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  • Recover data from hard drive with partitions (but not most data) overwritten

    - by Macha
    I have a 500GB hard drive I've been keeping around to recover data from that I removed from a failing NAS drive that got sort of... erratic at the end. I finally got rid of the NAS when during a firmware update it removed the partition table. Fast forward to a week ago, when I was building a new PC, and a mixup resulted in me placing the hard drive in question in the new PC and installing Windows XP on the first 100GB. I'm presuming any data on that first 100GB is now gone, but for the rest of it, is there any way I can recover it at home, as professional data recovery is currently too expensive? I have a blank 1TB HDD if I can store any images of that hard drive on. The problem was definitely with the NAS and not the hard drive, as the hard drive had a successful install of Windows when mistakenly place in the new PC, and there were capacitors in the NAS's circuitry clearly broken. The data I want to recover (in order of priority) is: High: Some jpgs of family photos. Medium: Some RAW files. (There are also jpg versions of all of these) Low: Some mp3s, avis and ISOs, I can re-rip most of these if need be, but it'd be handy not to have to. (I don't need a backup lecture, and if you can hold it in from nagging Jeff Atwood for it, you can hold it in from nagging me for it) In short: The partition tables are gone and overwritten. The data is not overwritten, except for an amount equal to the size of a Windows XP SP3 installation.

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  • Why Oracle Delivers More Value than IBM in Data Integration Solutions

    - by irem.radzik(at)oracle.com
    For data integration projects, IT organization look for a robust but an easy-to-use solution, which simplifies enterprise data architecture while providing exceptional value-- not one that adds complexity and costs. This is a major challenge today for customers who are using IBM InfoSphere products like DataStage or Change Data Capture. Whereas, Oracle consistently delivers higher level value with its data integration products such as Oracle Data Integrator, Oracle GoldenGate. There are many differentiators for Oracle's Data Integration offering in comparison to IBM. Here are the top five: Lower cost of ownership Higher performance in both real-time and bulk data movement Ease of use and flexibility Reliability Complete, Open, and Integrated Middleware Offering Architectural differences between products contribute a great deal to these differences. First of all, Oracle's ETL architecture does not require a middle-tier transformation server, something IBM does require. Not only it costs more to manage an additional transformation server including energy costs, but it adds a performance bottleneck as well. In addition, IBM's data integration products are complex and often require lengthy professional services engagements to integrate. This translates to higher costs and delayed time to market. Then there's the reliability factor. Our customers choose Oracle GoldenGate over IBM's InfoSphere Change Data Capture product because Oracle GoldenGate is designed for mission-critical systems that require guaranteed data delivery and automatic recovery in case of process interruptions. On Thursday we will discuss these key differentiators in detail and provide customer examples that chose Oracle over IBM in data integration projects. Join us on Thursday Feb 10th at 11am PT to learn how Oracle delivers more value than IBM in data integration solutions.

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  • Removing a node from a linked list

    - by lost_with_coding
    I would like to create a delete_node function that deletes the node at the location in the list as a count from the first node. So far this is the code I have: class node: def __init__(self): self.data = None # contains the data self.next = None # contains the reference to the next node class linked_list: def __init__(self): self.cur_node = None def add_node(self, data): new_node = node() # create a new node new_node.data = data new_node.next = self.cur_node # link the new node to the 'previous' node. self.cur_node = new_node # set the current node to the new one. def list_print(self): node = ll.cur_node while node: print node.data node = node.next def delete_node(self,location): node = ll.cur_node count = 0 while count != location: node = node.next count+=1 delete node ll = linked_list() ll.add_node(1) ll.add_node(2) ll.add_node(3) ll.list_print()

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  • Linked List Inserting in sorted format

    - by user2738718
    package practise; public class Node { public int data; public Node next; public Node (int data, Node next) { this.data = data; this.next = next; } public int size (Node list) { int count = 0; while(list != null){ list = list.next; count++; } return count; } public static Node insert(Node head, int value) { Node T; if (head == null || head.data <= value) { T = new Node(value,head); return T; } else { head.next = insert(head.next, value); return head; } } } This work fine for all data values less than the first or the head. anything greater than than doesn't get added to the list.please explain in simple terms thanks.

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  • Top 10 Linked Blogs of 2010

    - by Bill Graziano
    Each week I send out a SQL Server newsletter and include links to interesting blog posts.  I’ve linked to over 500 blog posts so far in 2010.  Late last year I started storing those links in a database so I could do a little reporting.  I tend to link to posts related to the OLTP engine.  I also try to link to the individual blogger in the group blogs.  Unfortunately that wasn’t possible for the SQLCAT and CSS blogs.  I also have a real weakness for posts related to PASS. These are the top 10 blogs that I linked to during the year ordered by the number of posts I linked to. Paul Randal – Paul writes extensively on the internals of the relational engine.  Lots of great posts around transactions, transaction log, disaster recovery, corruption, indexes and DBCC.  I also linked to many of his SQL Server myths posts. Glenn Berry – Glenn writes very interesting posts on how hardware affects SQL Server.  I especially like his posts on the various CPU platforms.  These aren’t necessarily topics that I’m searching for but I really enjoy reading them. The SQLCAT Team – This Microsoft team focuses on the largest and most interesting SQL Server installations.  The regularly publish white papers and best practices. SQL Server CSS Team – These are the top engineers from the Microsoft Customer Service and Support group.  These are the folks you finally talk to after your case has been escalated about 20 times.  They write about the interesting problems they find. Brent Ozar – The posts I linked to mostly focused on the relational engine: CPU, NUMA, SSD drives, performance monitoring, etc.  But Brent writes about a real variety of topics including blogging, social networking, speaking, the MCM, SQL Azure and anything else that seems to strike his fancy.  His posts are always well written and though provoking. Jeremiah Peschka – A number of Jeremiah’s posts weren’t about SQL Server.  He’s very active in the “NoSQL” area and I linked to a number of those posts.  I think it’s important for people to know what other technologies are out there. Brad McGehee – Brad writes about being a DBA including maintenance plans, DBA checklists, compression and audit. Thomas LaRock – I linked to a variety of posts from PBM to networking to 24 Hours of PASS to TDE.  Just a real variety of topics.  Tom always writes with an interesting style usually mixing in a movie theme and/or bacon. Aaron Bertrand – Many of my links this year were Denali features.  He also had a great series on bad habits to kick. Michael J. Swart – This last one surprised me.  There are some well known SQL Server bloggers below Michael on this list.  I linked to posts on indexes, hierarchies, transactions and I/O performance and a variety of other engine related posts.  All are interesting and well thought out.  Many of his non-SQL posts are also very good.  He seems to have an interest in puzzles and other brain teasers.  Michael, I won’t be surprised again!

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  • linked list problem (with insert)

    - by JohnWong
    The problem appears with the insert function that I wrote. 3 conditions must work, I tested b/w 1 and 2, b/w 2 and 3 and as last element, they worked. But b/w 3 and 4, it did not work. It only display up to the new added record, and did not show the fourth element. Efficiency is not my concern here (not yet). Please guide me through this debug process. Thank you very much. #include<iostream> #include<string> using namespace std; struct List // we create a structure called List { string name; string tele; List *nextAddr; }; void populate(List *); void display(List *); void insert(List *); int main() { const int MAXINPUT = 3; char ans; List * data, * current, * point; // create two pointers data = new List; current = data; for (int i = 0; i < (MAXINPUT - 1); i++) { populate(current); current->nextAddr = new List; current = current->nextAddr; } // last record we want to do it sepeartely populate(current); current->nextAddr = NULL; cout << "The current list consists of the following data records: " << endl; display(data); // now ask whether user wants to insert new record or not cout << "Do you want to add a new record (Y/N)?"; cin >> ans; if (ans == 'Y' || ans == 'y') { /* To insert b/w first and second, use point as parameter between second and third uses point->nextAddr between third and fourth uses point->nextAddr->nextAddr and insert as last element, uses current instead */ point = data; insert(()); display(data); } return 0; } void populate(List *data) { cout << "Enter a name: "; cin >> data->name; cout << "Enter a phone number: "; cin >> data->tele; return; } void display(List *content) { while (content != NULL) { cout << content->name << " " << content->tele; content = content->nextAddr; cout << endl; // we skip to next line } return; } void insert(List *last) { List * temp = last->nextAddr; //save the next address to temp last->nextAddr = new List; // now modify the address pointed to new allocation last = last->nextAddr; populate(last); last->nextAddr = temp; // now link all three together, eg 1-NEW-2 return; }

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  • Reworking my singly linked list

    - by Stradigos
    Hello everyone, thanks for taking the time to stop by my question. Below you will find my working SLL, but I want to make more use of C# and, instead of having two classes, SLL and Node, I want to use Node's constructors to do all the work (To where if you pass a string through the node, the constructor will chop it up into char nodes). The problem is, after an a few hours of tinkering, I'm not really getting anywhere... using System; using System.Collections.Generic; using System.Text; using System.IO; namespace PalindromeTester { class Program { static void Main(string[] args) { SLL mySLL = new SLL(); mySLL.add('a'); mySLL.add('b'); mySLL.add('c'); mySLL.add('d'); mySLL.add('e'); mySLL.add('f'); Console.Out.WriteLine("Node count = " + mySLL.count); mySLL.reverse(); mySLL.traverse(); Console.Out.WriteLine("\n The header is: " + mySLL.gethead); Console.In.ReadLine(); } class Node { private char letter; private Node next; public Node() { next = null; } public Node(char c) { this.data = c; } public Node(string s) { } public char data { get { return letter; } set { letter = value; } } public Node nextNode { get { return next; } set { next = value; } } } class SLL { private Node head; private int totalNode; public SLL() { head = null; totalNode = 0; } public void add(char s) { if (head == null) { head = new Node(); head.data = s; } else { Node temp; temp = new Node(); temp.data = s; temp.nextNode = head; head = temp; } totalNode++; } public int count { get { return totalNode; } } public char gethead { get { return head.data; } } public void traverse() { Node temp = head; while(temp != null) { Console.Write(temp.data + " "); temp = temp.nextNode; } } public void reverse() { Node q = null; Node p = this.head; while(p!=null) { Node r=p; p=p.nextNode; r.nextNode=q; q=r; } this.head = q; } } } } Here's what I have so far in trying to work it into Node's constructors: using System; using System.Collections.Generic; using System.Text; using System.IO; namespace PalindromeTester { class Program { static void Main(string[] args) { //Node myList = new Node(); //TextReader tr = new StreamReader("data.txt"); //string line; //while ((line = tr.ReadLine()) != null) //{ // Console.WriteLine(line); //} //tr.Close(); Node myNode = new Node("hello"); Console.Out.WriteLine(myNode.count); myNode.reverse(); myNode.traverse(); // Console.Out.WriteLine(myNode.gethead); Console.In.ReadLine(); } class Node { private char letter; private Node next; private Node head; private int totalNode; public Node() { head = null; totalNode = 0; } public Node(char c) { if (head == null) { head = new Node(); head.data = c; } else { Node temp; temp = new Node(); temp.data = c; temp.nextNode = head; head = temp; } totalNode++; } public Node(string s) { foreach (char x in s) { new Node(x); } } public char data { get { return letter; } set { letter = value; } } public Node nextNode { get { return next; } set { next = value; } } public void reverse() { Node q = null; Node p = this.head; while (p != null) { Node r = p; p = p.nextNode; r.nextNode = q; q = r; } this.head = q; } public void traverse() { Node temp = head; while (temp != null) { Console.Write(temp.data + " "); temp = temp.nextNode; } } public int count { get { return totalNode; } } } } } Ideally, the only constructors and methods I would be left with are Node(), Node(char c), Node(string s), Node reserve() and I'll be reworking traverse into a ToString overload. Any suggestions?

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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  • Introducing Data Annotations Extensions

    - by srkirkland
    Validation of user input is integral to building a modern web application, and ASP.NET MVC offers us a way to enforce business rules on both the client and server using Model Validation.  The recent release of ASP.NET MVC 3 has improved these offerings on the client side by introducing an unobtrusive validation library built on top of jquery.validation.  Out of the box MVC comes with support for Data Annotations (that is, System.ComponentModel.DataAnnotations) and can be extended to support other frameworks.  Data Annotations Validation is becoming more popular and is being baked in to many other Microsoft offerings, including Entity Framework, though with MVC it only contains four validators: Range, Required, StringLength and Regular Expression.  The Data Annotations Extensions project attempts to augment these validators with additional attributes while maintaining the clean integration Data Annotations provides. A Quick Word About Data Annotations Extensions The Data Annotations Extensions project can be found at http://dataannotationsextensions.org/, and currently provides 11 additional validation attributes (ex: Email, EqualTo, Min/Max) on top of Data Annotations’ original 4.  You can find a current list of the validation attributes on the afore mentioned website. The core library provides server-side validation attributes that can be used in any .NET 4.0 project (no MVC dependency). There is also an easily pluggable client-side validation library which can be used in ASP.NET MVC 3 projects using unobtrusive jquery validation (only MVC3 included javascript files are required). On to the Preview Let’s say you had the following “Customer” domain model (or view model, depending on your project structure) in an MVC 3 project: public class Customer { public string Email { get; set; } public int Age { get; set; } public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } When it comes time to create/edit this Customer, you will probably have a CustomerController and a simple form that just uses one of the Html.EditorFor() methods that the ASP.NET MVC tooling generates for you (or you can write yourself).  It should look something like this: With no validation, the customer can enter nonsense for an email address, and then can even report their age as a negative number!  With the built-in Data Annotations validation, I could do a bit better by adding a Range to the age, adding a RegularExpression for email (yuck!), and adding some required attributes.  However, I’d still be able to report my age as 10.75 years old, and my profile picture could still be any string.  Let’s use Data Annotations along with this project, Data Annotations Extensions, and see what we can get: public class Customer { [Email] [Required] public string Email { get; set; }   [Integer] [Min(1, ErrorMessage="Unless you are benjamin button you are lying.")] [Required] public int Age { get; set; }   [FileExtensions("png|jpg|jpeg|gif")] public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s try to put in some invalid values and see what happens: That is very nice validation, all done on the client side (will also be validated on the server).  Also, the Customer class validation attributes are very easy to read and understand. Another bonus: Since Data Annotations Extensions can integrate with MVC 3’s unobtrusive validation, no additional scripts are required! Now that we’ve seen our target, let’s take a look at how to get there within a new MVC 3 project. Adding Data Annotations Extensions To Your Project First we will File->New Project and create an ASP.NET MVC 3 project.  I am going to use Razor for these examples, but any view engine can be used in practice.  Now go into the NuGet Extension Manager (right click on references and select add Library Package Reference) and search for “DataAnnotationsExtensions.”  You should see the following two packages: The first package is for server-side validation scenarios, but since we are using MVC 3 and would like comprehensive sever and client validation support, click on the DataAnnotationsExtensions.MVC3 project and then click Install.  This will install the Data Annotations Extensions server and client validation DLLs along with David Ebbo’s web activator (which enables the validation attributes to be registered with MVC 3). Now that Data Annotations Extensions is installed you have all you need to start doing advanced model validation.  If you are already using Data Annotations in your project, just making use of the additional validation attributes will provide client and server validation automatically.  However, assuming you are starting with a blank project I’ll walk you through setting up a controller and model to test with. Creating Your Model In the Models folder, create a new User.cs file with a User class that you can use as a model.  To start with, I’ll use the following class: public class User { public string Email { get; set; } public string Password { get; set; } public string PasswordConfirm { get; set; } public string HomePage { get; set; } public int Age { get; set; } } Next, create a simple controller with at least a Create method, and then a matching Create view (note, you can do all of this via the MVC built-in tooling).  Your files will look something like this: UserController.cs: public class UserController : Controller { public ActionResult Create() { return View(new User()); }   [HttpPost] public ActionResult Create(User user) { if (!ModelState.IsValid) { return View(user); }   return Content("User valid!"); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Create.cshtml: @model NuGetValidationTester.Models.User   @{ ViewBag.Title = "Create"; }   <h2>Create</h2>   <script src="@Url.Content("~/Scripts/jquery.validate.min.js")" type="text/javascript"></script> <script src="@Url.Content("~/Scripts/jquery.validate.unobtrusive.min.js")" type="text/javascript"></script>   @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>User</legend> @Html.EditorForModel() <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } In the Create.cshtml view, note that we are referencing jquery validation and jquery unobtrusive (jquery is referenced in the layout page).  These MVC 3 included scripts are the only ones you need to enjoy both the basic Data Annotations validation as well as the validation additions available in Data Annotations Extensions.  These references are added by default when you use the MVC 3 “Add View” dialog on a modification template type. Now when we go to /User/Create we should see a form for editing a User Since we haven’t yet added any validation attributes, this form is valid as shown (including no password, email and an age of 0).  With the built-in Data Annotations attributes we can make some of the fields required, and we could use a range validator of maybe 1 to 110 on Age (of course we don’t want to leave out supercentenarians) but let’s go further and validate our input comprehensively using Data Annotations Extensions.  The new and improved User.cs model class. { [Required] [Email] public string Email { get; set; }   [Required] public string Password { get; set; }   [Required] [EqualTo("Password")] public string PasswordConfirm { get; set; }   [Url] public string HomePage { get; set; }   [Integer] [Min(1)] public int Age { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s re-run our form and try to use some invalid values: All of the validation errors you see above occurred on the client, without ever even hitting submit.  The validation is also checked on the server, which is a good practice since client validation is easily bypassed. That’s all you need to do to start a new project and include Data Annotations Extensions, and of course you can integrate it into an existing project just as easily. Nitpickers Corner ASP.NET MVC 3 futures defines four new data annotations attributes which this project has as well: CreditCard, Email, Url and EqualTo.  Unfortunately referencing MVC 3 futures necessitates taking an dependency on MVC 3 in your model layer, which may be unadvisable in a multi-tiered project.  Data Annotations Extensions keeps the server and client side libraries separate so using the project’s validation attributes don’t require you to take any additional dependencies in your model layer which still allowing for the rich client validation experience if you are using MVC 3. Custom Error Message and Globalization: Since the Data Annotations Extensions are build on top of Data Annotations, you have the ability to define your own static error messages and even to use resource files for very customizable error messages. Available Validators: Please see the project site at http://dataannotationsextensions.org/ for an up-to-date list of the new validators included in this project.  As of this post, the following validators are available: CreditCard Date Digits Email EqualTo FileExtensions Integer Max Min Numeric Url Conclusion Hopefully I’ve illustrated how easy it is to add server and client validation to your MVC 3 projects, and how to easily you can extend the available validation options to meet real world needs. The Data Annotations Extensions project is fully open source under the BSD license.  Any feedback would be greatly appreciated.  More information than you require, along with links to the source code, is available at http://dataannotationsextensions.org/. Enjoy!

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  • Linked servers SQLNCLI problem. "No transaction is active"

    - by Felipe Fiali
    Im trying to execute a stored procedure and simply insert its results in a temporary table, and I'm getting the following message: The operation could not be performed because OLE DB provider "SQLNCLI" for linked server "MyServerName" was unable to begin a distributed transaction. OLE DB provider "SQLNCLI" for linked server "MyServerName" returned message "No transaction is active.". My query looks like this: INSERT INTO #TABLE EXEC MyServerName.MyDatabase.dbo.MyStoredProcedure Param1, Param2, Param3 Exact column number, names, the problem is not the result. MSDTC is allowed and started in both computers, Remote procedure calling too. The machines are not in the same domain, but I can execute remote queries from my machine and get the result. I can even execute the stored procedure and see its results, I just can't insert it in another table. Help, please? :)

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  • How to optimize simple linked server select query?

    - by tomaszs
    Hello, I have a table called Table with columns: ID (int, primary key, clustered, unique index) TEXT (varchar 15) on a MSSQL linked server called LS. Linked server is on the same server computer. And: When I call: SELECT ID, TEXT FROM OPENQUERY(LS, 'SELECT ID, TEXT FROM Table') It takes 400 ms. When I call: SELECT ID, TEXT FROM LS.dbo.Table It takes 200 ms And when I call the query directly while being at LS server: SELECT ID, TEXT FROM dbo.Table It takes 100 ms. In many places i've read that OPENQUERY is faster, but in this simple case it does not seem to work. What can I do to make this query faster when I call it from another server, not LS directly?

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  • Copy a linked list

    - by emkrish
    typedef struct Node { int data; Node *next; Node *other; }; Node *pHead; pHead is a singly linked list. The next field points to the next element in the list. The other field may point to any other element (could be one of the previous nodes or one of the nodes ahead) in the list or NULL. How does one write a copy function that duplicates the linked list and its connectivity? None of the elements (next and other) in the new list should point to any element in the old list.

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  • Efficient data structure for fast random access, search, insertion and deletion

    - by Leonel
    I'm looking for a data structure (or structures) that would allow me keep me an ordered list of integers, no duplicates, with indexes and values in the same range. I need four main operations to be efficient, in rough order of importance: taking the value from a given index finding the index of a given value inserting a value at a given index deleting a value at a given index Using an array I have 1 at O(1), but 2 is O(N) and insertion and deletions are expensive (O(N) as well, I believe). A Linked List has O(1) insertion and deletion (once you have the node), but 1 and 2 are O(N) thus negating the gains. I tried keeping two arrays a[index]=value and b[value]=index, which turn 1 and 2 into O(1) but turn 3 and 4 into even more costly operations. Is there a data structure better suited for this?

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  • Query through linked server is very slow

    - by Sergey Olontsev
    I have 2 SQL 2005 servers SRV1 and SRV2. SRV2 is the linked server on SRV1. I run a storep proc with params on SRV2 and it is completed immediately. But when I run the same proc through the linked server on SRV1, for example EXEC [SRV1].DB_TEST.dbo.p_sample_proc it takes about 8-10 minutes to complete. After restarting SRV2 the problem is gone. But some time later it returns. Does anyone have any ideas what it could be?

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  • Adding nodes to a global linked-list

    - by Zack
    I am attempting to construct my first linked list, and having read a basic introduction, have done the following. Firstly, declare a linked list node as: struct errorNode { uint8 error; struct errorNode* next; }; Secondly, define the first node globally as: struct errorNode errorList = {0, NULL}; This has been done to allow each of the libraries that make up my current project to insert errors into a common list. The function to do this is: void errorListWrite(uint8 error) { struct errorNode* newNode = malloc(sizeof(struct errorNode)); newNode->error = error; newNode->next = &errorList; errorList = *newNode; } Whilst this compiles without error, it does not function as expected. I thnk the problem is with the last two statements of the list write function, but I am unsure. A hint as to what I am doing wrong would be most appreciated.

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  • Building a linked list with LINQ

    - by FreshCode
    What is the fastest way to order an unordered list of elements by predecessor (or parent) element index using LINQ? Each element has a unique ID and the ID of that element's predecessor (or parent) element, from which a linked list can be built to represent an ordered state. Example ID | Predecessor's ID --------|-------------------- 20 | 81 81 | NULL 65 | 12 12 | 20 120 | 65 The sorted order is {81, 20, 12, 65, 120}. An (ordered) linked list can easily be assembled iteratively from these elements, but can it be done in fewer LINQ statements? Edit: I should have specified that IDs are not necessarily sequential. I chose 1 to 5 for simplicity. See updated element indices which are random.

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  • Cleaning a dataset of song data - what sort of problem is this?

    - by Rob Lourens
    I have a set of data about songs. Each entry is a line of text which includes the artist name, song title, and some extra text. Some entries are only "extra text". My goal is to resolve as many of these as possible to songs on Spotify using their web API. My strategy so far has been to search for the entry via the API - if there are no results, apply a transformation such as "remove all text between ( )" and search again. I have a list of heuristics and I've had reasonable success with this but as the code gets more and more convoluted I keep thinking there must be a more generic and consistent way. I don't know where to look - any suggestions for what to try, topics to study, buzzwords to google?

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  • Augmenting your Social Efforts via Data as a Service (DaaS)

    - by Mike Stiles
    The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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  • Working with data and meta data that are separated on different servers

    - by afuzzyllama
    While developing a product, I've come across a situation where my group wants to store meta data for data entry forms (questions, layout, etc) in a different database then the database where the collected data is stored. This is mostly for security because we want to be able to have our meta data public facing, while keeping collected data as secure as possible. I was thinking about writing a web service that provides the meta information that the data collection program could access. The only issue I see with this approach is the front end is going to have to match the meta data with the collected data, which would be more efficient as a join on the back end. Currently, this system is slated to run on .NET and MSSQL. I haven't played around with .NET libraries running in SQL, but I'm considering trying to create logic that would pull from the web service, convert the meta data into a table that SQL can join on, and return the combined data and meta data that way. Is this solution the wrong way to approach the problem? Is there a pattern or "industry standard" way of bringing together two datasets that don't live in the same database?

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  • The Business case for Big Data

    - by jasonw
    The Business Case for Big Data Part 1 What's the Big Deal Okay, so a new buzz word is emerging. It's gone beyond just a buzzword now, and I think it is going to change the landscape of retail, financial services, healthcare....everything. Let me spend a moment to talk about what i'm going to talk about. Massive amounts of data are being collected every second, more than ever imaginable, and the size of this data is more than can be practically managed by today’s current strategies and technologies. There is a revolution at hand centering on this groundswell of data and it will change how we execute our businesses through greater efficiencies, new revenue discovery and even enable innovation. It is the revolution of Big Data. This is more than just a new buzzword is being tossed around technology circles.This blog series for Big Data will explain this new wave of technology and provide a roadmap for businesses to take advantage of this growing trend. Cases for Big Data There is a growing list of use cases for big data. We naturally think of Marketing as the low hanging fruit. Many projects look to analyze twitter feeds to find new ways to do marketing. I think of a great example from a TED speech that I recently saw on data visualization from Facebook from my masters studies at University of Virginia. We can see when the most likely time for breaks-ups occurs by looking at status changes and updates on users Walls. This is the intersection of Big Data, Analytics and traditional structured data. Ted Video Marketers can use this to sell more stuff. I really like the following piece on looking at twitter feeds to measure mood. The following company was bought by a hedge fund. They could predict how the S&P was going to do within three days at an 85% accuracy. Link to the article Here we see a convergence of predictive analytics and Big Data. So, we'll look at a lot of these business cases and start talking about what this means for the business. It's more than just finding ways to use Hadoop + NoSql and we'll talk about that too. How do I start in Big Data? That's what is coming next post.

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  • Big Data – Buzz Words: What is NoSQL – Day 5 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored the basic architecture of Big Data . In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – NoSQL. What is NoSQL? NoSQL stands for Not Relational SQL or Not Only SQL. Lots of people think that NoSQL means there is No SQL, which is not true – they both sound same but the meaning is totally different. NoSQL does use SQL but it uses more than SQL to achieve its goal. As per Wikipedia’s NoSQL Database Definition – “A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases.“ Why use NoSQL? A traditional relation database usually deals with predictable structured data. Whereas as the world has moved forward with unstructured data we often see the limitations of the traditional relational database in dealing with them. For example, nowadays we have data in format of SMS, wave files, photos and video format. It is a bit difficult to manage them by using a traditional relational database. I often see people using BLOB filed to store such a data. BLOB can store the data but when we have to retrieve them or even process them the same BLOB is extremely slow in processing the unstructured data. A NoSQL database is the type of database that can handle unstructured, unorganized and unpredictable data that our business needs it. Along with the support to unstructured data, the other advantage of NoSQL Database is high performance and high availability. Eventual Consistency Additionally to note that NoSQL Database may not provided 100% ACID (Atomicity, Consistency, Isolation, Durability) compliance.  Though, NoSQL Database does not support ACID they provide eventual consistency. That means over the long period of time all updates can be expected to propagate eventually through the system and data will be consistent. Taxonomy Taxonomy is the practice of classification of things or concepts and the principles. The NoSQL taxonomy supports column store, document store, key-value stores, and graph databases. We will discuss the taxonomy in detail in later blog posts. Here are few of the examples of the each of the No SQL Category. Column: Hbase, Cassandra, Accumulo Document: MongoDB, Couchbase, Raven Key-value : Dynamo, Riak, Azure, Redis, Cache, GT.m Graph: Neo4J, Allegro, Virtuoso, Bigdata As of now there are over 150 NoSQL Database and you can read everything about them in this single link. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • BAM Data Control in multiple ADF Faces Components

    - by [email protected]
    As we know Oracle BAM data control instance sharing is not supported.When two or more ADF Faces components must display the same data, and are bound to the same Oracle BAM data control definition, we have to make sure that we wrap each ADF Faces component in an ADF task flow, and set the Data Control Scope to isolated. This blog will show a small sample to demonstrate this. In this sample we will create a Pie and Bar using same BAM DC, such that both components use same Data control but have isolated scope.This sample can be downloaded  fromSample1.zip Set-up: Create a BAM data control using employees DO (sample) Steps: Right click on View Controller project and select "New->ADF Task Flow" Check "Create Bounded Task Flow" and give some meaningful name (ex:EmpPieTF.xml ) to the TaskFlow(TF) and click on "OK"CreateTF.bmpFrom the "Components Palette", drag and drop "View" into the task flow diagram. Give a meaningful name to the view. Double Click and Click "Ok" for  "Create New JSF Page Fragment" From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Pie" (Pie: Sales_Number and Slices: Salesperson) Repeat step 1 through 4 for another Task Flow (ex: EmpBarTF). From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Bar" (Bars :Sales_Number and X-axis : Salesperson). Open the Taskflow created in step 2. In the Structure Pane, right click on "Task Flow Definition -EmpPieTF" Click "Insert inside Task Flow Definition - EmpPieTF -> ADF Task Flow -> Data Control Scope". Click "OK"TFDCScope.bmpFor the "Data Control Scope", In the Property Inspector ->General section, change data control scope from Shared to Isolated. Repeat step 8 through 11 for the 2nd Task flow created. Now create a new jspx page example: Main.jspxDrag and drop both the Task flows (ex: "EmpPieTF" and "EmpBarTF") as regions. Surround with panel components as needed.Run the page Main.jspxMainPage.bmpNow when the page runs although both components are created using same Data control the bindings are not shared and each component will have a separate instance of the data control.

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  • Linked Measure Groups and Local Dimensions

    - by ekoner
    Mulling over something I've been reading up on. According to Chris Webb, A linked measure group can only be used with dimensions from the same database as the source measure group. So I took this to mean as long as two cubes share a database, a linked measure group can be used with a dimension. So I created a new cube and added a local measure group, a local dimension and a linked measure group. However, I can't create a relationship between the linked measure group and the local dimension even though they are within the same database. I get the message below: Regular relationships in the current database between non-linked (local) dimensions and linked measure groups cannot be edited. These relationship can only be created through the wizard. This dialog can be used to delete these relationships. I see that I can go to the original cube and add the dimension there, but does the message below mean I have an alternative? I just know it's going to be something simple and trivial! Thanks for reading.

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