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• More Value From Data Using Data Mining Presentation

  Here is a presentation I gave at the SQLBits conference in September which was recorded by Microsoft.  Usually I speak about SSIS but on this particular event I thought people would like to hear something different from me. Microsoft are making a big play for making Data Mining more accessible to everyone and not just boffins.  In this presentation I give an overview of data mining and then do some demonstrations using the excellent Excel Add-Ins available from Microsoft SQL Server 2008 SQL Server 2005 I hope you enjoy this presentation http://go.microsoft.com/?linkid=9633764

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• Move data from others user accounts in my user account

  - by user118136

  I had problems with compiz setting and I make multiple accounts, now I want to transfer my information from all deleted users in my current account, some data I can not copy because I am not right to read, I type in terminal "sudo nautilus" and I get the permission for read, but the copied data is available only for superusers and I must charge the permissions for each file and each folder. How I can copy the information with out the superuser rights OR how I can charge the permissions for selected folder and all files and folders included in it?

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• OpenGL - Cascaded shadow mapping - Texture lookup

  - by Silverlan

  I'm trying to implement cascaded shadow mapping in my engine, but I'm somewhat stuck at the last step. For testing purposes I've made sure all cascades encompass my entire scene. The result is currently this: The different intensity of the cascades is not on purpose, it's actually the problem. This is how I do the texture lookup for the shadow maps inside the fragment shader: layout(std140) uniform CSM { vec4 csmFard; // far distances for each cascade mat4 csmVP[4]; // View-Projection Matrix int numCascades; // Number of cascades to use. In this example it's 4. }; uniform sampler2DArrayShadow csmTextureArray; // The 4 shadow maps in vec4 csmPos[4]; // Vertex position in shadow MVP space float GetShadowCoefficient() { int index = numCascades -1; vec4 shadowCoord; for(int i=0;i<numCascades;i++) { if(gl_FragCoord.z < csmFard[i]) { shadowCoord = csmPos[i]; index = i; break; } } shadowCoord.w = shadowCoord.z; shadowCoord.z = float(index); shadowCoord.x = shadowCoord.x *0.5f +0.5f; shadowCoord.y = shadowCoord.y *0.5f +0.5f; return shadow2DArray(csmTextureArray,shadowCoord).x; } I then use the return value and simply multiply it with the diffuse color. That explains the different intensity of the cascades, since I'm grabbing the depth value directly from the texture. I've tried to do a depth comparison instead, but with limited success: [...] // Same code as above shadowCoord.w = shadowCoord.z; shadowCoord.z = float(index); shadowCoord.x = shadowCoord.x *0.5f +0.5f; shadowCoord.y = shadowCoord.y *0.5f +0.5f; float z = shadow2DArray(csmTextureArray,shadowCoord).x; if(z < shadowCoord.w) return 0.25f; return 1.f; } While this does give me the same shadow value everywhere, it only works for the first cascade, all others are blank: (I colored the cascades because otherwise the transitions wouldn't be visible in this case) What am I missing here?

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• Fluent | Nhibernate multiple inheritance mapping?

  - by Broken Pipe

  I'm trying to map this classes: public interface IBusinessObject { Guid Id { get; set; } } public class Product { public virtual Guid Id { get; set; } public virtual int ProductTypeId { get; set; } } public class ProductWeSell : Product, IBusinessObject { } public class ProductWeDontSell : Product { } Using this Fluent mapping code: public class IBusinessObjectMap : ClassMap<IBusinessObject> { public IBusinessObjectMap() { Id(t => t.Id).GeneratedBy.Guid(); Table("BusinessObject"); } } public class ProductMap : ClassMap<Product> { public ProductMap() { Id(t => t.Id); DiscriminateSubClassesOnColumn("ProductTypeId", "null").Nullable(); } } public class ProductWeSellMap : SubclassMap<ProductWeSell> { public ProductWeSellMap() { DiscriminatorValue(1); KeyColumn("Id"); } } public class ProductWeDontSellMap : SubclassMap<ProductWeDontSell> { public ProductWeDontSellMap() { DiscriminatorValue(2); KeyColumn("Id"); } } But I get {"Duplicate class/entity mapping ProductWeSell"} error. And if we take a look at generated HBM, indeed it's duplicated, but i have no idea how to write this mapping without duplicating it if it's possible at all. Produced hbm: <hibernate-mapping xmlns="urn:nhibernate-mapping-2.2"> <class xmlns="urn:nhibernate-mapping-2.2" name="IBusinessObject" table="BusinessObject"> <joined-subclass name="ProductWeSell" table="ProductWeSell"/> </class> </hibernate-mapping> <hibernate-mapping xmlns="urn:nhibernate-mapping-2.2"> <class xmlns="urn:nhibernate-mapping-2.2" discriminator-value="null" name="Product" table="Product"> <discriminator type="String"> <column name="ProductTypeId" not-null="false" /> </discriminator> <subclass name="ProductWeDontSell" discriminator-value="2" /> <subclass name="ProductWeSell" discriminator-value="1" /> </class> </hibernate-mapping> So far I was unable to figure out how to map this using fluent Nhibernate (i haven't tried mapping this using hmb files). Any help appreciated Fluent or HBM files. The thing I'm trying to solve look identical to this topic: NHibernate inheritance mapping question

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• SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

  - by pinaldave

  Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• First-Time GLSL Shadow Mapping Problems

  - by Locke

  I'm working on building out a 2.5D engine and having massive problems getting my shadows working. I'm at a point where I'm VERY close. So, let's see a picture to see what I have: As you can see above, the image has lighting -- but the shadow map is displaying incorrectly. The shadow map is shown in the bottom left hand side of the screen as a normal 2D texture, so we can see what it looks like at any given time. If you notice, it appears that the shadows are generating backwards in the wrong direction -- I think. But the problem is a little more deep -- I'm just plotting the shadow onto the screen, which I know is wrong -- I'm ignoring the actual test to see if we NEED to show a shadow. The incoming parameters all appear to be correct -- so there has to be something wrong with my shader code somewhere. Here's what my code looks like: VERTEX: uniform mat4 LightModelViewProjectionMatrix; varying vec3 Normal; // The eye-space normal of the current vertex. varying vec4 LightCoordinate; // The texture coordinate of the light of the current vertex. varying vec3 LightDirection; // The eye-space direction of the light. void main() { Normal = normalize(gl_NormalMatrix * gl_Normal); LightDirection = normalize(gl_NormalMatrix * gl_LightSource[0].position.xyz); LightCoordinate = LightModelViewProjectionMatrix * gl_Vertex; LightCoordinate.xy = ( LightCoordinate.xy * 0.5 ) + 0.5; gl_Position = ftransform(); gl_TexCoord[0] = gl_MultiTexCoord0; } FRAGMENT: uniform sampler2D DiffuseMap; uniform sampler2D ShadowMap; varying vec3 Normal; // The eye-space normal of the current vertex. varying vec4 LightCoordinate; // The texture coordinate of the light of the current vertex. varying vec3 LightDirection; // The eye-space direction of the light. void main() { vec4 Texel = texture2D(DiffuseMap, vec2(gl_TexCoord[0])); // Directional lighting //Build ambient lighting vec4 AmbientElement = gl_LightSource[0].ambient; //Build diffuse lighting float Lambert = max(dot(Normal, LightDirection), 0.0); //max(abs(dot(Normal, LightDirection)), 0.0); vec4 DiffuseElement = ( gl_LightSource[0].diffuse * Lambert ); vec4 LightingColor = ( DiffuseElement + AmbientElement ); LightingColor.r = min(LightingColor.r, 1.0); LightingColor.g = min(LightingColor.g, 1.0); LightingColor.b = min(LightingColor.b, 1.0); LightingColor.a = min(LightingColor.a, 1.0); LightingColor *= Texel; //Everything up to this point is PERFECT // Shadow mapping // ------------------------------ vec4 ShadowCoordinate = LightCoordinate / LightCoordinate.w; float DistanceFromLight = texture2D( ShadowMap, ShadowCoordinate.st ).z; float DepthBias = 0.001; float ShadowFactor = 1.0; if( LightCoordinate.w > 0.0 ) { ShadowFactor = DistanceFromLight < ( ShadowCoordinate.z + DepthBias ) ? 0.5 : 1.0; } LightingColor.rgb *= ShadowFactor; //gl_FragColor = LightingColor; //Yes, I know this is wrong, but the line above (gl_FragColor = LightingColor;) produces the wrong effect gl_FragColor = LightingColor * texture2D( ShadowMap, ShadowCoordinate.st ); } I wanted to make sure the coordinates were correct for the shadow map -- so that's why you see it applied to the image as it is below. But the depth for each point seems to be wrong -- the shadows SHOULD be opposite (look at how the image is -- the shaded areas from normal lighting are facing the opposite direction of the shadows). Maybe my matrices are bad or something going in? They're isolated and appear to be correct -- nothing else is going in unusual. When I view from the light's view and get the MVP matrices for it, they're correct. EDIT: Added an image so you can see what happens when I do the correct command at the end of the GLSL: That's the image when the last line is just glFragColor = LightingColor; Maybe someone has some idea of what I screwed up?

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• Data Mining Resources

  - by Dejan Sarka

  There are many different types of analyses, each one with its own pros and cons. Relational reports have a predefined structure, and end users cannot change it. They are simple to use for end users. Reports can use real-time data and snapshots of data to show the state of a report at specific points in time. One of the drawbacks is that report authoring is limited to IT pros and advanced users. Any kind of dynamic restructuring is very limited. If real-time data is used for a report, the report has a negative impact on the performance of the source system. Processing of the reports might be slow because the data comes from relational database management systems, which are not optimized for reporting only. If you create a semantic model of your data, your end users can create ad-hoc report structures. However, the development is more complex because a developer is needed to create these semantic models. For OLAP, you typically use specialized database management systems. You get lightning speed of analyses. End users can use rich and thin clients to interactively change the structure of the report. Typically, they do it graphically. However, the development of an OLAP system is many times quite complex. It involves the preparation and maintenance of an enterprise data warehouse and OLAP cubes. In order to exploit the possibility of real-time restructuring of reports, the users must be both active and educated. The data is usually stale, as it is loaded into data warehouses and OLAP cubes with a scheduled process. With data mining, a structure is not selected in advance; it searches for the structure. As a result, data mining can give you the most valuable results because you can discover patterns you did not expect. A data mining model structure is limited only by the attributes that you use to train the model. One of the drawbacks is that a lot of knowledge is needed for a successful data mining project. End users have to understand the results. Subject matter experts and IT professionals need to understand business problem thoroughly. The development might be sometimes even more complex than the development of OLAP cubes. Each type of analysis has its own place in an enterprise system. SQL Server has tools for all kinds of analyses. However, data mining is the most advanced way of analyzing the data; this is the “I” in BI. In order to get the most out of it, you need to learn quite a lot. In this blog post, I am gathering together resources for learning, including forthcoming events. Books Multiple authors: SQL Server MVP Deep Dives – I wrote an introductory data mining chapter there. Erik Veerman, Teo Lachev and Dejan Sarka: MCTS Self-Paced Training Kit (Exam 70-448): Microsoft SQL Server 2008 - Business Intelligence Development and Maintenance – you can find a good overview of a complete BI solution, including data mining, in this book. Jamie MacLennan, ZhaoHui Tang, and Bogdan Crivat: Data Mining with Microsoft SQL Server 2008 – can’t miss this book if you want to mine your data with SQL Server tools. Michael Berry, Gordon Linoff: Mastering Data Mining: The Art and Science of Customer Relationship Management – data mining from both, business and technical perspective. Dorian Pyle: Data Preparation for Data Mining – an in-depth book about data preparation. Thomas and Ronald Wonnacott: Introductory Statistics – if you thought that you could get away without statistics, then you are not serious about data mining. Jiawei Han and Micheline Kamber: Data Mining Concepts and Techniques – in-depth explanation of the most popular data mining algorithms. Michael Berry and Gordon Linoff: Data Mining Techniques – another book that explains data mining algorithms, more fro a business perspective. Paolo Guidici: Applied Data Mining – very mathematical book, only if you enjoy statistics and mathematics in general. Forthcoming presentations I am presenting two data mining related sessions during the PASS Summit in Charlotte, NC: Wednesday, October 16th, 2013 - Fraud Detection: Notes from the Field – I am showing how to use data mining for a specific business problem. The presentation is based on real-life projects. Friday, October 18th: Excel 2013 Advanced Analytics – I am focusing on Excel Data Mining Add-ins, and how to use them together with Power Pivot and other add-ins. This is the most you can get out of Excel. Sinergija 2013, Belgrade, Serbia Tuesday, October 22nd: Excel 2013 Analytics to the Max – another presentation focusing on the most advanced analytics you can get in Excel. SQL Rally Amsterdam, Netherlands Thursday, November 7th: Advanced Analytics in Excel 2013 – and again I am presenting about data mining in Excel. Why three different titles for the same presentation? I don’t know, I guess I forgot the name I proposed every time right after I sent the proposal. Courses Data Mining with SQL Server 2012 – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. OK, now you know: no more excuses, start learning data mining, get the most out of your data

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• Master Data Services Employees Sample Model

  - by Davide Mauri

  I’ve been playing with Master Data Services quite a lot in those last days and I’m also monitoring the web for all available resources on it. Today I’ve found this freshly released sample available on MSDN Code Gallery: SQL Server Master Data Services Employee Sample Model http://code.msdn.microsoft.com/SSMDSEmployeeSample This sample shows how Recursive Hierarchies can be modeled in order to represent a typical organizational chart scenario where a self-relationship exists on the Employee entity. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Looking for Cutting-Edge Data Integration: 2010 Innovation Awards

  - by dain.hansen

  This year's Oracle Fusion Middleware Innovation Awards will honor customers and partners who are creatively using to various products across Oracle Fusion Middleware. Brand new to this year's awards is a category for Data Integration. Think you have something unique and innovative with one of our Oracle Data Integration products? We'd love to hear from you! Please submit today The deadline for the nomination is 5 p.m. PT Friday, August 6th 2010, and winning organizations will be notified by late August 2010. What you win! FREE pass to Oracle OpenWorld 2010 in San Francisco for select winners in each category. Honored by Oracle executives at awards ceremony held during Oracle OpenWorld 2010 in San Francisco. Oracle Middleware Innovation Award Winner Plaque 1-3 meetings with Oracle Executives during Oracle OpenWorld 2010 Feature article placement in Oracle Magazine and placement in Oracle Press Release Customer snapshot and video testimonial opportunity, to be hosted on oracle.com Podcast interview opportunity with Senior Oracle Executive

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• Data Integration 12c Raising the Big Data Roof at Oracle OpenWorld

  - by Tanu Sood

  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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Author: Dain Hansen, Director, Oracle It was an exciting OpenWorld 2013 for us in the Data Integration track. Our theme this year was all about ‘being future ready’ - previewing one of our biggest releases this year: Oracle Data Integration 12c. Just this week we followed up with this preview by announcing the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. 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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Mark Hurd's keynote on day one set the tone for the Data Integration sessions. Mark focused on big data analytics and the changing consumer expectations. Especially real-time insight is a key theme for Oracle overall and data integration products. In Mark Hurd's keynote we heard from key customers, such as Airbus and Thomson Reuters, how real-time analysis of operational data including machine data creates value, in some cases even saves lives. Thomas Kurian gave a deeper look into Oracle's big data and fast data solutions. In the initial lead Data Integration track session - Brad Adelberg, VP of Development, presented Oracle’s Data Integration 12c product strategy based on key trends from the initial OpenWorld keynotes. Brad talked about how Oracle's data integration products address the new data integration requirements that evolved with cloud computing, big data, and changing consumer expectations and how they set the key themes in our products’ road map. Brad explained why and how fast-time to value, high-performance and future-ready solutions is the top focus areas for product development. If you were not able to attend OpenWorld or this session I recommend reading the white paper: Five New Data Integration Requirements and How to Meet them with Oracle Data Integration, which provides an in-depth look into how Oracle addresses the new trends in the DI market. Following Brad’s session, Nick Wagner provided in depth review of Oracle GoldenGate’s latest features and roadmap. Nick discussed how Oracle GoldenGate’s tight integration with Oracle Database sets the product apart from the competition. We also heard that heterogeneity of the product is still a major focus for GoldenGate’s development and there will be more news on that front when there is a major release. 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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} After GoldenGate’s product strategy session, Denis Gray from the PM team presented Oracle Data Integrator’s product strategy session, talking about the latest and greatest on ODI. Another good session was delivered by long-time GoldenGate users, Comcast.  Jason Hurd and Amit Patel of Comcast talked about the various use cases they deploy Oracle GoldenGate throughout their enterprise, from database upgrades, feeding reporting systems, to active-active database synchronization.  The Comcast team shared many good tips on how to use GoldenGate for both zero downtime upgrades and active-active replication with conflict management requirement. One of our other important goals we had this year for the Data Integration track at OpenWorld was hearing from our customers. We ended day 1 on just that, with a wonderful award ceremony for Oracle Excellence Awards for Oracle Fusion Middleware Innovation. The ceremony was held in the Yerba Buena Center for the Arts. Congratulations to Royal Bank of Scotland and Yalumba Wine Company, the winners in the Data Integration category. You can find more information on the award and the winners in our previous blog post: 2013 Oracle Excellence Awards for Fusion Middleware Innovation… Selected for their innovation use of Oracle’s Data Integration products; the winners for the Data Integration Category are Royal Bank of Scotland and The Yalumba Wine Company. Congratulations!!! Royal Bank of Scotland’s Market and International Banking division provides clients across the globe with seamless trading and competitive pricing, underpinned by a deep knowledge of risk management across the full spectrum of financial products. They handle millions of transactions daily to keep the lifeblood of their clients’ businesses flowing – whether through payment management solutions or through bespoke trade finance solutions. Royal Bank of Scotland is leveraging Oracle GoldenGate and Oracle Data Integrator along with Oracle Business Intelligence Enterprise Edition and the Oracle Database for a variety of solutions. Mainly, Oracle GoldenGate and Oracle Data Integrator are used to feed their data warehouse – providing a real-time data integration solution that feeds transactional data to their analytics system in minutes to enable improved decision making with timely, accurate data for their business users. Oracle Data Integrator’s in-database transformation capabilities and its ability to integrate with Oracle GoldenGate for real-time data capture is the foundation of this implementation. This solution makes it such that changes happening in the analytics systems are available the same day they are deployed on the operational system with 100% data quality guaranteed. Additionally, the solution has helped to reduce their operational database size from 150GB to 10GB. Impressive! Now what if I told you this solution was built in 3 months and had a less than 6 month return on investment? That’s outstanding! The Yalumba Wine Company is situated in the Barossa Valley of Australia. It is the oldest family owned winery in Australia with a unique way of aging their wines in specially crafted 100 liter barrels. Did you know that “Yalumba” is Aboriginal for “all the land around”? The Yalumba Wine Company is growing rapidly, and was in need of introducing a more modern standard to the existing manufacturing processes to meet globalization demands, overall time-to-market, and better operational efficiency objectives of product development. The Yalumba Wine Company worked with a partner, Bristlecone to develop a unique solution whereby Oracle Data Integrator is leveraged to pull data from Salesforce.com and JD Edwards, in addition to their other pre-existing source systems, for consumption into their data warehouse. They have emphasized the overall ease of developing integration workflows with Oracle Data Integrator. The solution has brought better visibility for the business users, shorter data loading and transformation performance to their data warehouse with rapid incorporation of new data sources, and a solid future-proof foundation for their organization. Moving forward, they plan on leveraging more from Oracle’s Data Integration portfolio. Terrific! In addition to these two customers on Tuesday we featured many other important Oracle Data Integrator and Oracle GoldenGate customers. On Tuesday the GoldenGate panel included: Land O’Lakes, Smuckers, and Veolia Water. Besides giving us yummy nutrition and healthy water, these companies have another aspect in common. They all use GoldenGate to boost their ERP application. Please read the recap by Irem Radzik. On Wednesday, the ODI Panel included: Barry Ralston and Ryan Weber of Infinity Insurance, Paul Stracke of Paychex Inc., and Ian Wall of Vertex Pharmaceuticals for a session filled with interesting projects, use cases and approaches to leveraging Oracle Data Integrator. Please read the recap by Sandrine Riley for more. Thanks to everyone who joined with us and we hope to stay connected! To hear more about our Data Integration12c products join us in an upcoming webcast to learn more. Follow us www.twitter.com/ORCLGoldenGate or goto our website at www.oracle.com/goto/dataintegration

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• Fast Data - Big Data's achilles heel

  - by thegreeneman

  At OOW 2013 in Mark Hurd and Thomas Kurian's keynote, they discussed Oracle's Fast Data software solution stack and discussed a number of customers deploying Oracle's Big Data / Fast Data solutions and in particular Oracle's NoSQL Database.  Since that time, there have been a large number of request seeking clarification on how the Fast Data software stack works together to deliver on the promise of real-time Big Data solutions.   Fast Data is a software solution stack that deals with one aspect of Big Data, high velocity.   The software in the Fast Data solution stack involves 3 key pieces and their integration:  Oracle Event Processing, Oracle Coherence, Oracle NoSQL Database.   All three of these technologies address a high throughput, low latency data management requirement.   Oracle Event Processing enables continuous query to filter the Big Data fire hose, enable intelligent chained events to real-time service invocation and augments the data stream to provide Big Data enrichment. Extended SQL syntax allows the definition of sliding windows of time to allow SQL statements to look for triggers on events like breach of weighted moving average on a real-time data stream.    Oracle Coherence is a distributed, grid caching solution which is used to provide very low latency access to cached data when the data is too big to fit into a single process, so it is spread around in a grid architecture to provide memory latency speed access.  It also has some special capabilities to deploy remote behavioral execution for "near data" processing.   The Oracle NoSQL Database is designed to ingest simple key-value data at a controlled throughput rate while providing data redundancy in a cluster to facilitate highly concurrent low latency reads.  For example, when large sensor networks are generating data that need to be captured while analysts are simultaneously extracting the data using range based queries for upstream analytics.  Another example might be storing cookies from user web sessions for ultra low latency user profile management, also leveraging that data using holistic MapReduce operations with your Hadoop cluster to do segmented site analysis.  Understand how NoSQL plays a critical role in Big Data capture and enrichment while simultaneously providing a low latency and scalable data management infrastructure thru clustered, always on, parallel processing in a shared nothing architecture. Learn how easily a NoSQL cluster can be deployed to provide essential services in industry specific Fast Data solutions. See these technologies work together in a demonstration highlighting the salient features of these Fast Data enabling technologies in a location based personalization service. The question then becomes how do these things work together to deliver an end to end Fast Data solution.  The answer is that while different applications will exhibit unique requirements that may drive the need for one or the other of these technologies, often when it comes to Big Data you may need to use them together.   You may have the need for the memory latencies of the Coherence cache, but just have too much data to cache, so you use a combination of Coherence and Oracle NoSQL to handle extreme speed cache overflow and retrieval.   Here is a great reference to how these two technologies are integrated and work together.  Coherence & Oracle NoSQL Database.   On the stream processing side, it is similar as with the Coherence case.  As your sliding windows get larger, holding all the data in the stream can become difficult and out of band data may need to be offloaded into persistent storage.  OEP needs an extreme speed database like Oracle NoSQL Database to help it continue to perform for the real time loop while dealing with persistent spill in the data stream.  Here is a great resource to learn more about how OEP and Oracle NoSQL Database are integrated and work together.  OEP & Oracle NoSQL Database.

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• Oracle Announces Oracle Big Data Appliance X3-2 and Enhanced Oracle Big Data Connectors

  - by jgelhaus

  Enables Customers to Easily Harness the Business Value of Big Data at Lower Cost Engineered System Simplifies Big Data for the Enterprise Oracle Big Data Appliance X3-2 hardware features the latest 8-core Intel® Xeon E5-2600 series of processors, and compared with previous generation, the 18 compute and storage servers with 648 TB raw storage now offer: 33 percent more processing power with 288 CPU cores; 33 percent more memory per node with 1.1 TB of main memory; and up to a 30 percent reduction in power and cooling Oracle Big Data Appliance X3-2 further simplifies implementation and management of big data by integrating all the hardware and software required to acquire, organize and analyze big data. It includes: Support for CDH4.1 including software upgrades developed collaboratively with Cloudera to simplify NameNode High Availability in Hadoop, eliminating the single point of failure in a Hadoop cluster; Oracle NoSQL Database Community Edition 2.0, the latest version that brings better Hadoop integration, elastic scaling and new APIs, including JSON and C support; The Oracle Enterprise Manager plug-in for Big Data Appliance that complements Cloudera Manager to enable users to more easily manage a Hadoop cluster; Updated distributions of Oracle Linux and Oracle Java Development Kit; An updated distribution of open source R, optimized to work with high performance multi-threaded math libraries Read More   Data sheet: Oracle Big Data Appliance X3-2 Oracle Big Data Appliance: Datacenter Network Integration Big Data and Natural Language: Extracting Insight From Text Thomson Reuters Discusses Oracle's Big Data Platform Connectors Integrate Hadoop with Oracle Big Data Ecosystem Oracle Big Data Connectors is a suite of software built by Oracle to integrate Apache Hadoop with Oracle Database, Oracle Data Integrator, and Oracle R Distribution. Enhancements to Oracle Big Data Connectors extend these data integration capabilities. With updates to every connector, this release includes: Oracle SQL Connector for Hadoop Distributed File System, for high performance SQL queries on Hadoop data from Oracle Database, enhanced with increased automation and querying of Hive tables and now supported within the Oracle Data Integrator Application Adapter for Hadoop; Transparent access to the Hive Query language from R and introduction of new analytic techniques executing natively in Hadoop, enabling R developers to be more productive by increasing access to Hadoop in the R environment. Read More Data sheet: Oracle Big Data Connectors High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database

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• Best approach to accessing multiple data source in a web application

  - by ced

  I've a base web application developed with .net technologies (asp.net) used into our LAN by 30 users simultanousley. From this web application I've developed two verticalization used from online users. In future i expect hundreds users simultanousley. Our company has different locations. Each site use its own database. The web application needs to retrieve information from all existing databases. Currently there are 3 database, but it's not excluded in the future expansion of new offices. My question then is: What is the best strategy for a web application to retrieve information from different databases (which have the same schema) whereas the main objective performance data access and high fault tolerance? There are case studies in the literature that I can take as an example? Do you know some good documents to study? Do you have any tips to implement this task so efficient? Intuitively I would say that two possible strategy are: perform queries from different sources in real time and aggregate data on the fly; create a repository that contains the union of the entities of interest and perform queries directly on repository;

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• UV texture mapping with perspective correct interpolation

  - by Twodordan

  I am working on a software rasterizer for educational purposes and I am having issues with the texturing. The problem is, only one face of the cube gets correctly textured. The rest are stretched edges: You can see the running program online here. I have used cartesian coordinates, and all I do is interpolate the uv values along the scanlines. The general formula I use for interpolating the uv coordinates is pretty much the one I use for the z-buffering interpolation and looks like this (in this case for horizontal scanlines): u_Slope = (right.u - left.u) / (triangleRight_x - triangleLeft_x); v_Slope = (right.v - left.v) / (triangleRight_x - triangleLeft_x); //[...] new_u = left.u + ((currentX_onScanLine - triangleLeft_x) * u_Slope); new_v = left.v + ((currentX_onScanLine - triangleLeft_x) * v_Slope); Then, when I add each point to the pixel buffer, I restore z and uv: z = (1/z); uv.u = Math.round(uv.u * z *100);//*100 because my texture is 100x100px uv.v = Math.round(uv.v * z *100); Then I turn the u v indexes into one index in order to fetch the correct pixel from the image data (which is a 1 dimensional px array): var index = texture.width * uv.u + uv.v; //and the rest is unimportant imagedata[index].RGBA bla bla The interpolation formula is correct considering the consistency of the texture (including the straight stripes). However, I seem to get quite a lot of 0 values for either u or v. Which is probably why I only get one face right. Furthermore, why is the texture flipped horizontally? (the "1" is flipped) I must get some sleep now, but before I get into further dissecting of every single value to see what goes wrong, Can someone more experienced guess why might this be happening, just by looking at the cube? "I have no idea what I'm doing" (it's my first time implementing a rasterizer). Did I miss an important stage? Thanks for any insight. PS: My UV values are as follows: { u:0, v:0 }, { u:0, v:0.5 }, { u:0.5, v:0.5 }, { u:0.5, v:0 }, { u:0, v:0 }, { u:0, v:0.5 }, { u:0.5, v:0.5 }, { u:0.5, v:0 }

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• SQL SERVER – Introduction to Big Data – Guest Post

  - by pinaldave

  BIG Data – such a big word – everybody talks about this now a days. It is the word in the database world. In one of the conversation I asked my friend Jasjeet Sigh the same question – what is Big Data? He instantly came up with a very effective write-up.  Jasjeet is working as a Technical Manager with Koenig Solutions. He leads the SQL domain, and holds rich IT industry experience. Talking about Koenig, it is a 19 year old IT training company that offers several certification choices. Some of its courses include SharePoint Training, Project Management certifications, Microsoft Trainings, Business Intelligence programs, Web Design and Development courses etc. Big Data, as the name suggests, is about data that is BIG in nature. The data is BIG in terms of size, and it is difficult to manage such enormous data with relational database management systems that are quite popular these days. Big Data is not just about being large in size, it is also about the variety of the data that differs in form or type. Some examples of Big Data are given below : Scientific data related to weather and atmosphere, Genetics etc Data collected by various medical procedures, such as Radiology, CT scan, MRI etc Data related to Global Positioning System Pictures and Videos Radio Frequency Data Data that may vary very rapidly like stock exchange information Apart from difficulties in managing and storing such data, it is difficult to query, analyze and visualize it. The characteristics of Big Data can be defined by four Vs: Volume: It simply means a large volume of data that may span Petabyte, Exabyte and so on. However it also depends organization to organization that what volume of data they consider as Big Data. Variety: As discussed above, Big Data is not limited to relational information or structured Data. It can also include unstructured data like pictures, videos, text, audio etc. Velocity:  Velocity means the speed by which data changes. The higher is the velocity, the more efficient should be the system to capture and analyze the data. Missing any important point may lead to wrong analysis or may even result in loss. Veracity: It has been recently added as the fourth V, and generally means truthfulness or adherence to the truth. In terms of Big Data, it is more of a challenge than a characteristic. It is difficult to ascertain the truth out of the enormous amount of data and the one that has high velocity. There are always chances of having un-precise and uncertain data. It is a challenging task to clean such data before it is analyzed. Big Data can be considered as the next big thing in the IT sector in terms of innovation and development. If appropriate technologies are developed to analyze and use the information, it can be the driving force for almost all industrial segments. These include Retail, Manufacturing, Service, Finance, Healthcare etc. This will help them to automate business decisions, increase productivity, and innovate and develop new products. Thanks Jasjeet Singh for an excellent write up.  Jasjeet Sign is working as a Technical Manager with Koenig Solutions. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Database, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Big Data

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• Creating a Corporate Data Hub

  - by BuckWoody

  The Windows Azure Marketplace has a rich assortment of data and software offerings for you to use – a type of Software as a Service (SaaS) for IT workers, not necessarily for end-users. Among those offerings is the “Data Hub” – a  codename for a project that ironically actually does what the codename says. In many of our organizations, we have multiple data quality issues. Finding data is one problem, but finding it just once is often a bigger problem. Lots of departments and even individuals have stored the same data more than once, and in some cases, made changes to one of the copies. It’s difficult to know which location or version of the data is authoritative. Then there’s the problem of accessing the data. It’s fairly straightforward to publish a database, share or other location internally to store the data. But then you have to figure out who owns it, how it is controlled, and pass out the various connection strings to those who want to use it. And then you need to figure out how to let folks access the internal data externally – bringing up all kinds of security issues. Finally, in many cases our user community wants us to combine data from the internally sources with external data, bringing up the security, strings, and exploration features up all over again. Enter the Data Hub. This is an online offering, where you assign an administrator and data stewards. You import the data into the service, and it’s available to you - and only you and your organization if you wish. The basic steps for this service are to set up the portal for your company, assign administrators and permissions, and then you assign data areas and import data into them. From there you make them discoverable, and then you have multiple options that you or your users can access that data. You’re then able, if you wish, to combine that data with other data in one location. So how does all that work? What about security? Is it really that easy? And can you really move the data definition off to the Subject Matter Experts (SME’s) that know the particular data stack better than the IT team does? Well, nothing good is easy – but using the Data Hub is actually pretty simple. I’ll give you a link in a moment where you can sign up and try this yourself. Once you sign up, you assign an administrator. From there you’ll create data areas, and then use a simple interface to bring the data in. All of this is done in a portal interface – nothing to install, configure, update or manage. After the data is entered in, and you’ve assigned meta-data to describe it, your users have multiple options to access it. They can simply use the portal – which actually has powerful visualizations you can use on any platform, even mobile phones or tablets.     Your users can also hit the data with Excel – which gives them ultimate flexibility for display, all while using an authoritative, single reference for the data. Since the service is online, they can do this wherever they are – given the proper authentication and permissions. You can also hit the service with simple API calls, like this one from C#: http://msdn.microsoft.com/en-us/library/hh921924  You can make HTTP calls instead of code, and the data can even be exposed as an OData Feed. As you can see, there are a lot of options. You can check out the offering here: http://www.microsoft.com/en-us/sqlazurelabs/labs/data-hub.aspx and you can read the documentation here: http://msdn.microsoft.com/en-us/library/hh921938

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• hibernate fail mapping two tables

  - by sebbalex

  Hi guys, I'd like to understand how it is possible: Until I was working with one table everything worked fine, when I have mapped another table it fails as shown below: Glassfish start: INFO: configuring from resource: /hibernate.cfg.xml INFO: Configuration resource: /hibernate.cfg.xml INFO: Reading mappings from resource : hibernate_centrale.hbm.xml //first table INFO: Mapping class: com.italtel.patchfinder.objects.centrale - centrale INFO: Reading mappings from resource : hibernate_impianti.hbm.xml //second table INFO: Mapping class: com.italtel.patchfinder.objects.Impianto - impianti INFO: Configured SessionFactory: null INFO: schema update complete INFO: Hibernate: select centrale0_.id as id0_, centrale0_.name as name0_, centrale0_.impianto as impianto0_, centrale0_.servizio as servizio0_ from centrale centrale0_ group by centrale0_.name INFO: Hibernate: select centrale0_.id as id0_, centrale0_.name as name0_, centrale0_.impianto as impianto0_, centrale0_.servizio as servizio0_ from centrale centrale0_ where centrale0_.name='ANCONA' order by centrale0_.name asc //Error org.hibernate.hql.ast.QuerySyntaxException: impianti is not mapped [from impianti where impianto='SD' order by modulo asc] at org.hibernate.hql.ast.util.SessionFactoryHelper.requireClassPersister(SessionFactoryHelper.java:181) ..... config: table1 <!DOCTYPE hibernate-mapping PUBLIC "-//Hibernate/Hibernate Mapping DTD 3.0//EN" "http://hibernate.sourceforge.net/hibernate-mapping-3.0.dtd"> <hibernate-mapping> <class name="com.italtel.patchfinder.objects.Impianto" table="impianti"> <id column="id" name="id"> <generator class="increment"/> </id> <property name="impianto"/> <property name="modulo"/> </class> </hibernate-mapping> table2 <!DOCTYPE hibernate-mapping PUBLIC "-//Hibernate/Hibernate Mapping DTD 3.0//EN" "http://hibernate.sourceforge.net/hibernate-mapping-3.0.dtd"> <hibernate-mapping> <class name="com.italtel.patchfinder.objects.centrale" table="centrale"> <id column="id" name="id"> <generator class="increment"/> </id> <property name="name"/> <property name="impianto"/> <property name="servizio"/> </class> </hibernate-mapping> connection stuff ... <property name="hbm2ddl.auto">update</property> <mapping resource="hibernate_centrale.hbm.xml"/> <mapping resource="hibernate_impianti.hbm.xml"/> </session-factory> </hibernate-configuration> Class: public List loadAll() { Session session = sessionFactory.getCurrentSession(); session.beginTransaction(); return session.createQuery("from centrale group by name").list(); } public List<centrale> loadImplants(String centrale) { Session session = sessionFactory.getCurrentSession(); session.beginTransaction(); return session.createQuery("from centrale where name='" + centrale + "' order by name asc").list(); } public List<Impianto> loadModules(String implant) { Session session = sessionFactory.getCurrentSession(); session.beginTransaction(); return session.createQuery("from impianti where impianto='" + implant + "' order by modulo asc").list(); } } Do you have some advice? Thanks in advance

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• Ray Tracing concers: Efficient Data Structure and Photon Mapping

  - by Grieverheart

  I'm trying to build a simple ray tracer for specific target scenes. An example of such scene can be seen below. I'm concerned as to what accelerating data structure would be most efficient in this case since all objects are touching but on the other hand, the scene is uniform. The objects in my ray tracer are stored as a collection of triangles, thus I also have access to individual triangles. Also, when trying to find the bounding box of the scene, how should infinite planes be handled? Should one instead use the viewing frustum to calculate the bounding box? A few other questions I have are about photon mapping. I've read the original paper by Jensen and many more material. In the compact data structure for the photon they introduce, they store photon power as 4 chars, which from my understanding is 3 chars for color and 1 for flux. But I don't understand how 1 char is enough to store a flux of the order of 1/n, where n is the number of photons (I'm also a bit confused about flux vs power). The other question about photon mapping is, if it would be more efficient in my case to store photons per object (or even per Object's triangle) instead of using a balanced kd-tree. Also, same question about bounding box of the scene but for photon mapping. How should one find a bounding box from the pov of the light when infinite planes are involved?

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• Ideal data structure/techniques for storing generic scheduler data in C#

  - by GraemeMiller

  I am trying to implement a generic scheduler object in C# 4 which will output a table in HTML. Basic aim is to show some object along with various attributes, and whether it was doing something in a given time period. The scheduler will output a table displaying the headers: Detail Field 1 ....N| Date1.........N I want to initialise the table with a start date and an end date to create the date range (ideally could also do other time periods e.g. hours but that isn't vital). I then want to provide a generic object which will have associated events. Where an object has events within the period I want a table cell to be marked E.g. Name Height Weight 1/1/2011 2/1/2011 3/1/20011...... 31/1/2011 Ben 5.11 75 X X X Bill 5.7 83 X X So I created scheduler with Start Date=1/1/2011 and end date 31/1/2011 I'd like to give it my person object (already sorted) and tell it which fields I want displayed (Name, Height, Weight) Each person has events which have a start date and end date. Some events will start and end outwith but they should still be shown on the relevant date etc. Ideally I'd like to have been able to provide it with say a class booking object as well. So I'm trying to keep it generic. I have seen Javasript implementations etc of similar. What would a good data structure be for this? Any thoughts on techniques I could use to make it generic. I am not great with generics so any tips appreciated.

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• Trouble with UV Mapping Blender => Unity 3

  - by Lea Hayes

  For some reason I am getting nasty grey edges around the edges of rendered 3D models that are not present in Blender. I seem to be able to solve the problem by reducing the size of the UV coordinates within the part of the texture that is to be mapped. But this means that: I am wasting valuable texture space Loss of accuracy in drawn UV maps Could I be doing something wrong, perhaps a setting in Unity that needs changing? I have watched countless tutorials which demonstrate Blender default generated UV coordinates with "Texture Paint" which are perfectly aligned in Unity. Here is an illustration of the problem: Left: approximately 15 pixels of margin on each side of UV coordinates Right: Approximately 3 pixels of margin on each side of UV coordinates Note: Texture image resolution is 1024x1024

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• Per-pixel displacement mapping GLSL

  - by Chris

  Im trying to implement a per-pixel displacement shader in GLSL. I read through several papers and "tutorials" I found and ended up with trying to implement the approach NVIDIA used in their Cascade Demo (http://www.slideshare.net/icastano/cascades-demo-secrets) starting at Slide 82. At the moment I am completly stuck with following problem: When I am far away the displacement seems to work. But as more I move closer to my surface, the texture gets bent in x-axis and somehow it looks like there is a little bent in general in one direction. EDIT: I added a video: click I added some screen to illustrate the problem: Well I tried lots of things already and I am starting to get a bit frustrated as my ideas run out. I added my full VS and FS code: VS: #version 400 layout(location = 0) in vec3 IN_VS_Position; layout(location = 1) in vec3 IN_VS_Normal; layout(location = 2) in vec2 IN_VS_Texcoord; layout(location = 3) in vec3 IN_VS_Tangent; layout(location = 4) in vec3 IN_VS_BiTangent; uniform vec3 uLightPos; uniform vec3 uCameraDirection; uniform mat4 uViewProjection; uniform mat4 uModel; uniform mat4 uView; uniform mat3 uNormalMatrix; out vec2 IN_FS_Texcoord; out vec3 IN_FS_CameraDir_Tangent; out vec3 IN_FS_LightDir_Tangent; void main( void ) { IN_FS_Texcoord = IN_VS_Texcoord; vec4 posObject = uModel * vec4(IN_VS_Position, 1.0); vec3 normalObject = (uModel * vec4(IN_VS_Normal, 0.0)).xyz; vec3 tangentObject = (uModel * vec4(IN_VS_Tangent, 0.0)).xyz; //vec3 binormalObject = (uModel * vec4(IN_VS_BiTangent, 0.0)).xyz; vec3 binormalObject = normalize(cross(tangentObject, normalObject)); // uCameraDirection is the camera position, just bad named vec3 fvViewDirection = normalize( uCameraDirection - posObject.xyz); vec3 fvLightDirection = normalize( uLightPos.xyz - posObject.xyz ); IN_FS_CameraDir_Tangent.x = dot( tangentObject, fvViewDirection ); IN_FS_CameraDir_Tangent.y = dot( binormalObject, fvViewDirection ); IN_FS_CameraDir_Tangent.z = dot( normalObject, fvViewDirection ); IN_FS_LightDir_Tangent.x = dot( tangentObject, fvLightDirection ); IN_FS_LightDir_Tangent.y = dot( binormalObject, fvLightDirection ); IN_FS_LightDir_Tangent.z = dot( normalObject, fvLightDirection ); gl_Position = (uViewProjection*uModel) * vec4(IN_VS_Position, 1.0); } The VS just builds the TBN matrix, from incoming normal, tangent and binormal in world space. Calculates the light and eye direction in worldspace. And finally transforms the light and eye direction into tangent space. FS: #version 400 // uniforms uniform Light { vec4 fvDiffuse; vec4 fvAmbient; vec4 fvSpecular; }; uniform Material { vec4 diffuse; vec4 ambient; vec4 specular; vec4 emissive; float fSpecularPower; float shininessStrength; }; uniform sampler2D colorSampler; uniform sampler2D normalMapSampler; uniform sampler2D heightMapSampler; in vec2 IN_FS_Texcoord; in vec3 IN_FS_CameraDir_Tangent; in vec3 IN_FS_LightDir_Tangent; out vec4 color; vec2 TraceRay(in float height, in vec2 coords, in vec3 dir, in float mipmap){ vec2 NewCoords = coords; vec2 dUV = - dir.xy * height * 0.08; float SearchHeight = 1.0; float prev_hits = 0.0; float hit_h = 0.0; for(int i=0;i<10;i++){ SearchHeight -= 0.1; NewCoords += dUV; float CurrentHeight = textureLod(heightMapSampler,NewCoords.xy, mipmap).r; float first_hit = clamp((CurrentHeight - SearchHeight - prev_hits) * 499999.0,0.0,1.0); hit_h += first_hit * SearchHeight; prev_hits += first_hit; } NewCoords = coords + dUV * (1.0-hit_h) * 10.0f - dUV; vec2 Temp = NewCoords; SearchHeight = hit_h+0.1; float Start = SearchHeight; dUV *= 0.2; prev_hits = 0.0; hit_h = 0.0; for(int i=0;i<5;i++){ SearchHeight -= 0.02; NewCoords += dUV; float CurrentHeight = textureLod(heightMapSampler,NewCoords.xy, mipmap).r; float first_hit = clamp((CurrentHeight - SearchHeight - prev_hits) * 499999.0,0.0,1.0); hit_h += first_hit * SearchHeight; prev_hits += first_hit; } NewCoords = Temp + dUV * (Start - hit_h) * 50.0f; return NewCoords; } void main( void ) { vec3 fvLightDirection = normalize( IN_FS_LightDir_Tangent ); vec3 fvViewDirection = normalize( IN_FS_CameraDir_Tangent ); float mipmap = 0; vec2 NewCoord = TraceRay(0.1,IN_FS_Texcoord,fvViewDirection,mipmap); //vec2 ddx = dFdx(NewCoord); //vec2 ddy = dFdy(NewCoord); vec3 BumpMapNormal = textureLod(normalMapSampler, NewCoord.xy, mipmap).xyz; BumpMapNormal = normalize(2.0 * BumpMapNormal - vec3(1.0, 1.0, 1.0)); vec3 fvNormal = BumpMapNormal; float fNDotL = dot( fvNormal, fvLightDirection ); vec3 fvReflection = normalize( ( ( 2.0 * fvNormal ) * fNDotL ) - fvLightDirection ); float fRDotV = max( 0.0, dot( fvReflection, fvViewDirection ) ); vec4 fvBaseColor = textureLod( colorSampler, NewCoord.xy,mipmap); vec4 fvTotalAmbient = fvAmbient * fvBaseColor; vec4 fvTotalDiffuse = fvDiffuse * fNDotL * fvBaseColor; vec4 fvTotalSpecular = fvSpecular * ( pow( fRDotV, fSpecularPower ) ); color = ( fvTotalAmbient + (fvTotalDiffuse + fvTotalSpecular) ); } The FS implements the displacement technique in TraceRay method, while always using mipmap level 0. Most of the code is from NVIDIA sample and another paper I found on the web, so I guess there cannot be much wrong in here. At the end it uses the modified UV coords for getting the displaced normal from the normal map and the color from the color map. I looking forward for some ideas. Thanks in advance! Edit: Here is the code loading the heightmap: glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, mWidth, mHeight, 0, GL_RGBA, GL_UNSIGNED_BYTE, mImageData); glGenerateMipmap(GL_TEXTURE_2D); //glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR_MIPMAP_LINEAR); //glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR); //glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT); //glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT); Maybe something wrong in here?

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• 3D Texture Mapping (Atlas)

  - by Tim Hatch

  This is a pretty simple question. If I was to use multiple images in a single texture for a 3D cube, how would I go about re-using each vertex (having 8 total vs 24)? With a single buffer of 8 vertices, I don't see how I'd properly reuse the UV values. Any help on that? I know it's not terribly clear, but I figured it was a simple question. The 2D method is pretty easy, the next coordinates would be the same as the first (0,0 and 0,1 respectively). However, the above 3D version has me quite befuddled.

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• Accessing SQL Data Services via ADO.NET Data Service Client Library

  - by Mehmet Aras

  Is this possible? Basically I would like to use SQL Data Services REST interface and let the ADO.NET Data Service Client library handle communication details and generate the entities that I can use. I looked at the samples in February release of Azure services kit but the samples in there are using HttpWebRequest and HttpWebResponse to consume SQL Data Services RESTfully. I was hoping to use ADO.NET Data Service Client library to abstract low-level details away.

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• Bridging Two Worlds: Big Data and Enterprise Data

  - by Dain C. Hansen

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* 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;} The big data world is all the vogue in today’s IT conversations. It’s a world of volume, velocity, variety – tantalizing us with its untapped potential. It’s a world of transformational game-changing technologies that have already begun to alter the information management landscape. One of the reasons that big data is so compelling is that it’s a universal challenge that impacts every one of us. Whether it is healthcare, financial, manufacturing, government, retail - big data presents a pressing problem for many industries: how can so much information be processed so quickly to deliver the ‘bigger’ picture? With big data we’re tapping into new information that didn’t exist before: social data, weblogs, sensor data, complex content, and more. What also makes big data revolutionary is that it turns traditional information architecture on its head, putting into question commonly accepted notions of where and how data should be aggregated processed, analyzed, and stored. This is where Hadoop and NoSQL come in – new technologies which solve new problems for managing unstructured data. And now for some worst practices that I'd recommend that you please not follow: Worst Practice Lesson 1: Throw away everything that you already know about data management, data integration tools, and start completely over. One shouldn’t forget what’s already running in today’s IT. Today’s Business Analytics, Data Warehouses, Business Applications (ERP, CRM, SCM, HCM), and even many social, mobile, cloud applications still rely almost exclusively on structured data – or what we’d like to call enterprise data. This dilemma is what today’s IT leaders are up against: what are the best ways to bridge enterprise data with big data? And what are the best strategies for dealing with the complexities of these two unique worlds? Worst Practice Lesson 2: Throw away all of your existing business applications … because they don’t run on big data yet. Bridging the two worlds of big data and enterprise data means considering solutions that are complete, based on emerging Hadoop technologies (as well as traditional), and are poised for success through integrated design tools, integrated platforms that connect to your existing business applications, as well as and support real-time analytics. Leveraging these types of best practices translates to improved productivity, lowered TCO, IT optimization, and better business insights. Worst Practice Lesson 3: Separate out [and keep separate] your big data sandboxes from all the current enterprise IT systems. Don’t mix sand among playgrounds. We didn't tell you that you wouldn't get dirty doing this. Correlation between the two worlds is key. The real advantage to analyzing big data comes when you can correlate it with the existing data in your data warehouse or your current applications to make sense of the larger patterns. If you have not followed these worst practices 1-3 then you qualify for the first step of our journey: bridging the two worlds of enterprise data and big data. Over the next several weeks we’ll be discussing this topic along with several others around big data as it relates to data integration. We welcome you to join us in the conversation by following us on twitter on #BridgingBigData or download our latest white paper and resource kit: Big Data and Enterprise Data: Bridging Two Worlds.

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