internal to external

  - by Theo Gulland

  Afternoon all! Currently I have a website with a list of audio products (essentially a search engine for audio deals). http://www.soundplaza.co.uk Once you go to the details page, you can then press the 'view deal' button to go to providers site e.g. = http://www.soundplaza.co.uk/all-deals/113/bookshelf-speakers/acoustic-energy-1 This jump between two sites is a bit harsh and I would like to show a transition page, to simply ease them into another site and not scare them off. Within this tradition page I will have a simple loading gif and some graphics showing that your transferring. QUESTION: What is the best way to send the details (link, product name etc) to this transfer page, to then wait 5 seconds, to then move on to the desired link... this can in NO WAY damage my SEO, if anything rel="nofollow" would be great if possible. Currently I have seen that you can submit form to the transition page, then you can use php sleep and then php header to transfer... however I am not sure if php header will transfer SEO value tot he provider? Any opinions would be great! Thanks

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• SQL Rally Pre-Con: Data Warehouse Modeling – Making the Right Choices

  - by Davide Mauri

  As you may have already learned from my old post or Adam’s or Kalen’s posts, there will be two SQL Rally in North Europe. In the Stockholm SQL Rally, with my friend Thomas Kejser, I’ll be delivering a pre-con on Data Warehouse Modeling: Data warehouses play a central role in any BI solution. It's the back end upon which everything in years to come will be created. For this reason, it must be rock solid and yet flexible at the same time. To develop such a data warehouse, you must have a clear idea of its architecture, a thorough understanding of the concepts of Measures and Dimensions, and a proven engineered way to build it so that quality and stability can go hand-in-hand with cost reduction and scalability. In this workshop, Thomas Kejser and Davide Mauri will share all the information they learned since they started working with data warehouses, giving you the guidance and tips you need to start your BI project in the best way possible?avoiding errors, making implementation effective and efficient, paving the way for a winning Agile approach, and helping you define how your team should work so that your BI solution will stand the test of time. You'll learn: Data warehouse architecture and justification Agile methodology Dimensional modeling, including Kimball vs. Inmon, SCD1/SCD2/SCD3, Junk and Degenerate Dimensions, and Huge Dimensions Best practices, naming conventions, and lessons learned Loading the data warehouse, including loading Dimensions, loading Facts (Full Load, Incremental Load, Partitioned Load) Data warehouses and Big Data (Hadoop) Unit testing Tracking historical changes and managing large sizes With all the Self-Service BI hype, Data Warehouse is become more and more central every day, since if everyone will be able to analyze data using self-service tools, it’s better for him/her to rely on correct, uniform and coherent data. Already 50 people registered from the workshop and seats are limited so don’t miss this unique opportunity to attend to this workshop that is really a unique combination of years and years of experience! http://www.sqlpass.org/sqlrally/2013/nordic/Agenda/PreconferenceSeminars.aspx See you there!

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• Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

  - by denis.gray

  Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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• What is the definition of "Big Data"?

  - by Ben

  Is there one? All the definitions I can find describe the size, complexity / variety or velocity of the data. Wikipedia's definition is the only one I've found with an actual number Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. However, this seemingly contradicts the MIKE2.0 definition, referenced in the next paragraph, which indicates that "big" data can be small and that 100,000 sensors on an aircraft creating only 3GB of data could be considered big. IBM despite saying that: Big data is more simply than a matter of size. have emphasised size in their definition. O'Reilly has stressed "volume, velocity and variety" as well. Though explained well, and in more depth, the definition seems to be a re-hash of the others - or vice-versa of course. I think that a Computer Weekly article title sums up a number of articles fairly well "What is big data and how can it be used to gain competitive advantage". But ZDNet wins with the following from 2012: “Big Data” is a catch phrase that has been bubbling up from the high performance computing niche of the IT market... If one sits through the presentations from ten suppliers of technology, fifteen or so different definitions are likely to come forward. Each definition, of course, tends to support the need for that supplier’s products and services. Imagine that. Basically "big data" is "big" in some way shape or form. What is "big"? Is it quantifiable at the current time? If "big" is unquantifiable is there a definition that does not rely solely on generalities?

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• Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Key-Value Pair Databases and Document Databases in the Big Data Story. In this article we will understand the role of Columnar, Graph and Spatial Database supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (The day before yesterday’s post) NoSQL Databases (The day before yesterday’s post) Key-Value Pair Databases (Yesterday’s post) Document Databases (Yesterday’s post) Columnar Databases (Tomorrow’s post) Graph Databases (Today’s post) Spatial Databases (Today’s post) Columnar Databases  Relational Database is a row store database or a row oriented database. Columnar databases are column oriented or column store databases. As we discussed earlier in Big Data we have different kinds of data and we need to store different kinds of data in the database. When we have columnar database it is very easy to do so as we can just add a new column to the columnar database. HBase is one of the most popular columnar databases. It uses Hadoop file system and MapReduce for its core data storage. However, remember this is not a good solution for every application. This is particularly good for the database where there is high volume incremental data is gathered and processed. Graph Databases For a highly interconnected data it is suitable to use Graph Database. This database has node relationship structure. Nodes and relationships contain a Key Value Pair where data is stored. The major advantage of this database is that it supports faster navigation among various relationships. For example, Facebook uses a graph database to list and demonstrate various relationships between users. Neo4J is one of the most popular open source graph database. One of the major dis-advantage of the Graph Database is that it is not possible to self-reference (self joins in the RDBMS terms) and there might be real world scenarios where this might be required and graph database does not support it. Spatial Databases  We all use Foursquare, Google+ as well Facebook Check-ins for location aware check-ins. All the location aware applications figure out the position of the phone with the help of Global Positioning System (GPS). Think about it, so many different users at different location in the world and checking-in all together. Additionally, the applications now feature reach and users are demanding more and more information from them, for example like movies, coffee shop or places see. They are all running with the help of Spatial Databases. Spatial data are standardize by the Open Geospatial Consortium known as OGC. Spatial data helps answering many interesting questions like “Distance between two locations, area of interesting places etc.” When we think of it, it is very clear that handing spatial data and returning meaningful result is one big task when there are millions of users moving dynamically from one place to another place & requesting various spatial information. PostGIS/OpenGIS suite is very popular spatial database. It runs as a layer implementation on the RDBMS PostgreSQL. This makes it totally unique as it offers best from both the worlds. Courtesy: mushroom network Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Hive. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Transfer files using java

  - by markovuksanovic

  I need to transfer lots of small files to a remote computer within my java program. I was wondering if somebody could suggest the best way to do so... I need to transfer lots of small files and it has to be really fast. Should I use some existing protocol implementation? maybe ftp? One important thing is that most files would be the same all the time, or the difference would be minor so I was thinking of using git for that purpose. Does anyone have experience with sth like this?

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• Purchase existing domain and transfer to new registrar

  - by Kiefer

  I am purchasing an existing domain from the owner who has it registered with GoDaddy. I want to transfer the domain to another registrar and of course have it under my name. If they update the registrant info to my name then it will lock down for 60 days. That's no good. If they simply transfer it to my registrar, how will they update the registrant info? I know about escrow services, but I don't feel I need one because I trust the seller and the amount is (relatively) small. Advice? Thanks!

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