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• Java Cloud Service Integration using Web Service Data Control

  - by Jani Rautiainen

  Java Cloud Service (JCS) provides a platform to develop and deploy business applications in the cloud. In Fusion Applications Cloud deployments customers do not have the option to deploy custom applications developed with JDeveloper to ensure the integrity and supportability of the hosted application service. Instead the custom applications can be deployed to the JCS and integrated to the Fusion Application Cloud instance.This series of articles will go through the features of JCS, provide end-to-end examples on how to develop and deploy applications on JCS and how to integrate them with the Fusion Applications instance.In this article a custom application integrating with Fusion Application using Web Service Data Control will be implemented. v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";} Pre-requisites Access to Cloud instance In order to deploy the application access to a JCS instance is needed, a free trial JCS instance can be obtained from Oracle Cloud site. To register you will need a credit card even if the credit card will not be charged. To register simply click "Try it" and choose the "Java" option. The confirmation email will contain the connection details. See this video for example of the registration. Once the request is processed you will be assigned 2 service instances; Java and Database. Applications deployed to the JCS must use Oracle Database Cloud Service as their underlying database. So when JCS instance is created a database instance is associated with it using a JDBC data source. The cloud services can be monitored and managed through the web UI. For details refer to Getting Started with Oracle Cloud. JDeveloper JDeveloper contains Cloud specific features related to e.g. connection and deployment. To use these features download the JDeveloper from JDeveloper download site by clicking the “Download JDeveloper 11.1.1.7.1 for ADF deployment on Oracle Cloud” link, this version of JDeveloper will have the JCS integration features that will be used in this article. For versions that do not include the Cloud integration features the Oracle Java Cloud Service SDK or the JCS Java Console can be used for deployment. For details on installing and configuring the JDeveloper refer to the installation guide. For details on SDK refer to Using the Command-Line Interface to Monitor Oracle Java Cloud Service and Using the Command-Line Interface to Manage Oracle Java Cloud Service. Create Application In this example the “JcsWsDemo” application created in the “Java Cloud Service Integration using Web Service Proxy” article is used as the base. Create Web Service Data Control In this example we will use a Web Service Data Control to integrate with Credit Rule Service in Fusion Applications. The data control will be used to query data from Fusion Applications using a web service call and present the data in a table. To generate the data control choose the “Model” project and navigate to "New -> All Technologies -> Business Tier -> Data Controls -> Web Service Data Control" and enter following: Name: CreditRuleServiceDC URL: https://ic-[POD].oracleoutsourcing.com/icCnSetupCreditRulesPublicService/CreditRuleService?WSDL Service: {{http://xmlns.oracle.com/apps/incentiveCompensation/cn/creditSetup/creditRule/creditRuleService/}CreditRuleService On step 2 select the “findRule” operation: Skip step 3 and on step 4 define the credentials to access the service. Do note that in this example these credentials are only used if testing locally, for JCS deployment credentials need to be manually updated on the EAR file: Click “Finish” and the proxy generation is done. Creating UI In order to use the data control we will need to populate complex objects FindCriteria and FindControl. For simplicity in this example we will create logic in a managed bean that populates the objects. Open “JcsWsDemoBean.java” and add the following logic: Map findCriteria; Map findControl; public void setFindCriteria(Map findCriteria) { this.findCriteria = findCriteria; } public Map getFindCriteria() { findCriteria = new HashMap(); findCriteria.put("fetchSize",10); findCriteria.put("fetchStart",0); return findCriteria; } public void setFindControl(Map findControl) { this.findControl = findControl; } public Map getFindControl() { findControl = new HashMap(); return findControl; } Open “JcsWsDemo.jspx”, navigate to “Data Controls -> CreditRuleServiceDC -> findRule(Object, Object) -> result” and drag and drop the “result” node into the “af:form” element in the page: On the “Edit Table Columns” remove all columns except “RuleId” and “Name”: On the “Edit Action Binding” window displayed enter reference to the java class created above by selecting “#{JcsWsDemoBean.findCriteria}”: Also define the value for the “findControl” by selecting “#{JcsWsDemoBean.findControl}”. Deploy to JCS For WS DC the authentication details need to be updated on the connection details before deploying. Open “connections.xml” by navigating “Application Resources -> Descriptors -> ADF META-INF -> connections.xml”: Change the user name and password entry from: <soap username="transportUserName" password="transportPassword" To match the access details for the target environment. Follow the same steps as documented in previous article ”Java Cloud Service ADF Web Application”. Once deployed the application can be accessed with URL: https://java-[identity domain].java.[data center].oraclecloudapps.com/JcsWsDemo-ViewController-context-root/faces/JcsWsDemo.jspx When accessed the first 10 rules in the system are displayed: Summary In this article we learned how to integrate with Fusion Applications using a Web Service Data Control in JCS. In future articles various other integration techniques will be covered. 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• The Sensemaking Spectrum for Business Analytics: Translating from Data to Business Through Analysis

  - by Joe Lamantia

  One of the most compelling outcomes of our strategic research efforts over the past several years is a growing vocabulary that articulates our cumulative understanding of the deep structure of the domains of discovery and business analytics. Modes are one example of the deep structure we’ve found.  After looking at discovery activities across a very wide range of industries, question types, business needs, and problem solving approaches, we've identified distinct and recurring kinds of sensemaking activity, independent of context.  We label these activities Modes: Explore, compare, and comprehend are three of the nine recognizable modes.  Modes describe *how* people go about realizing insights.  (Read more about the programmatic research and formal academic grounding and discussion of the modes here: https://www.researchgate.net/publication/235971352_A_Taxonomy_of_Enterprise_Search_and_Discovery) By analogy to languages, modes are the 'verbs' of discovery activity.  When applied to the practical questions of product strategy and development, the modes of discovery allow one to identify what kinds of analytical activity a product, platform, or solution needs to support across a spread of usage scenarios, and then make concrete and well-informed decisions about every aspect of the solution, from high-level capabilities, to which specific types of information visualizations better enable these scenarios for the types of data users will analyze. The modes are a powerful generative tool for product making, but if you've spent time with young children, or had a really bad hangover (or both at the same time...), you understand the difficult of communicating using only verbs.  So I'm happy to share that we've found traction on another facet of the deep structure of discovery and business analytics.  Continuing the language analogy, we've identified some of the ‘nouns’ in the language of discovery: specifically, the consistently recurring aspects of a business that people are looking for insight into.  We call these discovery Subjects, since they identify *what* people focus on during discovery efforts, rather than *how* they go about discovery as with the Modes. Defining the collection of Subjects people repeatedly focus on allows us to understand and articulate sense making needs and activity in more specific, consistent, and complete fashion.  In combination with the Modes, we can use Subjects to concretely identify and define scenarios that describe people’s analytical needs and goals.  For example, a scenario such as ‘Explore [a Mode] the attrition rates [a Measure, one type of Subject] of our largest customers [Entities, another type of Subject] clearly captures the nature of the activity — exploration of trends vs. deep analysis of underlying factors — and the central focus — attrition rates for customers above a certain set of size criteria — from which follow many of the specifics needed to address this scenario in terms of data, analytical tools, and methods. We can also use Subjects to translate effectively between the different perspectives that shape discovery efforts, reducing ambiguity and increasing impact on both sides the perspective divide.  For example, from the language of business, which often motivates analytical work by asking questions in business terms, to the perspective of analysis.  The question posed to a Data Scientist or analyst may be something like “Why are sales of our new kinds of potato chips to our largest customers fluctuating unexpectedly this year?” or “Where can innovate, by expanding our product portfolio to meet unmet needs?”.  Analysts translate questions and beliefs like these into one or more empirical discovery efforts that more formally and granularly indicate the plan, methods, tools, and desired outcomes of analysis.  From the perspective of analysis this second question might become, “Which customer needs of type ‘A', identified and measured in terms of ‘B’, that are not directly or indirectly addressed by any of our current products, offer 'X' potential for ‘Y' positive return on the investment ‘Z' required to launch a new offering, in time frame ‘W’?  And how do these compare to each other?”.  Translation also happens from the perspective of analysis to the perspective of data; in terms of availability, quality, completeness, format, volume, etc. By implication, we are proposing that most working organizations — small and large, for profit and non-profit, domestic and international, and in the majority of industries — can be described for analytical purposes using this collection of Subjects.  This is a bold claim, but simplified articulation of complexity is one of the primary goals of sensemaking frameworks such as this one.  (And, yes, this is in fact a framework for making sense of sensemaking as a category of activity - but we’re not considering the recursive aspects of this exercise at the moment.) Compellingly, we can place the collection of subjects on a single continuum — we call it the Sensemaking Spectrum — that simply and coherently illustrates some of the most important relationships between the different types of Subjects, and also illuminates several of the fundamental dynamics shaping business analytics as a domain.  As a corollary, the Sensemaking Spectrum also suggests innovation opportunities for products and services related to business analytics. The first illustration below shows Subjects arrayed along the Sensemaking Spectrum; the second illustration presents examples of each kind of Subject.  Subjects appear in colors ranging from blue to reddish-orange, reflecting their place along the Spectrum, which indicates whether a Subject addresses more the viewpoint of systems and data (Data centric and blue), or people (User centric and orange).  This axis is shown explicitly above the Spectrum.  Annotations suggest how Subjects align with the three significant perspectives of Data, Analysis, and Business that shape business analytics activity.  This rendering makes explicit the translation and bridging function of Analysts as a role, and analysis as an activity. Subjects are best understood as fuzzy categories [http://georgelakoff.files.wordpress.com/2011/01/hedges-a-study-in-meaning-criteria-and-the-logic-of-fuzzy-concepts-journal-of-philosophical-logic-2-lakoff-19731.pdf], rather than tightly defined buckets.  For each Subject, we suggest some of the most common examples: Entities may be physical things such as named products, or locations (a building, or a city); they could be Concepts, such as satisfaction; or they could be Relationships between entities, such as the variety of possible connections that define linkage in social networks.  Likewise, Events may indicate a time and place in the dictionary sense; or they may be Transactions involving named entities; or take the form of Signals, such as ‘some Measure had some value at some time’ - what many enterprises understand as alerts.   The central story of the Spectrum is that though consumers of analytical insights (represented here by the Business perspective) need to work in terms of Subjects that are directly meaningful to their perspective — such as Themes, Plans, and Goals — the working realities of data (condition, structure, availability, completeness, cost) and the changing nature of most discovery efforts make direct engagement with source data in this fashion impossible.  Accordingly, business analytics as a domain is structured around the fundamental assumption that sense making depends on analytical transformation of data.  Analytical activity incrementally synthesizes more complex and larger scope Subjects from data in its starting condition, accumulating insight (and value) by moving through a progression of stages in which increasingly meaningful Subjects are iteratively synthesized from the data, and recombined with other Subjects.  The end goal of  ‘laddering’ successive transformations is to enable sense making from the business perspective, rather than the analytical perspective.Synthesis through laddering is typically accomplished by specialized Analysts using dedicated tools and methods. Beginning with some motivating question such as seeking opportunities to increase the efficiency (a Theme) of fulfillment processes to reach some level of profitability by the end of the year (Plan), Analysts will iteratively wrangle and transform source data Records, Values and Attributes into recognizable Entities, such as Products, that can be combined with Measures or other data into the Events (shipment of orders) that indicate the workings of the business.  More complex Subjects (to the right of the Spectrum) are composed of or make reference to less complex Subjects: a business Process such as Fulfillment will include Activities such as confirming, packing, and then shipping orders.  These Activities occur within or are conducted by organizational units such as teams of staff or partner firms (Networks), composed of Entities which are structured via Relationships, such as supplier and buyer.  The fulfillment process will involve other types of Entities, such as the products or services the business provides.  The success of the fulfillment process overall may be judged according to a sophisticated operating efficiency Model, which includes tiered Measures of business activity and health for the transactions and activities included.  All of this may be interpreted through an understanding of the operational domain of the businesses supply chain (a Domain).   We'll discuss the Spectrum in more depth in succeeding posts.

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• Using jQuery to POST Form Data to an ASP.NET ASMX AJAX Web Service

  - by Rick Strahl

  The other day I got a question about how to call an ASP.NET ASMX Web Service or PageMethods with the POST data from a Web Form (or any HTML form for that matter). The idea is that you should be able to call an endpoint URL, send it regular urlencoded POST data and then use Request.Form[] to retrieve the posted data as needed. My first reaction was that you can’t do it, because ASP.NET ASMX AJAX services (as well as Page Methods and WCF REST AJAX Services) require that the content POSTed to the server is posted as JSON and sent with an application/json or application/x-javascript content type. IOW, you can’t directly call an ASP.NET AJAX service with regular urlencoded data. Note that there are other ways to accomplish this. You can use ASP.NET MVC and a custom route, an HTTP Handler or separate ASPX page, or even a WCF REST service that’s configured to use non-JSON inputs. However if you want to use an ASP.NET AJAX service (or Page Methods) with a little bit of setup work it’s actually quite easy to capture all the form variables on the client and ship them up to the server. The basic steps needed to make this happen are: Capture form variables into an array on the client with jQuery’s .serializeArray() function Use $.ajax() or my ServiceProxy class to make an AJAX call to the server to send this array On the server create a custom type that matches the .serializeArray() name/value structure Create extension methods on NameValue[] to easily extract form variables Create a [WebMethod] that accepts this name/value type as an array (NameValue[]) This seems like a lot of work but realize that steps 3 and 4 are a one time setup step that can be reused in your entire site or multiple applications. Let’s look at a short example that looks like this as a base form of fields to ship to the server: The HTML for this form looks something like this: <div id="divMessage" class="errordisplay" style="display: none"> </div> <div> <div class="label">Name:</div> <div><asp:TextBox runat="server" ID="txtName" /></div> </div> <div> <div class="label">Company:</div> <div><asp:TextBox runat="server" ID="txtCompany"/></div> </div> <div> <div class="label" ></div> <div> <asp:DropDownList runat="server" ID="lstAttending"> <asp:ListItem Text="Attending" Value="Attending"/> <asp:ListItem Text="Not Attending" Value="NotAttending" /> <asp:ListItem Text="Maybe Attending" Value="MaybeAttending" /> <asp:ListItem Text="Not Sure Yet" Value="NotSureYet" /> </asp:DropDownList> </div> </div> <div> <div class="label">Special Needs:<br /> <small>(check all that apply)</small></div> <div> <asp:ListBox runat="server" ID="lstSpecialNeeds" SelectionMode="Multiple"> <asp:ListItem Text="Vegitarian" Value="Vegitarian" /> <asp:ListItem Text="Vegan" Value="Vegan" /> <asp:ListItem Text="Kosher" Value="Kosher" /> <asp:ListItem Text="Special Access" Value="SpecialAccess" /> <asp:ListItem Text="No Binder" Value="NoBinder" /> </asp:ListBox> </div> </div> <div> <div class="label"></div> <div> <asp:CheckBox ID="chkAdditionalGuests" Text="Additional Guests" runat="server" /> </div> </div> <hr /> <input type="button" id="btnSubmit" value="Send Registration" /> The form includes a few different kinds of form fields including a multi-selection listbox to demonstrate retrieving multiple values. Setting up the Server Side [WebMethod] The [WebMethod] on the server we’re going to call is going to be very simple and just capture the content of these values and echo then back as a formatted HTML string. Obviously this is overly simplistic but it serves to demonstrate the simple point of capturing the POST data on the server in an AJAX callback. public class PageMethodsService : System.Web.Services.WebService { [WebMethod] public string SendRegistration(NameValue[] formVars) { StringBuilder sb = new StringBuilder(); sb.AppendFormat("Thank you {0}, <br/><br/>", HttpUtility.HtmlEncode(formVars.Form("txtName"))); sb.AppendLine("You've entered the following: <hr/>"); foreach (NameValue nv in formVars) { // strip out ASP.NET form vars like _ViewState/_EventValidation if (!nv.name.StartsWith("__")) { if (nv.name.StartsWith("txt") || nv.name.StartsWith("lst") || nv.name.StartsWith("chk")) sb.Append(nv.name.Substring(3)); else sb.Append(nv.name); sb.AppendLine(": " + HttpUtility.HtmlEncode(nv.value) + "<br/>"); } } sb.AppendLine("<hr/>"); string[] needs = formVars.FormMultiple("lstSpecialNeeds"); if (needs == null) sb.AppendLine("No Special Needs"); else { sb.AppendLine("Special Needs: <br/>"); foreach (string need in needs) { sb.AppendLine("&nbsp;&nbsp;" + need + "<br/>"); } } return sb.ToString(); } } The key feature of this method is that it receives a custom type called NameValue[] which is an array of NameValue objects that map the structure that the jQuery .serializeArray() function generates. There are two custom types involved in this: The actual NameValue type and a NameValueExtensions class that defines a couple of extension methods for the NameValue[] array type to allow for single (.Form()) and multiple (.FormMultiple()) value retrieval by name. The NameValue class is as simple as this and simply maps the structure of the array elements of .serializeArray(): public class NameValue { public string name { get; set; } public string value { get; set; } } The extension method class defines the .Form() and .FormMultiple() methods to allow easy retrieval of form variables from the returned array: /// <summary> /// Simple NameValue class that maps name and value /// properties that can be used with jQuery's /// $.serializeArray() function and JSON requests /// </summary> public static class NameValueExtensionMethods { /// <summary> /// Retrieves a single form variable from the list of /// form variables stored /// </summary> /// <param name="formVars"></param> /// <param name="name">formvar to retrieve</param> /// <returns>value or string.Empty if not found</returns> public static string Form(this NameValue[] formVars, string name) { var matches = formVars.Where(nv => nv.name.ToLower() == name.ToLower()).FirstOrDefault(); if (matches != null) return matches.value; return string.Empty; } /// <summary> /// Retrieves multiple selection form variables from the list of /// form variables stored. /// </summary> /// <param name="formVars"></param> /// <param name="name">The name of the form var to retrieve</param> /// <returns>values as string[] or null if no match is found</returns> public static string[] FormMultiple(this NameValue[] formVars, string name) { var matches = formVars.Where(nv => nv.name.ToLower() == name.ToLower()).Select(nv => nv.value).ToArray(); if (matches.Length == 0) return null; return matches; } } Using these extension methods it’s easy to retrieve individual values from the array: string name = formVars.Form("txtName"); or multiple values: string[] needs = formVars.FormMultiple("lstSpecialNeeds"); if (needs != null) { // do something with matches } Using these functions in the SendRegistration method it’s easy to retrieve a few form variables directly (txtName and the multiple selections of lstSpecialNeeds) or to iterate over the whole list of values. Of course this is an overly simple example – in typical app you’d probably want to validate the input data and save it to the database and then return some sort of confirmation or possibly an updated data list back to the client. Since this is a full AJAX service callback realize that you don’t have to return simple string values – you can return any of the supported result types (which are most serializable types) including complex hierarchical objects and arrays that make sense to your client code. POSTing Form Variables from the Client to the AJAX Service To call the AJAX service method on the client is straight forward and requires only use of little native jQuery plus JSON serialization functionality. To start add jQuery and the json2.js library to your page: <script src="Scripts/jquery.min.js" type="text/javascript"></script> <script src="Scripts/json2.js" type="text/javascript"></script> json2.js can be found here (be sure to remove the first line from the file): http://www.json.org/json2.js It’s required to handle JSON serialization for those browsers that don’t support it natively. With those script references in the document let’s hookup the button click handler and call the service: $(document).ready(function () { $("#btnSubmit").click(sendRegistration); }); function sendRegistration() { var arForm = $("#form1").serializeArray(); $.ajax({ url: "PageMethodsService.asmx/SendRegistration", type: "POST", contentType: "application/json", data: JSON.stringify({ formVars: arForm }), dataType: "json", success: function (result) { var jEl = $("#divMessage"); jEl.html(result.d).fadeIn(1000); setTimeout(function () { jEl.fadeOut(1000) }, 5000); }, error: function (xhr, status) { alert("An error occurred: " + status); } }); } The key feature in this code is the $("#form1").serializeArray();  call which serializes all the form fields of form1 into an array. Each form var is represented as an object with a name/value property. This array is then serialized into JSON with: JSON.stringify({ formVars: arForm }) The format for the parameter list in AJAX service calls is an object with one property for each parameter of the method. In this case its a single parameter called formVars and we’re assigning the array of form variables to it. The URL to call on the server is the name of the Service (or ASPX Page for Page Methods) plus the name of the method to call. On return the success callback receives the result from the AJAX callback which in this case is the formatted string which is simply assigned to an element in the form and displayed. Remember the result type is whatever the method returns – it doesn’t have to be a string. Note that ASP.NET AJAX and WCF REST return JSON data as a wrapped object so the result has a ‘d’ property that holds the actual response: jEl.html(result.d).fadeIn(1000); Slightly simpler: Using ServiceProxy.js If you want things slightly cleaner you can use the ServiceProxy.js class I’ve mentioned here before. The ServiceProxy class handles a few things for calling ASP.NET and WCF services more cleanly: Automatic JSON encoding Automatic fix up of ‘d’ wrapper property Automatic Date conversion on the client Simplified error handling Reusable and abstracted To add the service proxy add: <script src="Scripts/ServiceProxy.js" type="text/javascript"></script> and then change the code to this slightly simpler version: <script type="text/javascript"> proxy = new ServiceProxy("PageMethodsService.asmx/"); $(document).ready(function () { $("#btnSubmit").click(sendRegistration); }); function sendRegistration() { var arForm = $("#form1").serializeArray(); proxy.invoke("SendRegistration", { formVars: arForm }, function (result) { var jEl = $("#divMessage"); jEl.html(result).fadeIn(1000); setTimeout(function () { jEl.fadeOut(1000) }, 5000); }, function (error) { alert(error.message); } ); } The code is not very different but it makes the call as simple as specifying the method to call, the parameters to pass and the actions to take on success and error. No more remembering which content type and data types to use and manually serializing to JSON. This code also removes the “d” property processing in the response and provides more consistent error handling in that the call always returns an error object regardless of a server error or a communication error unlike the native $.ajax() call. Either approach works and both are pretty easy. The ServiceProxy really pays off if you use lots of service calls and especially if you need to deal with date values returned from the server  on the client. Summary Making Web Service calls and getting POST data to the server is not always the best option – ASP.NET and WCF AJAX services are meant to work with data in objects. However, in some situations it’s simply easier to POST all the captured form data to the server instead of mapping all properties from the input fields to some sort of message object first. For this approach the above POST mechanism is useful as it puts the parsing of the data on the server and leaves the client code lean and mean. It’s even easy to build a custom model binder on the server that can map the array values to properties on an object generically with some relatively simple Reflection code and without having to manually map form vars to properties and do string conversions. Keep in mind though that other approaches also abound. ASP.NET MVC makes it pretty easy to create custom routes to data and the built in model binder makes it very easy to deal with inbound form POST data in its original urlencoded format. The West Wind West Wind Web Toolkit also includes functionality for AJAX callbacks using plain POST values. All that’s needed is a Method parameter to query/form value to specify the method to be called on the server. After that the content type is completely optional and up to the consumer. It’d be nice if the ASP.NET AJAX Service and WCF AJAX Services weren’t so tightly bound to the content type so that you could more easily create open access service endpoints that can take advantage of urlencoded data that is everywhere in existing pages. It would make it much easier to create basic REST endpoints without complicated service configuration. Ah one can dream! In the meantime I hope this article has given you some ideas on how you can transfer POST data from the client to the server using JSON – it might be useful in other scenarios beyond ASP.NET AJAX services as well. Additional Resources ServiceProxy.js A small JavaScript library that wraps $.ajax() to call ASP.NET AJAX and WCF AJAX Services. Includes date parsing extensions to the JSON object, a global dataFilter for processing dates on all jQuery JSON requests, provides cleanup for the .NET wrapped message format and handles errors in a consistent fashion. Making jQuery Calls to WCF/ASMX with a ServiceProxy Client More information on calling ASMX and WCF AJAX services with jQuery and some more background on ServiceProxy.js. Note the implementation has slightly changed since the article was written. ww.jquery.js The West Wind West Wind Web Toolkit also includes ServiceProxy.js in the West Wind jQuery extension library. This version is slightly different and includes embedded json encoding/decoding based on json2.js.© Rick Strahl, West Wind Technologies, 2005-2010Posted in jQuery  ASP.NET  AJAX  

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• SQLAuthority News – Download Whitepaper Using SharePoint List Data in PowerPivot

  - by pinaldave

  One of the many features of Microsoft SQL Server PowerPivot is the range of data sources that can be used to import data. Anything, from Microsoft SQL Server relational databases, Oracle databases, and Microsoft Access databases, to text documents, can be used as data sources in PowerPivot. In this paper, I explain one of the new and upcoming data sources that people are excited about – SharePoint list data in the form of Atom feeds. This white paper goes on to explain the different ways you can import SharePoint list data into PowerPivot, what types of lists are supported, various components that need to be installed to use this feature, and where to get those components. Download and read this whitepaper. Note: Abstract is taken from MSDN Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology

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• Visualising data a different way with Pivot collections

  - by Rob Farley

  Roger’s been doing a great job extending PivotViewer recently, and you can find the list of LobsterPot pivots at http://pivot.lobsterpot.com.au Many months back, the TED Talk that Gary Flake did about Pivot caught my imagination, and I did some research into it. At the time, most of what we did with Pivot was geared towards what we could do for clients, including making Pivot collections based on students at a school, and using it to browse PDF invoices by their various properties. We had actual commercial work based on Pivot collections back then, and it was all kinds of fun. Later, we made some collections for events that were happening, and even got featured in the TechEd Australia keynote. But I’m getting ahead of myself... let me explain the concept. A Pivot collection is an XML file (with .cxml extension) which lists Items, each linking to an image that’s stored in a Deep Zoom format (this means that it contains tiles like Bing Maps, so that the browser can request only the ones of interest according to the zoom level). This collection can be shown in a Silverlight application that uses the PivotViewer control, or in the Pivot Browser that’s available from getpivot.com. Filtering and sorting the items according to their facets (attributes, such as size, age, category, etc), the PivotViewer rearranges the way that these are shown in a very dynamic way. To quote Gary Flake, this lets us “see patterns which are otherwise hidden”. This browsing mechanism is very suited to a number of different methods, because it’s just that – browsing. It’s not searching, it’s more akin to window-shopping than doing an internet search. When we decided to put something together for the conferences such as TechEd Australia 2010 and the PASS Summit 2010, we did some screen-scraping to provide a different view of data that was already available online. Nick Hodge and Michael Kordahi from Microsoft liked the idea a lot, and after a bit of tweaking, we produced one that Michael used in the TechEd Australia keynote to show the variety of talks on offer. It’s interesting to see a pattern in this data: The Office track has the most sessions, but if the Interactive Sessions and Instructor-Led Labs are removed, it drops down to only the sixth most popular track, with Cloud Computing taking over. This is something which just isn’t obvious when you look an ordinary search tool. You get a much better feel for the data when moving around it like this. The more observant amongst you will have noticed some difference in the collection that Michael is demonstrating in the picture above with the screenshots I’ve shown. That’s because it’s been extended some more. At the SQLBits conference in the UK this year, I had some interesting discussions with the guys from Xpert360, particularly Phil Carter, who I’d met in 2009 at an earlier SQLBits conference. They had got around to producing a Pivot collection based on the SQLBits data, which we had been planning to do but ran out of time. We discussed some of ways that Pivot could be used, including the ways that my old friend Howard Dierking had extended it for the MSDN Magazine. I’m not suggesting I influenced Xpert360 at all, but they certainly inspired us with some of their posts on the matter So with LobsterPot guys David Gardiner and Roger Noble both having dabbled in Pivot collections (and Dave doing some for clients), I set Roger to work on extending it some more. He’s used various events and so on to be able to make an environment that allows us to do quick deployment of new collections, as well as showing the data in a grid view which behaves as if it were simply a third view of the data (the other two being the array of images and the ‘histogram’ view). I see PivotViewer as being a significant step in data visualisation – so much so that I feature it when I deliver talks on Spatial Data Visualisation methods. Any time when there is information that can be conveyed through an image, you have to ask yourself how best to show that image, and whether that image is the focal point. For Spatial data, the image is most often a map, and the map becomes the central mode for navigation. I show Pivot with postcode areas, since I can browse the postcodes based on their data, and many of the images are recognisable (to locals of South Australia). Naturally, the images could link through to the map itself, and so on, but generally people think of Spatial data in terms of navigating a map, which doesn’t always gel with the information you’re trying to extract. Roger’s even looking into ways to hook PivotViewer into the Bing Maps API, in a similar way to the Deep Earth project, displaying different levels of map detail according to how ‘zoomed in’ the images are. Some of the work that Dave did with one of the schools was generating the Deep Zoom tiles “on the fly”, based on images stored in a database, and Roger has produced a collection which uses images from flickr, that lets you move from one search term to another. Pulling the images down from flickr.com isn’t particularly ideal from a performance aspect, and flickr doesn’t store images in a small-enough format to really lend itself to this use, but you might agree that it’s an interesting concept which compares nicely to using Maps. I’m looking forward to future versions of the PivotViewer control, and hope they provide many more events that can be used, and even more hooks into it. Naturally, LobsterPot could help provide your business with a PivotViewer experience, but you can probably do a lot of it yourself too. There’s a thorough guide at getpivot.com, which is how we got into it. For some examples of what we’ve done, have a look at http://pivot.lobsterpot.com.au. I’d like to see PivotViewer really catch on a data visualisation tool.

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• Oracle Unbreakable Enterprise Kernel and Emulex HBA Eliminate Silent Data Corruption

  - by sergio.leunissen

  Yesterday, Emulex announced that it has added support for T10 Protection Information (T10-PI), formerly called T10-DIF, to a number of its HBAs. When used with Oracle's Unbreakable Enterprise Kernel, this will prevent silent data corruption and help ensure the integrity and regulatory compliance of user data as it is transferred from the application to the SAN From the press release: Traditionally, protecting the integrity of customers' data has been done with multiple discrete solutions, including Error Correcting Code (ECC) and Cyclic Redundancy Check (CRC), but there have been coverage gaps across the I/O path from the operating system to the storage. The implementation of the T10-PI standard via Emulex's BlockGuard feature, in conjunction with other industry player's implementations, ensures that data is validated as it moves through the data path, from the application, to the HBA, to storage, enabling seamless end-to-end integrity. Read the white paper and don't miss the live webcast on eliminating silent data corruption on December 16th!

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• Storing large data in HTTP Session (Java Application)

  - by Umesh Awasthi

  I am asking this question in continuation with http-session-or-database-approach. I am planning to follow this approach. When user add product to cart, create a Cart Model, add items to cart and save to DB. Convert Cart model to cart data and save it to HTTP session. Any update/ edit update underlying cart in DB and update data snap shot in Session. When user click on view cart page, just pick cart data from Session and display to customer. I have following queries regarding HTTP Session How good is it to store large data (Shopping Cart) in Session? How scalable this approach can be ? (With respect to Session) Won't my application going to eat and demand a lot of memory? Is my approach is fine or do i need to consider other points while designing this? Though, we can control what all cart data should be stored in the Session, but still we need to have certain information in cart data being stored in session?

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• Google I/O 2012 - OAuth 2.0 for Identity and Data Access

  Google I/O 2012 - OAuth 2.0 for Identity and Data Access Ryan Boyd Users like to keep their data in one place on the web where it's easily accessible. Whether it's YouTube videos, Google Drive files, Google contacts or one of many other types of data, users need a way to securely grant applications access to their data. OAuth is the key web standard for delegated data access and OAuth 2.0 is the next-generation version with additional security features. This session will cover the latest advances in how OAuth can be used for data access, but will also dive into how you can lower the barrier to entry for your application by allowing users to login using their Google accounts. You will learn, through an example written in Python, how to use OAuth 2.0 to incorporate user identity into your web application. Best practices for desktop applications, mobile applications and server-to-server use cases will also be discussed. From: GoogleDevelopers Views: 11 1 ratings Time: 58:56 More in Science & Technology

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• Aggregating cache data from OCEP in CQL

  - by Manju James

  There are several use cases where OCEP applications need to join stream data with external data, such as data available in a Coherence cache. OCEP’s streaming language, CQL, supports simple cache-key based joins of stream data with data in Coherence (more complex queries will be supported in a future release). However, there are instances where you may need to aggregate the data in Coherence based on input data from a stream. This blog describes a sample that does just that. For our sample, we will use a simplified credit card fraud detection use case. The input to this sample application is a stream of credit card transaction data. The input stream contains information like the credit card ID, transaction time and transaction amount. The purpose of this application is to detect suspicious transactions and send out a warning event. For the sake of simplicity, we will assume that all transactions with amounts greater than $1000 are suspicious. The transaction history is available in a Coherence distributed cache. For every suspicious transaction detected, a warning event must be sent with maximum amount, total amount and total number of transactions over the past 30 days, as shown in the diagram below. Application Input Stream input to the EPN contains events of type CCTransactionEvent. This input has to be joined with the cache with all credit card transactions. The cache is configured in the EPN as shown below: <wlevs:caching-system id="CohCacheSystem" provider="coherence"/> <wlevs:cache id="CCTransactionsCache" value-type="CCTransactionEvent" key-properties="cardID, transactionTime" caching-system="CohCacheSystem"> </wlevs:cache> Application Output The output that must be produced by the application is a fraud warning event. This event is configured in the spring file as shown below. Source for cardHistory property can be seen here. <wlevs:event-type type-name="FraudWarningEvent"> <wlevs:properties type="tuple"> <wlevs:property name="cardID" type="CHAR"/> <wlevs:property name="transactionTime" type="BIGINT"/> <wlevs:property name="transactionAmount" type="DOUBLE"/> <wlevs:property name="cardHistory" type="OBJECT"/> </wlevs:properties </wlevs:event-type> Cache Data Aggregation using Java Cartridge In the output warning event, cardHistory property contains data from the cache aggregated over the past 30 days. To get this information, we use a java cartridge method. This method uses Coherence’s query API on credit card transactions cache to get the required information. Therefore, the java cartridge method requires a reference to the cache. This may be set up by configuring it in the spring context file as shown below: <bean class="com.oracle.cep.ccfraud.CCTransactionsAggregator"> <property name="cache" ref="CCTransactionsCache"/> </bean> This is used by the java class to set a static property: public void setCache(Map cache) { s_cache = (NamedCache) cache; } The code snippet below shows how the total of all the transaction amounts in the past 30 days is computed. Rest of the information required by CardHistory object is calculated in a similar manner. Complete source of this class can be found here. To find out more information about using Coherence's API to query a cache, please refer Coherence Developer’s Guide. public static CreditHistoryData(String cardID) { … Filter filter = QueryHelper.createFilter("cardID = :cardID and transactionTime :transactionTime", map); CardHistoryData history = new CardHistoryData(); Double sum = (Double) s_cache.aggregate(filter, new DoubleSum("getTransactionAmount")); history.setTotalAmount(sum); … return history; } The java cartridge method is used from CQL as seen below: select cardID, transactionTime, transactionAmount, CCTransactionsAggregator.execute(cardID) as cardHistory from inputChannel where transactionAmount1000 This produces a warning event, with history data, for every credit card transaction over $1000. That is all there is to it. The complete source for the sample application, along with the configuration files, is available here. In the sample, I use a simple java bean to load the cache with initial transaction history data. An input adapter is used to create and send transaction events for the input stream.

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• Logical and Physical Modeling for Analytical Applications

  - by Dejan Sarka

  I am proud to announce that my first course for Pluralsight is released. The course title is Logical and Physical Modeling for Analytical Applications. Here is the description of the course. A bad data model leads to an application that does not perform well. Therefore, when developing an application, you should create a good data model from the start. However, even the best logical model can’t help when the physical implementation is bad. It is also important to know how SQL Server stores and accesses data, and how to optimize the data access. Database optimization starts by splitting transactional and analytical applications. In this course, you learn how to support analytical applications with logical design, get understanding of the problems with data access for queries that deal with large amounts of data, and learn about SQL Server optimizations that help solving these problems. Enjoy the course!

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• Telerik Releases the Data Service Wizard

  After a great beta cycle, Telerik is proud to announce today the commercial availability of the OpenAccess Data Service Wizard. You can download it and install it with Telerik OpenAccess Q1 2010 for both Visual Studio 2008 and 2010 RTM. If you are new to the Data Service Wizard, it is a great tool that will allow you to point a wizard at your OpenAccess generated data access classes and automatically build an WCF, Astoria (WCF Data Services), REST or ATOMPub collection endpoint, complete with the CRUD methods if applicable. If you are familiar with the Data Service Wizard already, there will be two new surprises in the release version. If you generated a domain model with the new OpenAccess Visual Entity Designer, you have only one file added to your project, mydomainmodel.rlinq for example. The first surprise of the new Data Service Wizard is that if you right click on the domain model in Visual Studio, ...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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• MSSQL: Copying data from one database to another

  - by DigiMortal

  I have database that has data imported from another server using import and export wizard of SQL Server Management Studio. There is also empty database with same tables but it also has primary keys, foreign keys and indexes. How to get data from first database to another? Here is the description of my crusade. And believe me – it is not nice one. Bugs in import and export wizard There is some awful bugs in import and export wizard that makes data imports and exports possible only on very limited manner: wizard is not able to analyze foreign keys, wizard wants to create tables always, whatever you say in settings. The result is faulty and useless package. Now let’s go step by step and make things work in our scenario. Database There are two databases. Let’s name them like this: PLAIN – contains data imported from remote server (no indexes, no keys, no nothing, just plain dumb data) CORRECT – empty database with same structure as remote database (indexes, keys and everything else but no data) Our goal is to get data from PLAIN to CORRECT. 1. Create import and export package In this point we will create faulty SSIS package using SQL Server Management Studio. Run import and export wizard and let it create SSIS package that reads data from CORRECT and writes it to, let’s say, CORRECT-2. Make sure you enable identity insert. Make sure there are no views selected. Make sure you don’t let package to create tables (you can miss this step because it wants to create tables anyway). Save package to SSIS. 2. Modify import and export package Now let’s clean up the package and remove all faulty crap. Connect SQL Server Management Studio to SSIS instance. Select the package you just saved and export it to your hard disc. Run Business Intelligence Studio. Create new SSIS project (DON’T MISS THIS STEP). Add package from disc as existing item to project and open it. Move to Control Flow page do one of following: Remove all preparation SQL-tasks and connect Data Flow tasks. Modify all preparation SQL-tasks so the existence of tables is checked before table is created (yes, you have to do it manually). Add new Execute-SQL task as first task in control flow: Open task properties. Assign destination connection as connection to use. Insert the following SQL as command:   EXEC sp_MSForEachTable 'ALTER TABLE ? NOCHECK CONSTRAINT ALL' GO   EXEC sp_MSForEachTable 'DELETE FROM ?' GO   Save task. Add new Execute-SQL task as last task in control flow: Open task properties. Assign destination connection as connection to use. Insert the following SQL as command:   EXEC sp_MSForEachTable 'ALTER TABLE ? CHECK CONSTRAINT ALL' GO   Save task Now connect first Execute-SQL task with first Data Flow task and last Data Flow task with second Execute-SQL task. Now move to Package Explorer tab and change connections under Connection Managers folder. Make source connection to use database PLAIN. Make destination connection to use database CORRECT. Save package and rebuilt the project. Update package using SQL Server Management Studio. Some hints: Make sure you take the package from solution folder because it is saved there now. Don’t overwrite existing package. Use numeric suffix and let Management Studio to create a new version of package. Now you are done with your package. Run it to test it and clean out all the errors you find. TRUNCATE vs DELETE You can see that I used DELETE FROM instead of TRUNCATE. Why? Because TRUNCATE has some nasty limits (taken from MSDN): “You cannot use TRUNCATE TABLE on a table referenced by a FOREIGN KEY constraint; instead, use DELETE statement without a WHERE clause. Because TRUNCATE TABLE is not logged, it cannot activate a trigger. TRUNCATE TABLE may not be used on tables participating in an indexed view.” As I am not sure what tables you have and how they are used I provided here the solution that should work for all scenarios. If you need better performance then in some cases you can use TRUNCATE table instead of DELETE. Conclusion My conclusion is bitter this time although I am very positive guy. It is A.D. 2010 and still we have to write stupid hacks for simple things. Simple tools that existed before are long gone and we have to live mysterious bloatware that is our only choice when using default tools. If you take a look at the length of this posting and the count of steps I had to do for one easy thing you should treat it as a signal that something has went wrong in last years. Although I got my job done I would be still more happy if out of box tools are more intelligent one day. References T-SQL Trick for Deleting All Data in Your Database (Mauro Cardarelli) TRUNCATE TABLE (MSDN Library) Error Handling in SQL 2000 – a Background (Erland Sommarskog) Disable/Enable Foreign Key and Check constraints in SQL Server (Decipher)

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• Enterprise MDM: Rationalizing Reference Data in a Fast Changing Environment

  - by Mala Narasimharajan

  By Rahul Kamath Enterprises must move at a rapid pace to establish and retain global market leadership by continuously focusing on operational efficiency, customer intimacy and relentless execution. Reference Data Management    As multi-national companies with a presence in multiple industry categories, market segments, and geographies, their ability to proactively manage changes and harness them to align their front office with back-office operations and performance management initiatives is critical to make the proverbial elephant dance. Managing reference data including types and codes, business taxonomies, complex relationships as well as mappings represent a key component of the broader agenda for enabling flexibility and agility, without sacrificing enterprise-level consistency, regulatory compliance and control. Financial Transformation  Periodically, companies find that processes implemented a decade or more ago no longer mirror the way of doing business and seek to proactively transform how they operate their business and underlying processes. Financial transformation often begins with the redesign of one’s chart of accounts. The ability to model and redesign one’s chart of accounts collaboratively, quickly validate against historical transaction bases and secure business buy-in across multiple line of business stakeholders, while continuing to manage changes within the legacy general ledger systems and downstream analytical applications while piloting the in-flight transformation can mean the difference between controlled success and project failure. Attend the session titled CON8275 - Oracle Hyperion Data Relationship Management: Enabling Enterprise Transformation at Oracle Openworld on Monday, October 1, 2012 at 4:45pm in Ballroom A of the InterContinental Hotel to learn how Oracle’s Data Relationship Management solution can help you stay ahead of the competition and proactively harness master (and reference) data changes to transform your enterprise. Hear in-depth customer testimonials from GE Healthcare and Old Mutual South Africa to learn how others have harnessed this technology effectively to build enduring competitive advantage through business process innovation and investments in master data governance. Hear GE Healthcare discuss how DRM has enabled financial transformation, ERP consolidation, mergers and acquisitions, and the alignment reference data across financial and management reporting applications. Also, learn how Old Mutual SA has upgraded to EBS R12 Financials and is transforming the management of chart of accounts for corporate reporting. Separately, an esteemed panel of DRM customers including Cisco Systems, Nationwide Insurance, Ralcorp Holdings and Mentor Graphics will discuss their perspectives on how DRM has helped them address business challenges associated with enterprise MDM including major change management initiatives including financial transformations, corporate restructuring, mergers & acquisitions, and the rationalization of financial and analytical master reference data to support alternate business perspectives for the alignment of EPM/BI initiatives. Attend the session titled CON9377 - Customer Showcase: Success with Oracle Hyperion Data Relationship Management at Openworld on Thursday, October 4, 2012 at 12:45pm in Ballroom of the InterContinental Hotel to interact with our esteemed speakers first hand.

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• Extending Database-as-a-Service to Provision Databases with Application Data

  - by Nilesh A

  Oracle Enterprise Manager 12c Database as a Service (DBaaS) empowers Self Service/SSA Users to rapidly spawn databases on demand in cloud. The configuration and structure of provisioned databases depends on respective service template selected by Self Service user while requesting for database. In EM12c, the DBaaS Self Service/SSA Administrator has the option of hosting various service templates in service catalog and based on underlying DBCA templates.Many times provisioned databases require production scale data either for UAT, testing or development purpose and managing DBCA templates with data can be unwieldy. So, we need to populate the database using post deployment script option and without any additional work for the SSA Users. The SSA Administrator can automate this task in few easy steps. For details on how to setup DBaaS Self Service Portal refer to the DBaaS CookbookIn this article, I will list steps required to enable EM 12c DBaaS to provision databases with application data in two distinct ways using: 1) Data pump 2) Transportable tablespaces (TTS). The steps listed below are just examples of how to extend EM 12c DBaaS and you can even have your own method plugged in part of post deployment script option. Using Data Pump to populate databases These are the steps to be followed to implement extending DBaaS using Data Pump methodolgy: Production DBA should run data pump export on the production database and make the dump file available to all the servers participating in the database zone [sample shown in Fig.1] -- Full exportexpdp FULL=y DUMPFILE=data_pump_dir:dpfull1%U.dmp, data_pump_dir:dpfull2%U.dmp PARALLEL=4 LOGFILE=data_pump_dir:dpexpfull.log JOB_NAME=dpexpfull Figure-1:  Full export of database using data pump Create a post deployment SQL script [sample shown in Fig. 2] and this script can either be uploaded into the software library by SSA Administrator or made available on a shared location accessible from servers where databases are likely to be provisioned Normal 0 -- Full importdeclare    h1   NUMBER;begin-- Creating the directory object where source database dump is backed up.    execute immediate 'create directory DEST_LOC as''/scratch/nagrawal/OracleHomes/oradata/INITCHNG/datafile''';-- Running import    h1 := dbms_datapump.open (operation => 'IMPORT', job_mode => 'FULL', job_name => 'DB_IMPORT10');    dbms_datapump.set_parallel(handle => h1, degree => 1);    dbms_datapump.add_file(handle => h1, filename => 'IMP_GRIDDB_FULL.LOG', directory => 'DATA_PUMP_DIR', filetype => 3);    dbms_datapump.add_file(handle => h1, filename => 'EXP_GRIDDB_FULL_%U.DMP', directory => 'DEST_LOC', filetype => 1);    dbms_datapump.start_job(handle => h1);    dbms_datapump.detach(handle => h1);end;/ Figure-2: Importing using data pump pl/sql procedures Using DBCA, create a template for the production database – include all the init.ora parameters, tablespaces, datafiles & their sizes SSA Administrator should customize “Create Database Deployment Procedure” and provide DBCA template created in the previous step. In “Additional Configuration Options” step of Customize “Create Database Deployment Procedure” flow, provide the name of the SQL script in the Custom Script section and lock the input (shown in Fig. 3). Continue saving the deployment procedure. Figure-3: Using Custom script option for calling Import SQL Now, an SSA user can login to Self Service Portal and use the flow to provision a database that will also  populate the data using the post deployment step. Using Transportable tablespaces to populate databases Copy of all user/application tablespaces will enable this method of populating databases. These are the required steps to extend DBaaS using transportable tablespaces: Production DBA needs to create a backup of tablespaces. Datafiles may need conversion [such as from Big Endian to Little Endian or vice versa] based on the platform of production and destination where DBaaS created the test database. Here is sample backup script shows how to find out if any conversion is required, describes the steps required to convert datafiles and backup tablespace. SSA Administrator should copy the database (tablespaces) backup datafiles and export dumps to the backup location accessible from the hosts participating in the database zone(s). Create a post deployment SQL script and this script can either be uploaded into the software library by SSA Administrator or made available on a shared location accessible from servers where databases are likely to be provisioned. Here is sample post deployment SQL script using transportable tablespaces. Using DBCA, create a template for the production database – all the init.ora parameters should be included. NOTE: DO NOT choose to bring tablespace data into this template as they will be created SSA Administrator should customize “Create Database Deployment Procedure” and provide DBCA template created in the previous step. In the “Additional Configuration Options” step of the flow, provide the name of the SQL script in the Custom Script section and lock the input. Continue saving the deployment procedure. Now, an SSA user can login to Self Service Portal and use the flow to provision a database that will also populate the data using the post deployment step. More Information: Database-as-a-Service on Exadata Cloud Podcast on Database as a Service using Oracle Enterprise Manager 12c Oracle Enterprise Manager 12c Installation and Administration guide, Cloud Administration guide DBaaS Cookbook Screenwatch: Private Database Cloud: Set Up the Cloud Self-Service Portal Screenwatch: Private Database Cloud: Use the Cloud Self-Service Portal Stay Connected: Twitter |  Face book |  You Tube |  Linked in |  Newsletter

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• Microsoft launches two new Data Centres for Azure in US to meet growing demand

  - by Gopinath

  In order to meet the growing demand for Windows Azure in US, Microsoft has launched two new data centres in US – East US and West US. With the addition of these two data centres the number of Azure data centres across the globe has grown to 8 and 4 among them are located in US. The two new data centres are providing Computer and Storage resources and few enthusiastic customers already deployed their applications. The other services like SQL Azure and AppFabric will be offered by these data centres in the coming months. The addition of new data centres is a good sign to Microsoft as the customer demand for their Cloud offering is growing. Amazon Web Services is the pioneer in Cloud Computing and they offer wider range of Cloud Services compared to Microsoft. Source: Windows Azure Blog

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• Big Data Appliance

  - by David Dorf

  Today Oracle announced the next release of it's Big Data Appliance, an engineered system composed of hardware and software targeting the efficient processing of big data.  The solution leverages 288 Intel cores running Cloudera's distribution of Apache Hadoop in 1.1 TB of main memory.  This monster helps companies acquire, organize, and analyze large volumes of structured and un-structured data. Additionally a new versions of the Oracle Big Data Connectors and Oracle NoSQL Database were released. Why is this important to retailers?  As the infographic below conveys, mobile and social have added even more data to the already huge collections of POS transactions and e-commerce weblogs.  Retailers know that mining that data will help them make better decisions that lead to increased sales, better customer service, and ultimately a successful retail business. Monetate

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• Testing Reference Data Mappings

  - by Michael Stephenson

  Background Mapping reference data is one of the common scenarios in BizTalk development and its usually a bit of a pain when you need to manage a lot of reference data whether it be through the BizTalk Cross Referencing features or some kind of custom solution. I have seen many cases where only a couple of the mapping conditions are ever tested. Approach As usual I like to see these things tested in isolation before you start using them in your BizTalk maps so you know your mapping functions are working as expected. This approach can be used for almost all of your reference data type mapping functions where you can take advantage of MSTests data driven tests to test lots of conditions without having to write millions of tests. Walk Through Rather than go into the details of this here, I'm going to call out to one of my colleagues who wrote a nice little walk through about using data driven tests a while back. Check out Callum's blog: http://callumhibbert.blogspot.com/2009/07/data-driven-tests-with-mstest.html

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• Calling home, receiving calls and smartphone data from the US

  - by Rob Farley

  I got asked about calling home from the US, by someone going to the PASS Summit. I found myself thinking “there should be a blog post about this”... The easiest way to phone home is Skype - no question. Use WiFi, and if you’re calling someone who has Skype on their phone at the other end, it’s free. Even if they don’t, it’s still pretty good price-wise. The PASS Summit conference centre has good WiFI, as do the hotels, and plenty of other places (like Starbucks). But if you’re used to having data all the time, particularly when you’re walking from one place to another, then you’ll want a sim card. This also lets you receive calls more easily, not just solving your data problem. You’ll need to make sure your phone isn’t locked to your local network – get that sorted before you leave. It’s no trouble to drop by a T-mobile or AT&T store and getting a prepaid sim. You can’t get one from the airport, but if the PASS Summit is your first stop, there’s a T-mobile store on 6th in Seattle between Pine & Pike, so you can see it from the Sheraton hotel if that’s where you’re staying. AT&T isn’t far away either. But – there’s an extra step that you should be aware of. If you talk to one of these US telcos, you’ll probably (hopefully I’m wrong, but this is how it was for me recently) be told that their prepaid sims don’t work in smartphones. And they’re right – the APN gets detected and stops the data from working. But luckily, Apple (and others) have provided information about how to change the APN, which has been used by a company based in New Zealand to let you get your phone working. Basically, you send your phone browser to http://unlockit.co.nz and follow the prompts. But do this from a WiFi place somewhere, because you won’t have data access until after you’ve sorted this out... Oh, and if you get a prepaid sim with “unlimited data”, you will still need to get a Data Feature for it. And just for the record – this is WAY easier if you’re going to the UK. I dropped into a T-mobile shop there, and bought a prepaid sim card for five quid, which gave me 250MB data and some (but not much) call credit. In Australia it’s even easier, because you can buy data-enabled sim cards that work in smartphones from the airport when you arrive. I think having access to data really helps you feel at home in a different place. It means you can pull up maps, see what your friends are doing, and more. Hopefully this post helps, but feel free to post comments with extra information if you have it. @rob_farley

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• DataSets and XML - The Simplistic Approach

  One of the first ways I learned how to read xml data from external data sources was by using a DataSet’s ReadXML function. This function takes file path for an XML document and then converts it to a Dataset. This functionality is great when you need a simple way to process an XML document.  In addition the DataSet object also offers a simple way to save data in an xml format by using the WriteXML function. This function saves the current data in the DataSet to an XML file to be used later. DataSet ds  = New DataSet();String filePath = “http://www.yourdomain.com/someData.xml”;String fileSavePath = “C:\Temp\Test.xml”//Read file for this locationds.readxml(filePath);//Save file to this locationds.writexml(fileSavePath); I have used the ReadXML function before when consuming data from external Rss feeds to display on one of my sites.  It allows me to quickly pull in data from external sites with little to no processing. Example site: MyCreditTech.com

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• How is intermediate data organized in MapReduce?

  - by Pedro Cattori

  From what I understand, each mapper outputs an intermediate file. The intermediate data (data contained in each intermediate file) is then sorted by key. Then, a reducer is assigned a key by the master. The reducer reads from the intermediate file containing the key and then calls reduce using the data it has read. But in detail, how is the intermediate data organized? Can a data corresponding to a key be held in multiple intermediate files? What happens when there is too much data corresponding to one key to be held by a single file? In short, how do intermediate partitions differ from intermediate files and how are these differences dealt with in the implementation?

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• Advanced Analytics Oracle Data Mining - NEW 2-Day Training Course

  - by Mike.Hallett(at)Oracle-BI&EPM

  A NEW 2-Day Oracle University (OU) Instructor Led Course on Oracle Data Mining has been developed for partners and customers to learn more about data mining, predictive analytics and knowledge discovery inside the Oracle Database. Oracle Data Mining, provides data mining algorithms that run native for high performance in-database model building and model deployment. This OU course is a great way to learn the advantages and benefits of "big data analytics"; mining data, building and deploying "predictive analytics" all inside the Oracle Database and to work with OBI. To register for a class, click here, then click on View Schedule to see the latest scheduled classes and/or submit your information expressing interest in attending a class.

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• How to reduce tight coupling between two data sources

  - by fstuijt

  I'm having some trouble finding a proper solution to the following architecture problem. In our setting (sketched below) we have 2 data sources, where data source A is the primary source for items of type Foo. A secondary data source exists which can be used to retrieve additional information on a Foo; however this information does not always exist. Furthermore, data source A can be used to retrieve items of type Bar. However, each Bar refers to a Foo. The difficulty here is that each Bar should refer to a Foo which, if available, also contains the information as augmented by data source B. My question is: how to remove the tight coupling between data source A and B?

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• Oracle BPM and Open Data integration development

  - by drrwebber

  Rapidly developing Oracle BPM application solutions with data source integration previously required significant Java and JDeveloper skills. Now using open source tools for open data development significantly reduces the coding needed.  Key tasks can be performed with visual drag and drop designing combined with menu selections entry and automatic form generation directly from XSD schema definitions. The architecture used is extremely lightweight, portable, open platform and scalable allowing integration with a variety of Oracle and non-Oracle data sources and systems. Two videos available on YouTube walk through the process at both an introductory conceptual level and then a deep dive into the programming needed using JDeveloper, Oracle BPM composer and Oracle WLS (WebLogic Server) along with the CAM editor and Open-XDX open source tools. Also available are coding samples and resources from the GitHub project page, along with working online demonstration resources on the VerifyXML site. Combining Oracle BPM with these open source tools provides a comprehensive simple and elegant solution set. Development times are slashed and rapid prototyping is enabled. Also existing data sources can be integrated using open data formats with either XML or JSON along with CRUD accessing via the Open-XDX Java component. The Open-XDX tool is a code-free approach where data mapping is configured as templates using visual drag and drop in the CAM Editor open source tool.  XML or JSON is then automatically generated or processed (output or input) and appropriate SQL statements created to support the data accessing.   Also included is the ability to integrate with fillable PDF forms via the XML templates and the Java PDF form filling library.  Again minimal Java coding is needed to associate the XML source content with the PDF named fields.  The Oracle BPM forms can be automatically generated from XSD schema definitions that are built from the data mapping templates.  This dramatically simplifies development work as all the integration artifacts needed are created by the open source editor toolset. The developer level video is designed as a tutorial with segments, hands-on demonstrations and reviews.  This allows developers to learn the techniques and approaches used in incremental steps. The intended audience ranges from data analysts to developers and assumes only entry level Java skills and knowledge.  Most actions are menu driven while Java coding is limited to simply configuring values and parameters along with performing builds and deployments from JDeveloper and Oracle WLS.   Additional existing Oracle online training resources can be referenced on Oracle BPM and WLS that cover other normal delivery aspects such as user management and application deployment.

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• Powerful Lessons in Data from the Presidential Election

  - by Christina McKeon

  Now that we’ve had a few days to recover from the U.S. presidential election, it’s a good time to take a step back from politics and look for the customer experience lessons that we can take away. The most powerful lesson is that when you know more about your base, you will have an advantage over your competition. That advantage will translate into you winning and your competition losing. Michael Scherer of TIME was given access to Obama’s data analysts two days before the election. His account is documented in Inside the Secret World of the Data Crunchers Who Helped Obama Win. What we learned from Scherer’s inside view is how well Obama’s team did in getting the right data, analyzing it, and acting on it. This data team recognized how critical it was to break down data silos within the campaign. As Scherer noted, they created “a single system that merged information from pollsters, fundraisers, field workers, consumer databases, and social-media and mobile contacts with the main Democratic voter files in the swing states.” The Obama analysis was so meticulous that they knew which celebrity and which type of celebrity event would help them maximize campaign contributions. With a single system, their data models became more precise. They determined which messages were more successful with specific demographic groups and that who made the calls mattered. Data analysis also led to many other changes in Obama’s campaign including a new ad buying strategy, using social media and applications to tap into supporters’ friends, and using new social news sites. While we did not have that same inside view into Romney’s campaign, much of the post-mortem coverage indicates that Romney’s team did not have the right analysis. As Peter Hamby of CNN wrote in Analysis: Why Romney Lost, “Romney officials had modeled an electorate that looked something like a mix of 2004 and 2008….” That historical data did not account for the changing demographics in the U.S. Does your organization approach data like the Obama or Romney team? Do you really know your base? How well can you predict what is going to happen in your business? If you haven’t already put together a strategy and plan to know more, this week’s civics lesson is a powerful reason to do it sooner rather than later. Your competitors are probably thinking the same thing that you are!

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• Video: Analyzing Big Data using Oracle R Enterprise

  - by Sherry LaMonica

  Learn how Oracle R Enterprise is used to generate new insight and new value to business, answering not only what happened, but why it happened. View this YouTube Oracle Channel video overview describing how analyzing big data using Oracle R Enterprise is different from other analytics tools at Oracle. Oracle R Enterprise (ORE),  a component of the Oracle Advanced Analytics Option, couples the wealth of analytics packages in R with the performance, scalability, and security of Oracle Database. ORE executes base R functions transparently on database data without having to pull data from Oracle Database. As an embedded component of the database, Oracle R Enterprise can run your R script and open source packages via embedded R where the database manages the data served to the R engine and user-controlled data parallelism. The result is faster and more secure access to data. ORE also works with the full suite of in-database analytics, providing integrated results to the analyst.

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