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  • PHP - post data ends when '&' is in data.

    - by Phil Jackson
    Hi all, im posting data using jquery/ajax and PHP at the backend. Problem being, when I input something like 'Jack & Jill went up the hill' im only recieving 'Jack' when it gets to the backend. I have thrown an error at the frontend before that data is sent which alerts 'Jack & Jill went up the hill'. When I put die(print_r($_POST)); at the very top of my index page im only getting [key] => Jack how can I be loosing the data? I thought It may have been my filter; <?php function filter( $data ) { $data = trim( htmlentities( strip_tags( mb_convert_encoding( $data, 'HTML-ENTITIES', "UTF-8") ) ) ); if ( get_magic_quotes_gpc() ) { $data = stripslashes( $data ); } //$data = mysql_real_escape_string( $data ); return $data; } echo "<xmp>" . filter("you & me") . "</xmp>"; ?> but that returns fine in the test above you &amp; me which is in place after I added die(print_r($_POST));. Can anyone think of how and why this is happening? Any help much appreciated. Regards, Phil.

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  • Configuration data: single-row table vs. name-value-pair table

    - by Heinzi
    Let's say you write an application that can be configured by the user. For storing this "configuration data" into a database, two patterns are commonly used. The single-row table CompanyName | StartFullScreen | RefreshSeconds | ... ---------------+-------------------+------------------+-------- ACME Inc. | true | 20 | ... The name-value-pair table ConfigOption | Value -----------------+------------- CompanyName | ACME Inc. StartFullScreen | true (or 1, or Y, ...) RefreshSeconds | 20 ... | ... I've seen both options in the wild, and both have obvious advantages and disadvantages, for example: The single-row tables limits the number of configuration options you can have (since the number of columns in a row is usually limited). Every additional configuration option requires a DB schema change. In a name-value-pair table everything is "stringly typed" (you have to encode/decode your Boolean/Date/etc. parameters). (many more) Is there some consensus within the development community about which option is preferable?

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  • Can a table be both Fact and Dimension

    - by PatFromCanada
    Ok, I am a newbie and don't really think "dimensionally" yet, I have most of my initial schema roughed out but I keep flipping back and forth on one table. I have a Contract table and it has a quantity column (tonnes), and a net price column, which need to be summed up a bunch of different ways, and the contract has lots of foreign keys (producer, commodity, futures month etc.) and dates so it appears to be a fact table. Also the contract is never updated, if that makes a difference. However, we create cash tickets which we use to pay out part or all of the contract and they have a contract ID on them so then the contract looks like a dimension in the cash ticket's star schema. Is this a problem? Any ideas on the process to resolve this, because people don't seem to like the idea of joining two fact tables. Should I put producerId and commodityId on the cash ticket? It would seem really weird not to have a contractID on it.

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  • SQL Table stored as a Heap - the dangers within

    - by MikeD
    Nearly all of the time I create a table, I include a primary key, and often that PK is implemented as a clustered index. Those two don't always have to go together, but in my world they almost always do. On a recent project, I was working on a data warehouse and a set of SSIS packages to import data from an OLTP database into my data warehouse. The data I was importing from the business database into the warehouse was mostly new rows, sometimes updates to existing rows, and sometimes deletes. I decided to use the MERGE statement to implement the insert, update or delete in the data warehouse, I found it quite performant to have a stored procedure that extracted all the new, updated, and deleted rows from the source database and dump it into a working table in my data warehouse, then run a stored proc in the warehouse that was the MERGE statement that took the rows from the working table and updated the real fact table. Use Warehouse CREATE TABLE Integration.MergePolicy (PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date, Operation varchar(5)) CREATE TABLE fact.Policy (PolicyKey int identity primary key, PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date) CREATE PROC Integration.MergePolicy as begin begin tran Merge fact.Policy as tgtUsing Integration.MergePolicy as SrcOn (tgt.PolicyId = Src.PolicyId) When not matched by Target then Insert (PolicyId, PolicyTypeKey, Premium, Deductible, EffectiveDate)values (src.PolicyId, src.PolicyTypeKey, src.Premium, src.Deductible, src.EffectiveDate) When matched and src.Operation = 'U' then Update set PolicyTypeKey = src.PolicyTypeKey,Premium = src.Premium,Deductible = src.Deductible,EffectiveDate = src.EffectiveDate When matched and src.Operation = 'D' then Delete ;delete from Integration.WorkPolicy commit end Notice that my worktable (Integration.MergePolicy) doesn't have any primary key or clustered index. I didn't think this would be a problem, since it was relatively small table and was empty after each time I ran the stored proc. For one of the work tables, during the initial loads of the warehouse, it was getting about 1.5 million rows inserted, processed, then deleted. Also, because of a bug in the extraction process, the same 1.5 million rows (plus a few hundred more each time) was getting inserted, processed, and deleted. This was being sone on a fairly hefty server that was otherwise unused, and no one was paying any attention to the time it was taking. This week I received a backup of this database and loaded it on my laptop to troubleshoot the problem, and of course it took a good ten minutes or more to run the process. However, what seemed strange to me was that after I fixed the problem and happened to run the merge sproc when the work table was completely empty, it still took almost ten minutes to complete. I immediately looked back at the MERGE statement to see if I had some sort of outer join that meant it would be scanning the target table (which had about 2 million rows in it), then turned on the execution plan output to see what was happening under the hood. Running the stored procedure again took a long time, and the plan output didn't show me much - 55% on the MERGE statement, and 45% on the DELETE statement, and table scans on the work table in both places. I was surprised at the relative cost of the DELETE statement, because there were really 0 rows to delete, but I was expecting to see the table scans. (I was beginning now to suspect that my problem was because the work table was being stored as a heap.) Then I turned on STATS_IO and ran the sproc again. The output was quite interesting.Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'Policy'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'MergePolicy'. Scan count 1, logical reads 433276, physical reads 60, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. I've reproduced the above from memory, the details aren't exact, but the essential bit was the very high number of logical reads on the table stored as a heap. Even just doing a SELECT Count(*) from Integration.MergePolicy incurred that sort of output, even though the result was always 0. I suppose I should research more on the allocation and deallocation of pages to tables stored as a heap, but I haven't, and my original assumption that a table stored as a heap with no rows would only need to read one page to answer any query was definitely proven wrong. It's likely that some sort of physical defragmentation of the table may have cleaned that up, but it seemed that the easiest answer was to put a clustered index on the table. After doing so, the execution plan showed a cluster index scan, and the IO stats showed only a single page read. (I aborted my first attempt at adding a clustered index on the table because it was taking too long - instead I ran TRUNCATE TABLE Integration.MergePolicy first and added the clustered index, both of which took very little time). I suspect I may not have noticed this if I had used TRUNCATE TABLE Integration.MergePolicy instead of DELETE FROM Integration.MergePolicy, since I'm guessing that the truncate operation does some rather quick releasing of pages allocated to the heap table. In the future, I will likely be much more careful to have a clustered index on every table I use, even the working tables. Mike  

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  • Fact table with multiple facts

    - by Jeff Meatball Yang
    I have a dimension (SiteItem) has two important facts: perUserClicks perBrowserClicks however, within this dimension, I have groups of dimensions based on an attribute column (let's call the groups AboveFoldItems, LeftNavItems, OnTheFlyItems, etc.) each have more facts that are specific to that group: AboveFoldItems: eyeTime, loadTime LeftNavItems: mouseOverTime OnTheFlyItems: doesn't have any extra, but may in the future Is the following fact table schema ok? DateKey SessionKey SiteItemKey perUserClicks perBrowserClicks eyeTime loadTime mouseOverTime It seems a little wasteful since only some columns pertain to some dimension keys (the irrelevant facts are left NULL). But... this seems like it would be a common problem, so there should be a common solution for this, right?

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  • What is the best way to store a table in C++

    - by Topo
    I'm programming a decision tree in C++ using a slightly modified version of the C4.5 algorithm. Each node represents an attribute or a column of your data set and it has a children per possible value of the attribute. My problem is how to store the training data set having in mind that I have to use a subset for each node so I need a quick way to only select a subset of rows and columns. The main goal is to do it in the most memory and time efficient possible (in that order of priority). The best way I have thought of is to have an array of arrays (or std::vector), or something like that, and for each node have a list (array, vector, etc) or something with the column,line(probably a tuple) pairs that are valid for that node. I now there should be a better way to do this, any suggestions? UPDATE: What I need is something like this: In the beginning I have this data: Paris 4 5.0 True New York 7 1.3 True Tokio 2 9.1 False Paris 9 6.8 True Tokio 0 8.4 False But for the second node I just need this data: Paris 4 5.0 New York 7 1.3 Paris 9 6.8 And for the third node: Tokio 2 9.1 Tokio 0 8.4 But with a table of millions of records with up to hundreds of columns. What I have in mind is keep all the data in a matrix, and then for each node keep the info of the current columns and rows. Something like this: Paris 4 5.0 True New York 7 1.3 True Tokio 2 9.1 False Paris 9 6.8 True Tokio 0 8.4 False Node 2: columns = [0,1,2] rows = [0,1,3] Node 3: columns = [0,1,2] rows = [2,4] This way on the worst case scenario I just have to waste size_of(int) * (number_of_columns + number_of_rows) * node That is a lot less than having an independent data matrix for each node.

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  • SQL Developer Debugging, Watches, Smart Data, & Data

    - by thatjeffsmith
    After presenting the SQL Developer PL/SQL debugger for about an hour yesterday at KScope12 in San Antonio, my boss came up and asked, “Now, would you really want to know what the Smart Data panel does?” Apparently I had ‘made up’ my own story about what that panel’s intent is based on my experience with it. Not good Jeff, not good. It was a very small point of my presentation, but I probably should have read the docs. The Smart Data tab displays information about variables, using your Debugger: Smart Data preferences. You can also specify these preferences by right-clicking in the Smart Data window and selecting Preferences. Debugger Smart Data Preferences, control number of variables to display The Smart Data panel auto-inspects the last X accessed variables. So if you have a program with 26 variables, instead of showing you all 26, it will just show you the last two variables that were referenced in your program. If you were to click on the ‘Data’ debug panel, you’ll see EVERYTHING. And if you only want to see a very specific set of values, then you should use Watches. The Smart Data Panel As I step through the code, the variables being tracked change as they are referenced. Only the most recent ones display. This is controlled by the ‘Maximum Locations to Remember’ preference. Step through the code, see the latest variables accessed The Data Panel All variables are displayed. Might be information overload on large PL/SQL programs where you have many dozens or even hundreds of variables to track. Shows everything all the time Watches Watches are added manually and only show what you ask for. Data on Demand – add a watch to track a specific variable Remember, you can interact with your data If you want to do more than just watch, you can mouse-right on a data element, and change the value of the variable as the program is running. This is one of the primary benefits to debugging over using DBMS_OUTPUT to track what’s happening in your program. Change the values while the program is running to test your ‘What if?’ scenarios

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  • SQL – Biggest Concerns in a Data-Driven World

    - by Pinal Dave
    The ongoing chaos over Government Agency’s snooping has ignited a heated debate on privacy of personal data and its use by government and/or other institutions. It has created a feeling of disapproval and distrust among users. This incident proves to be a lesson for companies that are looking to leverage their business using a data driven approach. According to analysts, the goal of gathering personal information should be to deliver benefits to both the parties – the user as well as the data collector(government or business). Using data the right way is crucial, and companies need to deploy the right software applications and systems to ensure that their efforts are well-directed. However, there are various issues plaguing analysts regarding available software, which are highlighted below. According to a InformationWeek 2013 Survey of Analytics, Business Intelligence and Information Management where 541 business technology professionals contributed as respondents, it was discovered that the biggest concern was deemed to be the scarcity of expertise and high costs associated with the same. This concern was voiced by as many as 38% of the participants. A close second came out to be the issue of data warehouse appliance platforms being expensive, with 33% of those present believing it to be a huge roadblock. Another revelation made in this respect was that 31% professionals weren’t even sure how Data Analytics can create business opportunities for them. Another 17% shared that they found data platform technologies such as Hadoop and NoSQL technologies hard to learn. These results clearly pointed out that there are awareness and expertise issues that also need much attention. Unless the demand-supply gap of Business Intelligence professionals well versed in data analysis technologies is met, this divide is going to affect how companies make the most of their BI campaigns. One of the key action points that can be taken to salvage the situation, is to provide training on Data Analytics concepts. Koenig Solutions offer courses on many such technologies including a course on MCSE SQL Server 2012: BI Platform. So it’s time to brush up your skills and get down to work in a data driven world that awaits you ahead. 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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  • Store XML data in Core Data

    - by ct2k7
    Hi, is there any easy way of store XML data into core data? Currently, my app just pulls the values from the XML file directly, however, this isn't efficient for XML files which holds over 100 entries, thus storing the data in Core Data would be the best option. XML file is called/downloaded/parsed ever time the app opens. With the Core Data, the XML data would be downloaded ever 3600 seconds or so, and refresh the current data in the core data, to reduce the loading time when opening the app. Any ideas on how I can do this? Having reviewed the developer documentation, it doesn't look very tasty.

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  • Where can I find free and open data?

    - by kitsune
    Sooner or later, coders will feel the need to have access to "open data" in one of their projects, from knowing a city's zip to a more obscure information such as the axial tilt of Pluto. I know data.un.org which offers access to the UN's extensive array of databases that deal with human development and other socio-economic issues. The other usual suspects are NASA and the USGS for planetary data. There's an article at readwriteweb with more links. infochimps.org seems to stand out. Personally, I need to find historic commodity prices, stock values and other financial data. All these data sets seem to cost money however. Clarification To clarify, I'm interested in all kinds of open data, because sooner or later, I know I will be in a situation where I could need it. I will try to edit this answer and include the suggestions in a structured manners. A link for financial data was hidden in that readwriteweb article, doh! It's called opentick.com. Looks good so far! Update I stumbled over semantic data in another question of mine on here. There is opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). A project called UMBEL provides a light-weight, distilled version of opencyc. Umbel has semantic data in rdf/owl/skos n3 syntax. The Worldbank also released a very nice API. It offers data from the last 50 years for about 200 countries

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  • Temporary storage for keeping data between program iterations?

    - by mr.b
    I am working on an application that works like this: It fetches data from many sources, resulting in pool of about 500,000-1,500,000 records (depends on time/day) Data is parsed Part of data is processed in a way to compare it to pre-existing data (read from database), calculations are made, and stored in database. Resulting dataset that has to be stored in database is, however, much smaller in size (compared to original data set), and ranges from 5,000-50,000 records. This process almost always updates existing data, perhaps adds few more records. Then, data from step 2 should be kept somehow, somewhere, so that next time data is fetched, there is a data set which can be used to perform calculations, without touching pre-existing data in database. I should point out that this data can be lost, it's not irreplaceable (key information can be read from database if needed), but it would speed up the process next time. Application components can (and will be) run off different computers (in the same network), so storage has to be reachable from multiple hosts. I have considered using memcached, but I'm not quite sure should I do so, because one record is usually no smaller than 200 bytes, and if I have 1,500,000 records, I guess that it would amount to over 300 MB of memcached cache... But that doesn't seem scalable to me - what if data was 5x that amount? If it were to consume 1-2 GB of cache only to keep data in between iterations (which could easily happen)? So, the question is: which temporary storage mechanism would be most suitable for this kind of processing? I haven't considered using mysql temporary tables, as I'm not sure if they can persist between sessions, and be used by other hosts in network... Any other suggestion? Something I should consider?

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  • Efficient alternatives to merge for larger data.frames R

    - by Etienne Low-Décarie
    I am looking for an efficient (both computer resource wise and learning/implementation wise) method to merge two larger (size1 million / 300 KB RData file) data frames. "merge" in base R and "join" in plyr appear to use up all my memory effectively crashing my system. Example load test data frame and try test.merged<-merge(test, test) or test.merged<-join(test, test, type="all") - The following post provides a list of merge and alternatives: How to join data frames in R (inner, outer, left, right)? The following allows object size inspection: https://heuristically.wordpress.com/2010/01/04/r-memory-usage-statistics-variable/ Data produced by anonym

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  • Ignore a css class applied to a parent table

    - by user2585299
    <table class="table table-bordered table-hover table-condensed data_table"> <tbody data-bind="foreach: outboundFaxLogs"> <tr> </tr> <tr> <td></td> <td colspan="8"> <table> <tr style="border:none"> <td>ReFax Status</td> <td>FaxTo</td> <td>Completion</td> <td>FaxID</td> </tr> <tbody data-bind="foreach: ResubmissionHistory""> <tr style="border:none"> <td data-bind="text: Status" ></td> <td data-bind="text: FaxToNbr"></td> <td data-bind="text: $root.formatDateTime(CompletionTime)"></td> <td data-bind="text: OutboundFaxLogId"></td> </tr> </tbody> </table> </td> </tr> </tbody> </table> The parent table has a css class applied to it which is table-bordered. Its a twitter bootstrap style element. I don't want that style to be applied to the child table. How can I do this ? I do not want the lines that appear in between the table cells for the child table.

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  • Why are data structures so important in interviews?

    - by Vamsi Emani
    I am a newbie into the corporate world recently graduated in computers. I am a java/groovy developer. I am a quick learner and I can learn new frameworks, APIs or even programming languages within considerably short amount of time. Albeit that, I must confess that I was not so strong in data structures when I graduated out of college. Through out the campus placements during my graduation, I've witnessed that most of the biggie tech companies like Amazon, Microsoft etc focused mainly on data structures. It appears as if data structures is the only thing that they expect from a graduate. Adding to this, I see that there is this general perspective that a good programmer is necessarily a one with good knowledge about data structures. To be honest, I felt bad about that. I write good code. I follow standard design patterns of coding, I do use data structures but at the superficial level as in java exposed APIs like ArrayLists, LinkedLists etc. But the companies usually focused on the intricate aspects of Data Structures like pointer based memory manipulation and time complexities. Probably because of my java-ish background, Back then, I understood code efficiency and logic only when talked in terms of Object Oriented Programming like Objects, instances, etc but I never drilled down into the level of bits and bytes. I did not want people to look down upon me for this knowledge deficit of mine in Data Structures. So really why all this emphasis on Data Structures? Does, Not having knowledge in Data Structures really effect one's career in programming? Or is the knowledge in this subject really a sufficient basis to differentiate a good and a bad programmer?

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  • Data structure for pattern matching.

    - by alvonellos
    Let's say you have an input file with many entries like these: date, ticker, open, high, low, close, <and some other values> And you want to execute a pattern matching routine on the entries(rows) in that file, using a candlestick pattern, for example. (See, Doji) And that pattern can appear on any uniform time interval (let t = 1s, 5s, 10s, 1d, 7d, 2w, 2y, and so on...). Say a pattern matching routine can take an arbitrary number of rows to perform an analysis and contain an arbitrary number of subpatterns. In other words, some patterns may require 4 entries to operate on. Say also that the routine (may) later have to find and classify extrema (local and global maxima and minima as well as inflection points) for the ticker over a closed interval, for example, you could say that a cubic function (x^3) has the extrema on the interval [-1, 1]. (See link) What would be the most natural choice in terms of a data structure? What about an interface that conforms a Ticker object containing one row of data to a collection of Ticker so that an arbitrary pattern can be applied to the data. What's the first thing that comes to mind? I chose a doubly-linked circular linked list that has the following methods: push_front() push_back() pop_front() pop_back() [] //overloaded, can be used with negative parameters But that data structure seems very clumsy, since so much pushing and popping is going on, I have to make a deep copy of the data structure before running an analysis on it. So, I don't know if I made my question very clear -- but the main points are: What kind of data structures should be considered when analyzing sequential data points to conform to a pattern that does NOT require random access? What kind of data structures should be considered when classifying extrema of a set of data points?

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  • Five Key Strategies in Master Data Management

    - by david.butler(at)oracle.com
    Here is a very interesting Profit Magazine article on MDM: A recent customer survey reveals the deleterious effects of data fragmentation. by Trevor Naidoo, December 2010   Across industries and geographies, IT organizations have grown in complexity, whether due to mergers and acquisitions, or decentralized systems supporting functional or departmental requirements. With systems architected over time to support unique, one-off process needs, they are becoming costly to maintain, and the Internet has only further added to the complexity. Data fragmentation has become a key inhibitor in delivering flexible, user-friendly systems. The Oracle Insight team conducted a survey assessing customers' master data management (MDM) capabilities over the past two years to get a sense of where they are in terms of their capabilities. The responses, by 27 respondents from six different industries, reveal five key areas in which customers need to improve their data management in order to get better financial results. 1. Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains. Examples of the types of master data domains referred to are customer, supplier, product, financial and site. Many organizations have multiple sources of master data with varying degrees of data quality in each source -- customer data stored in the customer relationship management system is inconsistent with customer data stored in the order management system. Imagine not knowing how many places you stored your customer information, and whether a customer's address was the most up to date in each source. In fact, more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis. It is important for organizations to document their inventory of data sources and then profile these data sources to ensure that there is a consistent definition of key data entities throughout the organization. Some questions to ask are: How do we define a customer? What is a product? How do we define a site? The goal is to strive for one common repository for master data that acts as a cross reference for all other sources and ensures consistent, high-quality master data throughout the organization. 2. Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. The sales department may track all their interactions with customers as they move through the sales cycle, the service department is tracking their interactions with the same customers independently, and the finance department also has a different perspective on the same customer. The salesperson may not be aware that the customer she is trying to sell to is experiencing issues with existing products purchased, or that the customer is behind on previous invoices. The lack of a data strategy makes it difficult for business users to turn data into information via reports. Without the key building blocks in place, it is difficult to create key linkages between customer, product, site, supplier and financial data. These linkages make it possible to understand patterns. A well-defined data management strategy is aligned to the business strategy and helps create the governance needed to ensure that data stewardship is in place and data integrity is intact. 3. Almost 60 percent of respondents have no strategy to integrate data across operational applications. Many respondents have several disparate sources of data with no strategy to keep them in sync with each other. Even though there is no clear strategy to integrate the data (see #2 above), the data needs to be synced and cross-referenced to keep the business processes running. About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually with the help of Microsoft Excel spreadsheets. For example, a salesperson needs a report on global sales for a specific product, but the product has different product numbers in different countries. Typically, an analyst will pull all the data into Excel, manually create a cross reference for that product, and then aggregate the sales. The exact same procedure has to be followed if the same report is needed the following month. A well-defined consolidation strategy will ensure that a central cross-reference is maintained with updates in any one application being propagated to all the other systems, so that data is synchronized and up to date. This can be done in real time or in batch mode using integration technology. 4. Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data. Information stored in various systems usually follows different standards and formats, making it difficult to match the data. A customer's address can be stored in different ways using a variety of abbreviations -- for example, "av" or "ave" for avenue. Similarly, a product's attributes can be stored in a number of different ways; for example, a size attribute can be stored in inches and can also be entered as "'' ". These types of variations make it difficult to match up data from different sources. Today, most customers rely on manual, heroic efforts to match, cleanse, and de-duplicate data -- clearly not a scalable, sustainable model. To solve this challenge, organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates. It is no wonder, therefore, that we get communications about products we don't own, at addresses we don't reside, and using channels (like direct mail) we don't like. An all-too-common example of a potential challenge follows: Customers end up receiving duplicate communications, which not only impacts customer satisfaction, but also incurs additional mailing costs. Cleansing, normalizing, and standardizing data will help address most of these issues. 5. Only 10 percent of respondents have the ability to share data that was mastered in a master data hub. Close to 60 percent of respondents have efforts in place that profile, standardize and cleanse data manually, and the output of these efforts are stored in spreadsheets in various parts of the organization. This valuable information is not easily shared with the rest of the organization and, more importantly, this enriched information cannot be sent back to the source systems so that the data is fixed at the source. A key benefit of a master data management strategy is not only to clean the data, but to also share the data back to the source systems as well as other systems that need the information. Aside from the source systems, another key beneficiary of this data is the business intelligence system. Having clean master data as input to business intelligence systems provides more accurate and enhanced reporting.  Characteristics of Stellar MDM When deciding on the right master data management technology, organizations should look for solutions that have four main characteristics: enterprise-grade MDM performance complete technology that can be rapidly deployed and addresses multiple business issues end-to-end MDM process management with data quality monitoring and assurance pre-built MDM business relevant applications with data stores and workflows These master data management capabilities will aid in moving closer to a best-practice maturity level, delivering tremendous efficiencies and savings as well as revenue growth opportunities as a result of better understanding your customers.  Trevor Naidoo is a senior director in Industry Strategy and Insight at Oracle. 

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  • Address Regulatory Mandates for Data Encryption Without Changing Your Applications

    - by Troy Kitch
    The Payment Card Industry Data Security Standard, US state-level data breach laws, and numerous data privacy regulations worldwide all call for data encryption to protect personally identifiable information (PII). However encrypting PII data in applications requires costly and complex application changes. Fortunately, since this data typically resides in the application database, using Oracle Advanced Security, PII can be encrypted transparently by the Oracle database without any application changes. In this ISACA webinar, learn how Oracle Advanced Security offers complete encryption for data at rest, in transit, and on backups, along with built-in key management to help organizations meet regulatory requirements and save money. You will also hear from TransUnion Interactive, the consumer subsidiary of TransUnion, a global leader in credit and information management, which maintains credit histories on an estimated 500 million consumers across the globe, about how they addressed PCI DSS encryption requirements using Oracle Database 11g with Oracle Advanced Security. Register to watch the webinar now.

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  • Using Hadooop (HDInsight) with Microsoft - Two (OK, Three) Options

    - by BuckWoody
    Microsoft has many tools for “Big Data”. In fact, you need many tools – there’s no product called “Big Data Solution” in a shrink-wrapped box – if you find one, you probably shouldn’t buy it. It’s tempting to want a single tool that handles everything in a problem domain, but with large, complex data, that isn’t a reality. You’ll mix and match several systems, open and closed source, to solve a given problem. But there are tools that help with handling data at large, complex scales. Normally the best way to do this is to break up the data into parts, and then put the calculation engines for that chunk of data right on the node where the data is stored. These systems are in a family called “Distributed File and Compute”. Microsoft has a couple of these, including the High Performance Computing edition of Windows Server. Recently we partnered with Hortonworks to bring the Apache Foundation’s release of Hadoop to Windows. And as it turns out, there are actually two (technically three) ways you can use it. (There’s a more detailed set of information here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx, I’ll cover the options at a general level below)  First Option: Windows Azure HDInsight Service  Your first option is that you can simply log on to a Hadoop control node and begin to run Pig or Hive statements against data that you have stored in Windows Azure. There’s nothing to set up (although you can configure things where needed), and you can send the commands, get the output of the job(s), and stop using the service when you are done – and repeat the process later if you wish. (There are also connectors to run jobs from Microsoft Excel, but that’s another post)   This option is useful when you have a periodic burst of work for a Hadoop workload, or the data collection has been happening into Windows Azure storage anyway. That might be from a web application, the logs from a web application, telemetrics (remote sensor input), and other modes of constant collection.   You can read more about this option here:  http://blogs.msdn.com/b/windowsazure/archive/2012/10/24/getting-started-with-windows-azure-hdinsight-service.aspx Second Option: Microsoft HDInsight Server Your second option is to use the Hadoop Distribution for on-premises Windows called Microsoft HDInsight Server. You set up the Name Node(s), Job Tracker(s), and Data Node(s), among other components, and you have control over the entire ecostructure.   This option is useful if you want to  have complete control over the system, leave it running all the time, or you have a huge quantity of data that you have to bulk-load constantly – something that isn’t going to be practical with a network transfer or disk-mailing scheme. You can read more about this option here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx Third Option (unsupported): Installation on Windows Azure Virtual Machines  Although unsupported, you could simply use a Windows Azure Virtual Machine (we support both Windows and Linux servers) and install Hadoop yourself – it’s open-source, so there’s nothing preventing you from doing that.   Aside from being unsupported, there are other issues you’ll run into with this approach – primarily involving performance and the amount of configuration you’ll need to do to access the data nodes properly. But for a single-node installation (where all components run on one system) such as learning, demos, training and the like, this isn’t a bad option. Did I mention that’s unsupported? :) You can learn more about Windows Azure Virtual Machines here: http://www.windowsazure.com/en-us/home/scenarios/virtual-machines/ And more about Hadoop and the installation/configuration (on Linux) here: http://en.wikipedia.org/wiki/Apache_Hadoop And more about the HDInsight installation here: http://www.microsoft.com/web/gallery/install.aspx?appid=HDINSIGHT-PREVIEW Choosing the right option Since you have two or three routes you can go, the best thing to do is evaluate the need you have, and place the workload where it makes the most sense.  My suggestion is to install the HDInsight Server locally on a test system, and play around with it. Read up on the best ways to use Hadoop for a given workload, understand the parts, write a little Pig and Hive, and get your feet wet. Then sign up for a test account on HDInsight Service, and see how that leverages what you know. If you're a true tinkerer, go ahead and try the VM route as well. Oh - there’s another great reference on the Windows Azure HDInsight that just came out, here: http://blogs.msdn.com/b/brunoterkaly/archive/2012/11/16/hadoop-on-azure-introduction.aspx  

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  • Oracle - A Leader in Gartner's MQ for Master Data Management for Customer Data

    - by Mala Narasimharajan
      The Gartner MQ report for Master Data Management of Customer Data Solutions is released and we're proud to say that Oracle is in the leaders' quadrant.  Here's a snippet from the report itself:  " “Oracle has a strong, though complex, portfolio of domain-specific MDM products that include prepackaged data models. Gartner estimates that Oracle now has over 1,500 licensed MDM customers, including 650 customers managing customer data. The MDM portfolio includes three products that address MDM of customer data solution needs: Oracle Fusion Customer Hub (FCH), Oracle CDH and Oracle Siebel UCM. These three MDM products are positioned for different segments of the market and Oracle is progressively moving all three products onto a common MDM technology platform..." (Gartner, Oct 18, 2012)  For more information on Oracle's solutions for customer data in Master Data Management, click here.  

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  • Almost Realtime Data and Web application

    - by Chris G.
    I have a computer that is recording 100 different data points into an OPC server. I've written a simple OPC client that can read all of this data. I have a front-end website on a different network that I would like to consume this data. I could easily set the OPC client to send the data to a SQL server and the website could read from it, but that would be a lot of writes. If I wanted the data to be updated every 10 seconds I'd be writing to the database every 10 seconds. (I could probably just serialize the 100 points to get 1 write / 10 seconds but that would also limit my ability to search the data later). This solution wouldn't scale very well. If I had 100 of these computers the situation would quickly grow out of hand. Obviously I am well out of my league here and I have no experience with working with a large amount of data like this. What are my options and what should I research?

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  • Core Data table record count

    - by user339633
    I have an entity called Person and it has a relationship called participatingGames, to another entity called GameParticipant. I (apparently) can retrieve the number of matches in the GameParticipant entity using this simple code in the Person object I created from the entity in the model: [self.participatingGames count]; However, I'd just like to retrieve the number of Person records and one might guess the syntax for this is just as simple. I have lots of books including those by Jeff LaMarche, but those sources and what I find around here make me wonder if I need to set up a fetchedResultsController just to know the count of some entity. My background is in SQL, so of course it seems odd that what would take 15 seconds to code in any other environment seems like such a well-guarded secret in Core Data. I'm using iPhone SDK 3.1.4 under OSX 10.5.8 Suggestions?

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  • Cell Dequeue problem when table inside another table's cell content view

    - by Shailesh Kanzariya
    I am using two table views (Main Table and Sub Table), one table inside other's cell. I am adding Sub Table in Main Table's cell content view. I am also using different Cell Identifier for both table cells. Now, issue is : When - (UITableViewCell *)tableView:(UITableView *)tableView cellForRowAtIndexPath:(NSIndexPath *)indexPath is called, very first time cell of Main Table is generated and when I scroll up/down they all are just dequeued, so it is expected and standard behavior and working fine. But, cell of Sub Table is getting created/allocated every time. It is not dequeued as it should be. I guess, its happening because Sub Table is part of Main Table's Cell Content view. But not sure and don't know how to resolve it. Can somebody help me to find the solution?

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  • data structure for counting frequencies in a database table-like format

    - by user373312
    i was wondering if there is a data structure optimized to count frequencies against data that is stored in a database table-like format. for example, the data comes in a (comma) delimited format below. col1, col2, col3 x, a, green x, b, blue ... y, c, green now i simply want to count the frequency of col1=x or col1=x and col2=green. i have been storing the data in a database table, but in my profiling and from empirical observation, database connection is the bottle-neck. i have tried using in-memory database solutions too, and that works quite well; the only problem is memory requirements and quirky init/destroy calls. also, i work mainly with java, but have experience with .net, and was wondering if there was any api to work with "tabular" data in a linq way using java. any help is appreciated.

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  • SQL SERVER – Integrate Your Data with Skyvia – Cloud ETL Solution

    - by Pinal Dave
    In our days data integration often becomes a key aspect of business success. For business analysts it’s very important to get integrated data from various sources, such as relational databases, cloud CRMs, etc. to make correct and successful decisions. There are various data integration solutions on market, and today I will tell about one of them – Skyvia. Skyvia is a cloud data integration service, which allows integrating data in cloud CRMs and different relational databases. It is a completely online solution and does not require anything except for a browser. Skyvia provides powerful etl tools for data import, export, replication, and synchronization for SQL Server and other databases and cloud CRMs. You can use Skyvia data import tools to load data from various sources to SQL Server (and SQL Azure). Skyvia supports such cloud CRMs as Salesforce and Microsoft Dynamics CRM and such databases as MySQL and PostgreSQL. You even can migrate data from SQL Server to SQL Server, or from SQL Server to other databases and cloud CRMs. Additionally Skyvia supports import of CSV files, either uploaded manually or stored on cloud file storage services, such as Dropbox, Box, Google Drive, or FTP servers. When data import is not enough, Skyvia offers bidirectional data synchronization. With this tool, you can synchronize SQL Server data with other databases and cloud CRMs. After performing the first synchronization, Skyvia tracks data changes in the synchronized data storages. In SQL Server databases (and other relational databases) it creates additional tracking tables and triggers. This allows synchronizing only the changed data. Skyvia also maps records by their primary key values to each other, so it does not require different sources to have the same primary key structure. It still can match the corresponding records without having to add any additional columns or changing data structure. The only requirement for synchronization is that primary keys must be autogenerated. With Skyvia it’s not necessary for data to have the same structure in integrated data storages. Skyvia supports powerful mapping mechanisms that allow synchronizing data with completely different structure. It provides support for complex mathematical and string expressions when mapping data, using lookups, etc. You may use data splitting – loading data from a single CSV file or source table to multiple related target tables. Or you may load data from several source CSV files or tables to several related target tables. In each case Skyvia preserves data relations. It builds corresponding relations between the target data automatically. When you often work with cloud CRM data, native CRM data reporting and analysis tools may be not enough for you. And there is a vast set of professional data analysis and reporting tools available for SQL Server. With Skyvia you can quickly copy your cloud CRM data to an SQL Server database and apply corresponding SQL Server tools to the data. In such case you can use Skyvia data replication tools. It allows you to quickly copy cloud CRM data to SQL Server or other databases without customizing any mapping. You need just to specify columns to copy data from. Target database tables will be created automatically. Skyvia offers powerful filtering settings to replicate only the records you need. Skyvia also provides capability to export data from SQL Server (including SQL Azure) and other databases and cloud CRMs to CSV files. These files can be either downloadable manually or loaded to cloud file storages or FTP server. You can use export, for example, to backup SQL Azure data to Dropbox. Any data integration operation can be scheduled for automatic execution. Thus, you can automate your SQL Azure data backup or data synchronization – just configure it once, then schedule it, and benefit from automatic data integration with Skyvia. Currently registration and using Skyvia is completely free, so you can try it yourself and find out whether its data migration and integration tools suits for you. Visit this link to register on Skyvia: https://app.skyvia.com/register Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Cloud Computing

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  • SQL SERVER – Why Do We Need Master Data Management – Importance and Significance of Master Data Management (MDM)

    - by pinaldave
    Let me paint a picture of everyday life for you.  Let’s say you and your wife both have address books for your groups of friends.  There is definitely overlap between them, so that you both have the addresses for your mutual friends, and there are addresses that only you know, and some only she knows.  They also might be organized differently.  You might list your friend under “J” for “Joe” or even under “W” for “Work,” while she might list him under “S” for “Joe Smith” or under your name because he is your friend.  If you happened to trade, neither of you would be able to find anything! This is where data management would be very important.  If you were to consolidate into one address book, you would have to set rules about how to organize the book, and both of you would have to follow them.  You would also make sure that poor Joe doesn’t get entered twice under “J” and under “S.” This might be a familiar situation to you, whether you are thinking about address books, record collections, books, or even shopping lists.  Wherever there is a lot of data to consolidate, you are going to run into problems unless everyone is following the same rules. I’m sure that my readers can figure out where I am going with this.  What is SQL Server but a computerized way to organize data?  And Microsoft is making it easier and easier to get all your “addresses” into one place.  In the  2008 version of SQL they introduced a new tool called Master Data Services (MDS) for Master Data Management, and they have improved it for the new 2012 version. MDM was hailed as a major improvement for business intelligence.  You might not think that an organizational system is terribly exciting, but think about the kind of “address books” a company might have.  Many companies have lots of important information, like addresses, credit card numbers, purchase history, and so much more.  To organize all this efficiently so that customers are well cared for and properly billed (only once, not never or multiple times!) is a major part of business intelligence. MDM comes into play because it will comb through these mountains of data and make sure that all the information is consistent, accurate, and all placed in one database so that employees don’t have to search high and low and waste their time. MDM also has operational MDM functions.  This is not a redundancy.  Operational MDM means that when one employee updates one bit of information in the database, for example – updating a new address for a customer, operational MDM ensures that this address is updated throughout the system so that all departments will have the correct information. Another cool thing about MDM is that it features Master Data Services Configuration Manager, which is exactly what it sounds like.  It has a built-in “helper” that lets you set up your database quickly, easily, and with the correct configurations.  While talking about cool features, I can’t skip over the add-in for Excel.  This allows you to link certain data to Excel files for easier sharing and uploading. In summary, I want to emphasize that the scariest part of the database is slowly disappearing.  Everyone knows that a database – one consolidated area for all your data – is a good idea, but the idea of setting one up is daunting.  But SQL Server is making data management easier and easier with features like Master Data Services (MDS). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Master Data Services, MDM

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