Search Results

Search found 60085 results on 2404 pages for 'data flow diagrams'.

Page 2/2404 | < Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

• Introduction to Control Flow and Data Flow of SSIS 2008

  Learn about two primary components of SQL Server 2008 Integration Services packages and their graphical representation in the Business Intelligence Development Studio.

  Read the article

• Big Data – Basics of Big Data Analytics – Day 18 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story. When you have plenty of the data around you what is the first thing which comes to your mind? “What do all these data means?” Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as - On which date my maximum friends have a birthday? What is the most favorite film of my most of the friends so I can talk about it and engage them? What is the most liked placed to travel my friends? Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there. There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data. Here are few of the kind of analysis listed which you can use with Big Data. Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc. Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening. Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella. Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths. Big Data Analytics Solutions There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here. Tableau – This has to be one of the most popular visualization tools out in the big data market. SAS – A high performance analytics and infrastructure company IBM and Oracle – They have a range of tools for Big Data Analysis Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist. 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

  Read the article

• Describe business logic with diagrams

  - by Nikos M.

  I am currently developing a web application for my thesis.I was asked by my professor to make diagrams to describe the business logic. Since I don't have a prior experience, I am pretty confused with all the terminology. I managed to clarify,I think, what business rules and business logic are, but I can't find out how you describe the business logic. Is it something particular or is it something more general? Do I need to learn UML? Does the fact that I use MVC affects the way I'll describe it?

  Read the article

• 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

  Read the article

• Flow-Design Cheat Sheet &ndash; Part I, Notation

  - by Ralf Westphal

  You want to avoid the pitfalls of object oriented design? Then this is the right place to start. Use Flow-Oriented Analysis (FOA) and –Design (FOD or just FD for Flow-Design) to understand a problem domain and design a software solution. Flow-Orientation as described here is related to Flow-Based Programming, Event-Based Programming, Business Process Modelling, and even Event-Driven Architectures. But even though “thinking in flows” is not new, I found it helpful to deviate from those precursors for several reasons. Some aim at too big systems for the average programmer, some are concerned with only asynchronous processing, some are even not very much concerned with programming at all. What I was looking for was a design method to help in software projects of any size, be they large or tiny, involing synchronous or asynchronous processing, being local or distributed, running on the web or on the desktop or on a smartphone. That´s why I took ideas from all of the above sources and some additional and came up with Event-Based Components which later got repositioned and renamed to Flow-Design. In the meantime this has generated some discussion (in the German developer community) and several teams have started to work with Flow-Design. Also I´ve conducted quite some trainings using Flow-Orientation for design. The results are very promising. Developers find it much easier to design software using Flow-Orientation than OOAD-based object orientation. Since Flow-Orientation is moving fast and is not covered completely by a single source like a book, demand has increased for at least an overview of the current state of its notation. This page is trying to answer this demand by briefly introducing/describing every notational element as well as their translation into C# source code. Take this as a cheat sheet to put next to your whiteboard when designing software. However, please do not expect any explanation as to the reasons behind Flow-Design elements. Details on why Flow-Design at all and why in this specific way you´ll find in the literature covering the topic. Here´s a resource page on Flow-Design/Event-Based Components, if you´re able to read German. Notation Connected Functional Units The basic element of any FOD are functional units (FU): Think of FUs as some kind of software code block processing data. For the moment forget about classes, methods, “components”, assemblies or whatever. See a FU as an abstract piece of code. Software then consists of just collaborating FUs. I´m using circles/ellipses to draw FUs. But if you like, use rectangles. Whatever suites your whiteboard needs best.   The purpose of FUs is to process input and produce output. FUs are transformational. However, FUs are not called and do not call other FUs. There is no dependency between FUs. Data just flows into a FU (input) and out of it (output). From where and where to is of no concern to a FU.   This way FUs can be concatenated in arbitrary ways:   Each FU can accept input from many sources and produce output for many sinks:   Flows Connected FUs form a flow with a start and an end. Data is entering a flow at a source, and it´s leaving it through a sink. Think of sources and sinks as special FUs which conntect wires to the environment of a network of FUs.   Wiring Details Data is flowing into/out of FUs through wires. This is to allude to electrical engineering which since long has been working with composable parts. Wires are attached to FUs usings pins. They are the entry/exit points for the data flowing along the wires. Input-/output pins currently need not be drawn explicitly. This is to keep designing on a whiteboard simple and quick.   Data flowing is of some type, so wires have a type attached to them. And pins have names. If there is only one input pin and output pin on a FU, though, you don´t need to mention them. The default is Process for a single input pin, and Result for a single output pin. But you´re free to give even single pins different names.   There is a shortcut in use to address a certain pin on a destination FU:   The type of the wire is put in parantheses for two reasons. 1. This way a “no-type” wire can be easily denoted, 2. this is a natural way to describe tuples of data.   To describe how much data is flowing, a star can be put next to the wire type:   Nesting – Boards and Parts If more than 5 to 10 FUs need to be put in a flow a FD starts to become hard to understand. To keep diagrams clutter free they can be nested. You can turn any FU into a flow: This leads to Flow-Designs with different levels of abstraction. A in the above illustration is a high level functional unit, A.1 and A.2 are lower level functional units. One of the purposes of Flow-Design is to be able to describe systems on different levels of abstraction and thus make it easier to understand them. Humans use abstraction/decomposition to get a grip on complexity. Flow-Design strives to support this and make levels of abstraction first class citizens for programming. You can read the above illustration like this: Functional units A.1 and A.2 detail what A is supposed to do. The whole of A´s responsibility is decomposed into smaller responsibilities A.1 and A.2. FU A thus does not do anything itself anymore! All A is responsible for is actually accomplished by the collaboration between A.1 and A.2. Since A now is not doing anything anymore except containing A.1 and A.2 functional units are devided into two categories: boards and parts. Boards are just containing other functional units; their sole responsibility is to wire them up. A is a board. Boards thus depend on the functional units nested within them. This dependency is not of a functional nature, though. Boards are not dependent on services provided by nested functional units. They are just concerned with their interface to be able to plug them together. Parts are the workhorses of flows. They contain the real domain logic. They actually transform input into output. However, they do not depend on other functional units. Please note the usage of source and sink in boards. They correspond to input-pins and output-pins of the board.   Implicit Dependencies Nesting functional units leads to a dependency tree. Boards depend on nested functional units, they are the inner nodes of the tree. Parts are independent, they are the leafs: Even though dependencies are the bane of software development, Flow-Design does not usually draw these dependencies. They are implicitly created by visually nesting functional units. And they are harmless. Boards are so simple in their functionality, they are little affected by changes in functional units they are depending on. But functional units are implicitly dependent on more than nested functional units. They are also dependent on the data types of the wires attached to them: This is also natural and thus does not need to be made explicit. And it pertains mainly to parts being dependent. Since boards don´t do anything with regard to a problem domain, they don´t care much about data types. Their infrastructural purpose just needs types of input/output-pins to match.   Explicit Dependencies You could say, Flow-Orientation is about tackling complexity at its root cause: that´s dependencies. “Natural” dependencies are depicted naturally, i.e. implicitly. And whereever possible dependencies are not even created. Functional units don´t know their collaborators within a flow. This is core to Flow-Orientation. That makes for high composability of functional units. A part is as independent of other functional units as a motor is from the rest of the car. And a board is as dependend on nested functional units as a motor is on a spark plug or a crank shaft. With Flow-Design software development moves closer to how hardware is constructed. Implicit dependencies are not enough, though. Sometimes explicit dependencies make designs easier – as counterintuitive this might sound. So FD notation needs a ways to denote explicit dependencies: Data flows along wires. But data does not flow along dependency relations. Instead dependency relations represent service calls. Functional unit C is depending on/calling services on functional unit S. If you want to be more specific, name the services next to the dependency relation: Although you should try to stay clear of explicit dependencies, they are fundamentally ok. See them as a way to add another dimension to a flow. Usually the functionality of the independent FU (“Customer repository” above) is orthogonal to the domain of the flow it is referenced by. If you like emphasize this by using different shapes for dependent and independent FUs like above. Such dependencies can be used to link in resources like databases or shared in-memory state. FUs can not only produce output but also can have side effects. A common pattern for using such explizit dependencies is to hook a GUI into a flow as the source and/or the sink of data: Which can be shortened to: Treat FUs others depend on as boards (with a special non-FD API the dependent part is connected to), but do not embed them in a flow in the diagram they are depended upon.   Attributes of Functional Units Creation and usage of functional units can be modified with attributes. So far the following have shown to be helpful: Singleton: FUs are by default multitons. FUs in the same of different flows with the same name refer to the same functionality, but to different instances. Think of functional units as objects that get instanciated anew whereever they appear in a design. Sometimes though it´s helpful to reuse the same instance of a functional unit; this is always due to valuable state it holds. Signify this by annotating the FU with a “(S)”. Multiton: FUs on which others depend are singletons by default. This is, because they usually are introduced where shared state comes into play. If you want to change them to be a singletons mark them with a “(M)”. Configurable: Some parts need to be configured before the can do they work in a flow. Annotate them with a “(C)” to have them initialized before any data items to be processed by them arrive. Do not assume any order in which FUs are configured. How such configuration is happening is an implementation detail. Entry point: In each design there needs to be a single part where “it all starts”. That´s the entry point for all processing. It´s like Program.Main() in C# programs. Mark the entry point part with an “(E)”. Quite often this will be the GUI part. How the entry point is started is an implementation detail. Just consider it the first FU to start do its job.   Patterns / Standard Parts If more than a single wire is attached to an output-pin that´s called a split (or fork). The same data is flowing on all of the wires. Remember: Flow-Designs are synchronous by default. So a split does not mean data is processed in parallel afterwards. Processing still happens synchronously and thus one branch after another. Do not assume any specific order of the processing on the different branches after the split.   It is common to do a split and let only parts of the original data flow on through the branches. This effectively means a map is needed after a split. This map can be implicit or explicit.   Although FUs can have multiple input-pins it is preferrable in most cases to combine input data from different branches using an explicit join: The default output of a join is a tuple of its input values. The default behavior of a join is to output a value whenever a new input is received. However, to produce its first output a join needs an input for all its input-pins. Other join behaviors can be: reset all inputs after an output only produce output if data arrives on certain input-pins

  Read the article

• 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.

  Read the article

• WebCenter Spaces 11g PS2 Task Flow Customization

  - by Javier Ductor

  Previously, I wrote about Spaces Template Customization. In order to adapt Spaces to customers prototype, it was necessary to change template and skin, as well as the members task flow. In this entry, I describe how to customize this task flow.Default members portlet:Prototype Members Portlet:First thing to do, I downloaded SpacesTaskflowCustomizationApplication with its guide.This application allows developers to modify task flows in Spaces, such as Announcements, Discussions, Events, Members, etc. Before starting, some configuration is needed in jDeveloper, like changing role to 'Customization Developer' mode, although it is explained in the application guide. It is important to know that the way task flows are modified is through libraries, and they cannot be updated directly in the source code like templates, you must use the Structure panel for this. Steps to customize Members portlet:1. There are two members views: showIconicView and showListView. By default it is set to Iconic view, but in my case I preferred the View list, so I updated in table-of-members-taskflow.xml this default value.2. Change the TableOfMembers-ListView.jspx file. By editing this file, you can control the way this task flow is displayed. So I customized this list view using the structure panel to get the desired look&feel.3. After changes are made, click save all, because every time a library changes an xml file is generated with all modifications listed, and they must be saved.4. Rebuild project and deploy application.5. Open WLST command window and import this customization to MDS repository with the 'import' command.Eventually, this was the result:Other task flows can be customized in a similar way.

  Read the article

• 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.

  Read the article

• 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

  Read the article

• 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

  Read the article

• How to draw diagrams in Open Office?

  - by Amokrane

  Hi, I would like to draw diagrams using Open Office but I didn't find any installed by default. What I am exactly looking for are diagrams that look like the ones that come with MS Office 2007/2010 (like Pyramid diagrams, Star diagrams etc.). Any idea? A plugin to install? Otherwise are there any online services that can do it? (I have tested Cacoo and gliffy but they don't really offer the diagrams that I am looking for). Thanks!

  Read the article

• Task Flow Design Paper Revised

  - by Duncan Mills

  Thanks to some discussion over at the ADF Methodology Group and contributions from Simon Lessard and Jan Vervecken I have been able to make some refinements to the Task Flow Design Fundamentals paper on OTN.As a bonus, whilst I was making some edits anyway I've included some of Frank Nimphius's memory scope diagrams which are a really useful tool for understanding how request, view, backingBean and pageFlow scopes all fit together.

  Read the article

• Remote Task Flow vs. WSRP Portlets

  - by Frank Nimphius

  Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} A remote task flow is bounded task flow that is deployed as a stand-alone Java EE application on a remote server with its URL Invoke property set to url-invoke-allowed. The remote task flow is accessed either from a direct browser GET request or, when called from another ADF application, through the task flow call activity. For more information about how to invoke remote task flows from a task flow call activity see chapter 15.6.4 How to Call a Bounded Task Flow Using a URL of the Oracle Fusion Middleware Fusion Developer's Guide for Oracle Application Development Framework at http://docs.oracle.com/cd/E23943_01/web.1111/b31974/taskflows_activities.htm#CHDJDJEF Compared to WRSP portlets, remote task flows in Oracle JDeveloper 11g R1 and R2 have a functional limitation in that they cannot be embedded as a region on a page but require the calling ADF application to navigate off to another application and page. The difference between a remote task flow call using the task flow call activity and a simple redirect to a remote Java EE application is that the remote task flow has a state token attached that allows to restore the state of the calling application upon task flow return. A use case for a remote task flow call activity is a "yellow page lookup" scenario in which different ADF applications use an remote task flow to lookup people, products or similar to return a selected value to the calling application. Note that remote task flow calls need to be performed from a bounded or unbounded top level task flow of the calling application. If called from a region (using the parent call activity) in a page, the region state is not recovered upon task flow return. ADF developers recently have identified remote task flows as an architecture pattern to partition their ADF applications into independently deployed Java EE applications. While this sounds like a desirable use of the remote task flow feature, it is not possible to achieve for as long as remote task flows don't render as an ADF region.

  Read the article

• Using Exception Handler in an ADF Task Flow

  - by anmprs

  Problem Statement: Exception thrown in a task flow gets wrapped in an exception that gives an unintelligible error message to the user. Figure 1 Solution 1. Over-writing the error message with a user-friendly error message. Figure 2 Steps to code 1. Generating an exception: Write a method that throws an exception and drop it in the task flow.2. Adding an Exception Handler: Write a method (example below) to overwrite the Error in the bean or data control and drop the method in the task flow. Figure 3 This method is marked as the Exception Handler by Right-Click on method > Mark Activity> Exception Handler or by the button that is displayed in this screenshot Figure 4 The Final task flow should look like this. This will overwrite the exception with the error message in figure 2. Note: There is no need for a control flow between the two method calls (as shown below). Figure 5 Solution 2: Re-Routing the task flow to display an error page Figure 6 Steps to code 1. This is the same as step 1 of solution 1.2. Adding an Exception Handler: The Exception handler is not always a method; in this case it is implemented on a task flow return.  The task flow looks like this. Figure 7 In the figure below you will notice that the task flow return points to a control flow ‘error’ in the calling task flow. Figure 8 This control flow in turn goes to a view ‘error.jsff’ which contains the error message that one wishes to display.  This can be seen in the figure below. (‘withErrorHandling’ is a  call to the task flow in figure 7) Figure 9

  Read the article

• 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

  Read the article

• 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.

  Read the article

• 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.

  Read the article

• 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

  Read the article

• 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]} },

  Read the article

• 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.    

  Read the article

• 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!

  Read the article

• 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)

  Read the article

• How to Use USER_DEFINED Activity in OWB Process Flow

  - by Jinggen He

  Process Flow is a very important component of Oracle Warehouse Builder. With Process Flow, we can create and control the ETL process by setting all kinds of activities in a well-constructed flow. In Oracle Warehouse Builder 11gR2, there are 28 kinds of activities, which fall into three categories: Control activities, OWB specific activities and Utility activities. For more information about Process Flow activities, please refer to OWB online doc. Most of those activities are pre-defined for some specific use. For example, the Mapping activity allows execution an OWB mapping in Process Flow and the FTP activity allows an interaction between the local host and a remote FTP server. Besides those activities for specific purposes, the User Defined activity enables you to incorporate into a Process Flow an activity that is not defined within Warehouse Builder. So the User Defined activity brings flexibility and extensibility to Process Flow. In this article, we will take an amazing tour of using the User Defined activity. Let's start. Enable execution of User Defined activity Let's start this section from creating a very simple Process Flow, which contains a Start activity, a User Defined activity and an End Success activity. Leave all parameters of activity USER_DEFINED unchanged except that we enter /tmp/test.sh into the Value column of the COMMAND parameter. Then let's create the shell script test.sh in /tmp directory. Here is the content of /tmp/test.sh (this article is demonstrating a scenario in Linux system, and /tmp/test.sh is a Bash shell script): echo Hello World! > /tmp/test.txt Note: don't forget to grant the execution privilege on /tmp/test.sh to OS Oracle user. For simplicity, we just use the following command. chmod +x /tmp/test.sh OK, it's so simple that we’ve almost done it. Now deploy the Process Flow and run it. For a newly installed OWB, we will come across an error saying "RPE-02248: For security reasons, activity operator Shell has been disabled by the DBA". See below. That's because, by default, the User Defined activity is DISABLED. Configuration about this can be found in <ORACLE_HOME>/owb/bin/admin/Runtime.properties: property.RuntimePlatform.0.NativeExecution.Shell.security_constraint=DISABLED The property can be set to three different values: NATIVE_JAVA, SCHEDULER and DISBALED. Where NATIVE_JAVA uses the Java 'Runtime.exec' interface, SCHEDULER uses a DBMS Scheduler external job submitted by the Control Center repository owner which is executed by the default operating system user configured by the DBA. DISABLED prevents execution via these operators. We enable the execution of User Defined activity by setting: property.RuntimePlatform.0.NativeExecution.Shell.security_constraint= NATIVE_JAVA Restart the Control Center service for the change of setting to take effect. cd <ORACLE_HOME>/owb/rtp/sql sqlplus OWBSYS/<password of OWBSYS> @stop_service.sql sqlplus OWBSYS/<password of OWBSYS> @start_service.sql And then run the Process Flow again. We will see that the Process Flow completes successfully. The execution of /tmp/test.sh successfully generated a file /tmp/test.txt, containing the line Hello World!. Pass parameters to User Defined Activity The Process Flow created in the above section has a drawback: the User Defined activity doesn't accept any information from OWB nor does it give any meaningful results back to OWB. That's to say, it lacks interaction. Maybe, sometimes such a Process Flow can fulfill the business requirement. But for most of the time, we need to get the User Defined activity executed according to some information prior to that step. In this section, we will see how to pass parameters to the User Defined activity and pass them into the to-be-executed shell script. First, let's see how to pass parameters to the script. The User Defined activity has an input parameter named PARAMETER_LIST. This is a list of parameters that will be passed to the command. Parameters are separated from one another by a token. The token is taken as the first character on the PARAMETER_LIST string, and the string must also end in that token. Warehouse Builder recommends the '?' character, but any character can be used. For example, to pass 'abc,' 'def,' and 'ghi' you can use the following equivalent: ?abc?def?ghi? or !abc!def!ghi! or |abc|def|ghi| If the token character or '\' needs to be included as part of the parameter, then it must be preceded with '\'. For example '\\'. If '\' is the token character, then '/' becomes the escape character. Let's configure the PARAMETER_LIST parameter as below: And modify the shell script /tmp/test.sh as below: echo $1 is saying hello to $2! > /tmp/test.txt Re-deploy the Process Flow and run it. We will see that the generated /tmp/test.txt contains the following line: Bob is saying hello to Alice! In the example above, the parameters passed into the shell script are static. This case is not so useful because: instead of passing parameters, we can directly write the value of the parameters in the shell script. To make the case more meaningful, we can pass two dynamic parameters, that are obtained from the previous activity, to the shell script. Prepare the Process Flow as below: The Mapping activity MAPPING_1 has two output parameters: FROM_USER, TO_USER. The User Defined activity has two input parameters: FROM_USER, TO_USER. All the four parameters are of String type. Additionally, the Process Flow has two string variables: VARIABLE_FOR_FROM_USER, VARIABLE_FOR_TO_USER. Through VARIABLE_FOR_FROM_USER, the input parameter FROM_USER of USER_DEFINED gets value from output parameter FROM_USER of MAPPING_1. We achieve this by binding both parameters to VARIABLE_FOR_FROM_USER. See the two figures below. In the same way, through VARIABLE_FOR_TO_USER, the input parameter TO_USER of USER_DEFINED gets value from output parameter TO_USER of MAPPING_1. Also, we need to change the PARAMETER_LIST of the User Defined activity like below: Now, the shell script is getting input from the Mapping activity dynamically. Deploy the Process Flow and all of its necessary dependees then run the Process Flow. We see that the generated /tmp/test.txt contains the following line: USER B is saying hello to USER A! 'USER B' and 'USER A' are two outputs of the Mapping execution. Write the shell script within Oracle Warehouse Builder In the previous section, the shell script is located in the /tmp directory. But sometimes, when the shell script is small, or for the sake of maintaining consistency, you may want to keep the shell script inside Oracle Warehouse Builder. We can achieve this by configuring these three parameters of a User Defined activity properly: COMMAND: Set the path of interpreter, by which the shell script will be interpreted. PARAMETER_LIST: Set it blank. SCRIPT: Enter the shell script content. Note that in Linux the shell script content is passed into the interpreter as standard input at runtime. About how to actually pass parameters to the shell script, we can utilize variable substitutions. As in the following figure, ${FROM_USER} will be replaced by the value of the FROM_USER input parameter of the User Defined activity. So will the ${TO_USER} symbol. Besides the custom substitution variables, OWB also provide some system pre-defined substitution variables. You can refer to the online document for that. Deploy the Process Flow and run it. We see that the generated /tmp/test.txt contains the following line: USER B is saying hello to USER A! Leverage the return value of User Defined activity All of the previous sections are connecting the User Defined activity to END_SUCCESS with an unconditional transition. But what should we do if we want different subsequent activities for different shell script execution results? 1.  The simplest way is to add three simple-conditioned out-going transitions for the User Defined activity just like the figure below. In the figure, to simplify the scenario, we connect the User Defined activity to three End activities. Basically, if the shell script ends successfully, the whole Process Flow will end at END_SUCCESS, otherwise, the whole Process Flow will end at END_ERROR (in our case, ending at END_WARNING seldom happens). In the real world, we can add more complex and meaningful subsequent business logic. 2.  Or we can utilize complex conditions to work with different results of the User Defined activity. Previously, in our script, we only have this line: echo ${FROM_USER} is saying hello to ${TO_USER}! > /tmp/test.txt We can add more logic in it and return different values accordingly. echo ${FROM_USER} is saying hello to ${TO_USER}! > /tmp/test.txt if CONDITION_1 ; then ...... exit 0 fi if CONDITION_2 ; then ...... exit 2 fi if CONDITION_3 ; then ...... exit 3 fi After that we can leverage the result by checking RESULT_CODE in condition expression of those out-going transitions. Let's suppose that we have the Process Flow as the following graph (SUB_PROCESS_n stands for more different further processes): We can set complex condition for the transition from USER_DEFINED to SUB_PROCESS_1 like this: Other transitions can be set in the same way. Note that, in our shell script, we return 0, 2 and 3, but not 1. As in Linux system, if the shell script comes across a system error like IO error, the return value will be 1. We can explicitly handle such a return value. Summary Let's summarize what has been discussed in this article: How to create a Process Flow with a User Defined activity in it How to pass parameters from the prior activity to the User Defined activity and finally into the shell script How to write the shell script within Oracle Warehouse Builder How to do variable substitutions How to let the User Defined activity return different values and in what way can we leverage

  Read the article

• Software/Webapp for creating computer science diagrams

  - by incrediman

  What are some good programs or web-based applications that can be used (preferrably (but not necessarily) for free) to create diagrams for computer science articles or dissertations? Particularly, I'm looking to represent data structures such as stacks, linked lists, trees, etc.. Any ideas?

  Read the article

• 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?

  Read the article

< Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >