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  • Rails model belongs to model that belongs to model but i want to use another name

    - by Micke
    Hello. This may be a stupid question but im just starting to learn Rail thats why i am asking thsi question. I have one model called "User" which handles all the users in my community. Now i want to add a guestbook to every user. So i created a model called "user_guestbook" and inserted this into the new model: belongs_to :user and this into the user model: has_one :user_guestbook, :as => :guestbook The next thing i did was to add a new model to handle the posts inside the guestbook. I named it "guestbook_posts" and added this code into the new model: belongs_to :user_guestbook And this into the user_guestbook model: has_many :guestbook_posts, :as => :posts What i wanted to achive was to be able to fetch all the posts to a certain user by: @user = User.find(1) puts @user.guestbook.posts But it doesnt work for me. I dont know what i am doing wrong and if there is any easier way to do this please tell me so. Just to note, i have created some migrations for it to as follows: create_user_guestbook: t.integer :user_id create_guestbook_posts: t.integer :guestbook_id t.integer :from_user t.string :post Thanks in advance!

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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. 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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  • strange data annotations issue in MVC 2

    - by femi
    Hello, I came across something strange when creating an edit form with MVC 2. i realised that my error messages come up on form sumission even when i have filled ut valid data! i am using a buddy class which i have configured correctly ( i know that cos i can see my custom errors). Here is the code from the viewmodel that generates this; <%@ Control Language="C#" Inherits="System.Web.Mvc.ViewUserControl<TG_Careers.Models.Applicant>" %> <script src="/Scripts/MicrosoftAjax.js" type="text/javascript"></script> <script src="/Scripts/MicrosoftMvcAjax.js" type="text/javascript"></script> <script src="/Scripts/MicrosoftMvcValidation.js" type="text/javascript"></script> <%= Html.ValidationSummary() %> <% Html.EnableClientValidation(); %> <% using (Html.BeginForm()) {%> <div class="confirm-module"> <table cellpadding="4" cellspacing="2"> <tr> <td><%= Html.LabelFor(model => model.FirstName) %> </td> <td><%= Html.EditorFor(model => model.FirstName) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.FirstName) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.MiddleName) %></td> <td><%= Html.EditorFor(model => model.MiddleName) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.MiddleName) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.LastName) %></td> <td><%= Html.EditorFor(model => model.LastName) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.LastName) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.Gender) %></td> <td><%= Html.EditorFor(model => model.Gender) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.Gender) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.MaritalStatus) %></td> <td> <%= Html.EditorFor(model => model.MaritalStatus) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.MaritalStatus) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.DateOfBirth) %></td> <td><%= Html.EditorFor(model => model.DateOfBirth) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.DateOfBirth) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.Address) %></td> <td><%= Html.EditorFor(model => model.Address) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.Address) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.City) %></td> <td><%= Html.EditorFor(model => model.City) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.City) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.State) %></td> <td><%= Html.EditorFor(model => model.State) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.State) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.StateOfOriginID) %></td> <td><%= Html.DropDownList("StateOfOriginID", new SelectList(ViewData["States"] as IEnumerable, "StateID", "Name", Model.StateOfOriginID))%></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.StateOfOriginID) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.CompletedNYSC) %></td> <td><%= Html.EditorFor(model => model.CompletedNYSC) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.CompletedNYSC) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.YearsOfExperience) %></td> <td><%= Html.EditorFor(model => model.YearsOfExperience) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.YearsOfExperience) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.MobilePhone) %></td> <td><%= Html.EditorFor(model => model.MobilePhone) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.MobilePhone) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.DayPhone) %></td> <td> <%= Html.EditorFor(model => model.DayPhone) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.DayPhone) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.CVFileName) %></td> <td><%= Html.EditorFor(model => model.CVFileName) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.CVFileName) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.CurrentPosition) %></td> <td><%= Html.EditorFor(model => model.CurrentPosition) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.CurrentPosition) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.EmploymentCommenced) %></td> <td><%= Html.EditorFor(model => model.EmploymentCommenced) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.EmploymentCommenced) %></td> </tr> <tr> <td><%= Html.LabelFor(model => model.DateofTakingupCurrentPosition) %></td> <td><%= Html.EditorFor(model => model.DateofTakingupCurrentPosition) %></td> </tr> <tr> <td colspan="2"><%= Html.ValidationMessageFor(model => model.DateofTakingupCurrentPosition) %></td> </tr> <tr> <td>&nbsp;</td> <td>&nbsp;</td> </tr> <tr> <td colspan="2">&nbsp;</td> </tr> </table> <p> <input type="submit" value="Save Profile Details" /> </p> </div> <% } %> Any ideas on this one please? Thanks

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  • Use Oracle Product Hub Business Events to Integrate Additional Logic into Your Business Flows

    - by ToddAC-Oracle
    Business events provide a mechanism to plug-in and integrate some additional business processes or custom code into standard business flows.  You could send a notification to a business User, write to advanced queues or perform some custom processes. In-built business events are available specifically for each flow like Item Creation, Item Updation, User-Defined Attribute Changes, Change Order Creation, Change Order Status Changes and others.To get a list of business events, refer to the PIM implementation Guide or Using Business Events in PLM and PIM Data Librarian (Doc ID 372814.1) .If you are planning to use business events, Doc ID 1074754.1 walks you through a setup with examples. How to Subscribe and Use Product Hub (PIM / APC) Business Events [Video] ? (Doc ID 1074754.1). Review the 'Presentation' section of Doc ID 1074754.1 for complete information and best practices to follow while implementing code for subscriptions. Learn things you might want to avoid, like commit statements for instance. Doc ID 1074754.1 also provides sample code for testing, and can be used to troubleshoot missing setups or frequently experienced issues. Take advantage and run a test ahead of time with the sample code to isolate any issues from within business specific subscription code.Get more out of Oracle Product Hub by using Business Events!

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  • Why model => model.Reason_ID turns to model =>Convert(model.Reason_ID)

    - by er-v
    I have my own html helper extension, wich I use this way <%=Html.LocalizableLabelFor(model => model.Reason_ID, Register.PurchaseReason) %> which declared like this. public static MvcHtmlString LocalizableLabelFor<T>(this HtmlHelper<T> helper, Expression<Func<T, object>> expr, string captionValue) where T : class { return helper.LocalizableLabelFor(ExpressionHelper.GetExpressionText(expr), captionValue); } but when I open it in debugger expr.Body.ToString() will show me Convert(model.Reason_ID). But should model.Reason_ID. That's a big problem, becouse ExpressionHelper.GetExpressionText(expr) returns empty string. What a strange magic is that? How can I get rid of it?

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  • Unstructured Data - The future of Data Administration

    Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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  • SQL Server Management Data Warehouse - quick tour on setting health monitoring policies

    - by ssqa.net
    Profiler, Perfmon, DMVs & scripts are legendary tools for a DBA to monitor the SQL arena. In line with these tools SQL Server 2008 throws a powerful stream with policy based management (PBM) framework & management data warehouse (MDW) methods, which is a relational database that contains the data that is collected from a server that is a data collection target. This data is used to generate the reports for the System Data collection sets, and can also be used to create custom reports. .....(read more)

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  • Data Warehouse Workshop

    - by Davide Mauri
    I’m really really pleased to announce that it’s possible to register to the Data Warehouse Workshop that I and Thomas Kejser developed togheter.  Several months ago we decided to join forces in order to create a workshop that would contain not only the theoretical stuff, but also the experience we both have and all the best practices and lesson learned that can make the difference between a success and a failure when building a Data Warehouse. The first sheduled date is 7 February in Kista (Sweden): http://www.eventzilla.net/web/event?eventid=2138965081 and until 30th November there is the Super Early Bird to save more the 100€ (150$). The workshop will be very similar to the one I delivered at PASS Summit summit, with some extra technical stuff since it’s one hour longer. In addition to that for this first version both me and Thomas will be present, so it’s a great change  to make sure you super-charge your DW/BI project with insights that aren’t available anywhere else! If you’re into the BI field and you live in Europe, don’t miss this opportunity!

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  • Big Data – Beginning Big Data – Day 1 of 21

    - by Pinal Dave
    What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • American Modern Insurance Group recognized at 2010 INN VIP Best Practices Awards

    - by [email protected]
    Below: Helen Pitts (right), Oracle Insurance, congratulates Bruce Weisgerber, Munich Re, as he accepts a VIP Best Practices Award on behalf of American Modern Insurance Group.     Oracle Insurance Senior Product Marketing Manager Helen Pitts is attending the 2010 ACORD LOMA Insurance Forum this week at the Mandalay Bay Resort in Las Vegas, Nevada, and will be providing updates from the show floor. This is one of my favorite seasons of the year--insurance trade show season. It is a time to reconnect with peers, visit with partners, make new industry connections, and celebrate our customers' achievements. It's especially meaningful when we can share the experience of having one of our Oracle Insurance customers recognized for being an innovator in its business and in the industry. Congratulations to American Modern Insurance Group, part of the Munich Re Group. American Modern earned an Insurance Networking News (INN) 2010 VIP Best Practice Award yesterday evening during the 2010 ACORD LOMA Insurance Forum. The award recognizes an insurer's best practice for use of a specific technology and the role, if feasible, that ACORD data standards played as a part of their business and technology. American Modern received an Honorable Mention for leveraging the Oracle Documaker enterprise document automation solution to: Improve the quality of communications with customers in high value, high-touch lines of business Convert thousands of page elements or "forms" from their previous system, with near pixel-perfect accuracy Increase efficiency and reusability by storing all document elements (fonts, logos, approved wording, etc.) in one place Issue on-demand documents, such as address changes or policy transactions to multiple recipients at once Consolidate all customer communications onto a single platform Gain the ability to send documents to multiple recipients at once, further improving efficiency Empower agents to produce documents in real time via the Web, such as quotes, applications and policy documents, improving carrier-agent relationships Munich Re's Bruce Weisgerber accepted the award on behalf of American Modern from Lloyd Chumbly, vice president of standards at ACORD. In a press release issued after the ceremony Chumbly noted, "This award embodies a philosophy of efficiency--working smarter with standards, these insurers represent the 'best of the best' as chosen by a body of seasoned insurance industry professionals." We couldn't agree with you more, Lloyd. Congratulations again to American Modern on your continued innovation and success. You're definitely a VIP in our book! To learn more about how American Modern is putting its enterprise document automation strategy into practice, click here to read a case study. Helen Pitts is senior product marketing manager for Oracle Insurance.

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  • Reading data from an Entity Framework data model through a WCF Data Service

    - by nikolaosk
    This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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  • Big Data&rsquo;s Killer App&hellip;

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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  • SQL SERVER – Log File Growing for Model Database – model Database Log File Grew Too Big

    - by pinaldave
    After reading my earlier article SQL SERVER – master Database Log File Grew Too Big, I received an email recently from another reader asking why does the log file of model database grow every day when he is not carrying out any operation in the model database. As per the email, he is absolutely sure that he is doing nothing on his model database; he had used policy management to catch any T-SQL operation in the model database and there were none. This was indeed surprising to me. I sent a request to access to his server, which he happily agreed for and within a min, we figured out the issue. He was taking the backup of the model database every day taking the database backup every night. When I explained the same to him, he did not believe it; so I quickly wrote down the following script. The results before and after the usage of the script were very clear. What is a model database? The model database is used as the template for all databases created on an instance of SQL Server. Any object you create in the model database will be automatically created in subsequent user database created on the server. NOTE: Do not run this in production environment. During the demo, the model database was in full recovery mode and only full backup operation was performed (no log backup). Before Backup Script Backup Script in loop DECLARE @FLAG INT SET @FLAG = 1 WHILE(@FLAG < 1000) BEGIN BACKUP DATABASE [model] TO  DISK = N'D:\model.bak' SET @FLAG = @FLAG + 1 END GO After Backup Script Why did this happen? The model database was in full recovery mode and taking full backup is logged operation. As there was no log backup and only full backup was performed on the model database, the size of the log file kept growing. Resolution: Change the backup mode of model database from “Full Recovery” to “Simple Recovery.”. Take full backup of the model database “only” when you change something in the model database. Let me know if you have encountered a situation like this? If so, how did you resolve it? It will be interesting to know about your experience. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Accelerate your SOA with Data Integration - Live Webinar Tuesday!

    - by dain.hansen
    Need to put wind in your SOA sails? Organizations are turning more and more to Real-time data integration to complement their Service Oriented Architecture. The benefit? Lowering costs through consolidating legacy systems, reducing risk of bad data polluting their applications, and shortening the time to deliver new service offerings. Join us on Tuesday April 13th, 11AM PST for our live webinar on the value of combining SOA and Data Integration together. In this webcast you'll learn how to innovate across your applications swiftly and at a lower cost using Oracle Data Integration technologies: Oracle Data Integrator Enterprise Edition, Oracle GoldenGate, and Oracle Data Quality. You'll also hear: Best practices for building re-usable data services that are high performing and scalable across the enterprise How real-time data integration can maximize SOA returns while providing continuous availability for your mission critical applications Architectural approaches to speed service implementation and delivery times, with pre-integrations to CRM, ERP, BI, and other packaged applications Register now for this live webinar!

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  • Class Design for special business rules

    - by Samuel Front
    I'm developing an application that allows people to place custom manufacturing orders. However, while most require similar paperwork, some of them have custom paperwork that only they require. My current class design has a Manufacturer class, of which of one of the member variables is an array of RequiredSubmission objects. However, there are two issues that I am somewhat concerned about. First, some manufacturers are willing to accept either a standard form or their own custom form. I'm thinking of storing this in the RequiredSubmission object, with an array of alternate forms that are a valid substitute. I'm not sure that this is ideal, however. The major issue, however, is that some manufacturers have deadline cycles. For example, forms A, B and C have to be delivered by January 1, while payment must be rendered by January 10. If you miss those, you'll have to wait until the next cycle. I'm not exactly sure how I can get this to work with my existing classes—how can I say "this set of dates all belong to the same cycle, with date A for form A, date B for form B, etc." I would greatly appreciate any insights on how to best design these classes.

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

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

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  • How to use the client object model with SharePoint2010

    - by ybbest
    In SharePoint2010, you can use client object model to communicate with SharePoint server. Today, I’d like to show you how to achieve this by using the c# console application. You can download the solution here. 1. Create a Console application in visual studio and add the following references to the project. 2. Insert your code as below ClientContext context = new ClientContext("http://demo2010a"); Web currentWeb = context.Web; context.Load(currentWeb, web =&gt; web.Title); context.ExecuteQuery(); Console.WriteLine(currentWeb.Title); Console.ReadLine(); 3. Run your code then you will get the web title displayed as shown below Note: If you got the following errors, you need to change your target framework from .Net Framework 4 client profile to .Net Framework 4 as shown below: Change from TO

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  • Announcing General Availability of the E-Business Suite Plug-in

    - by Kenneth E.
    Oracle E-Business Suite Application Technology Group (ATG) is pleased to announce the General Availability of Oracle E-Business Suite Plug-in 12.1.0.1.0, an integral part of Application Management Suite for Oracle E-Business Suite.The combination of Enterprise Manager 12c Cloud Control and the Application Management Suite combines functionality that was available in the standalone Application Management Pack for Oracle E-Business Suite and Application Change Management Pack for Oracle E-Business Suite with Oracle’s Real User Experience Insight product and the Configuration & Compliance capabilities to provide the most complete solution for managing Oracle E-Business Suite applications. The features that were available in the standalone management packs are now packaged into the Oracle E-Business Suite Plug-in, which is now fully certified with Oracle Enterprise Manager 12c Cloud Control. This latest plug-in extends Cloud Control with E-Business Suite specific system management capabilities and features enhanced change management support.Here is all the information you need to get started:EBS Plug-in 12.1.0.1.0 info -Full Announcement•    E-Business Suite Plug-in 12.1.0.1 for Enterprise Manager 12c Now Available MOS -•    Getting Started with Oracle E-Business Suite Plug-in, Release 12.1.0.1.0 (Doc ID 1434392.1)Documentation -•    Oracle Application Management Pack for Oracle E-Business Suite Guide, Release 12.1.0.1.0Certification•    Platforms and OS Release certification information is available from My Oracle Support via the Certification page. •    Search using the official trademark name Oracle Application Management Pack for Oracle E-Business Suite and Release 12.1.0.1.0

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  • How much system and business analysis should a programmer be reasonably expected to do?

    - by Rahul
    In most places I have worked for, there were no formal System or Business Analysts and the programmers were expected to perform both the roles. One had to understand all the subsystems and their interdependencies inside out. Further, one was also supposed to have a thorough knowledge of the business logic of the applications and interact directly with the users to gather requirements, answer their queries etc. In my current job, for ex, I spend about 70% time doing system analysis and only 30% time programming. I consider myself a good programmer but struggle with developing a good understanding of the business rules of a complex application. Often, this creates a handicap because while I can write efficient algorithms and thread-safe code, I lose out to guys who may be average programmers but have a much better understanding of the business processes. So I want to know - How much business and systems knowledge should a programmer have ? - How does one go about getting this knowledge in an immensely complex software system (e.g. trading applications) with several interdependent business processes but poorly documented business rules.

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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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  • Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

    - by Pinal Dave
    Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Creating an Entity Data Model using the Model First approach

    - by nikolaosk
    This is going to be the second post of a series of posts regarding Entity Framework and how we can use Entity Framework version 4.0 new features. You can read the first post here . In order to follow along you must have some knowledge of C# and know what an ORM system is and what kind of problems Entity Framework addresses.It will be handy to know how to work inside the Visual Studio 2010 IDE . I have a post regarding ASP.Net and EntityDataSource . You can read it here .I have 3 more posts on Profiling...(read more)

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  • A New Home for E-Business Suite Customer Adoption Information

    - by linda.fishman.hoyle
    Phew! I made it! A new home with my name. Let's talk about E-Business Suite. So much is going on and more and more customers are upgrading and implementing the latest release. I think I will highlight in this blog entry the most recent press release we issued 2 weeks ago about our Applications Unlimited success but in the release, we name several customers who are live on E-Business Suite Release 12.1 and then have a fabulous quote from a customer who is doing great things with our product.   Here is a link to the press release To make it easy for you, I am pulling out just the E-Business Suite information Oracle E-Business Suite: Oracle® E-Business Suite Release 12.1 provides organizations of all sizes, across all industries and regions, with a global business foundation that helps them reduce costs and increase productivity through a portfolio of rapid value solutions, integrated business processes and industry-focused solutions. The latest version of the Oracle E-Business Suite was designed to help organizations make better decisions and be more competitive by providing a global or holistic view of their operations. Abu Dhabi Media Company, Agilysis, C3 Business Solutions, Chicago Public Schools, Datacard Group, Guidance Software, Leviton Manufacturing, McDonald's, MINOR International, Usana Health Sciences, Zamil Plastic Industries Ltd. and Zebra Technologies are just a few of the organizations that have deployed the latest release of the Oracle E-Business Suite to help them make better decisions and be more competitive, while lowering costs and increasing performance. Customer Speaks "Leviton Manufacturing makes a very diverse line of products including electrical devices and data center products that we sell globally. We upgraded to the latest version of the Oracle E-Business Suite Release 12.1 to support our service business with change management, purchasing, accounts payable, and our internal IT help desk," said Bob MacTaggart, CIO of Leviton Manufacturing. "We consolidated seven Web sites that we used to host individually onto iStore. In addition, we run a site, using the Oracle E-Business Suite configurator, pricing and quoting for our sales agents to do configuration work. This site can now generate a complete sales proposal using Oracle functionality; we actually generate CAD drawings - the actual drawings themselves - based on configuration results. It used to take six to eight weeks to generate these drawings and now it's all done online in an hour to two hours by our sales agents themselves, totally self-service. It does everything they need. From our point of view that is a major business success. Not only is it a very cool, innovative application, but it also puts us about two years ahead of our competition."

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  • Given the presentation model pattern, is the view, presentation model, or model responsible for adding child views to an existing view at runtime?

    - by Ryan Taylor
    I am building a Flex 4 based application using the presentation model design pattern. This application will have several different components to it as shown in the image below. The MainView and DashboardView will always be visible and they each have corresponding presentation models and models as necessary. These views are easily created by declaring their MXML in the application root. <s:HGroup width="100%" height="100%"> <MainView width="75% height="100%"/> <DashboardView width="25%" height="100%"/> </s:HGroup> There will also be many WidgetViewN views that can be added to the DashboardView by the user at runtime through a simple drop down list. This will need to be accomplished via ActionScript. The drop down list should always show what WidgetViewN has already been added to the DashboardView. Therefore some state about which WidgetViewN's have been created needs to be stored. Since the list of available WidgetViewN and which ones are added to the DashboardView also need to be accessible from other components in the system I think this needs to be stored in a Model object. My understanding of the presentation model design pattern is that the view is very lean. It contains as close to zero logic as is practical. The view communicates/binds to the presentation model which contains all the necessary view logic. The presentation model is effectively an abstract representation of the view which supports low coupling and eases testability. The presentation model may have one or more models injected in in order to display the necessary information. The models themselves contain no view logic whatsoever. So I have a several questions around this design. Who should be responsible for creating the WidgetViewN components and adding these to the DashboardView? Is this the responsibility of the DashboardView, DashboardPresentationModel, DashboardModel or something else entirely? It seems like the DashboardPresentationModel would be responsible for creating/adding/removing any child views from it's display but how do you do this without passing in the DashboardView to the DashboardPresentationModel? The list of available and visible WidgetViewN components needs to be accessible to a few other components as well. Is it okay for a reference to a WidgetViewN to be stored/referenced in a model? Are there any good examples of the presentation model pattern online in Flex that also include creating child views at runtime?

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  • Fraud Detection with the SQL Server Suite Part 2

    - by Dejan Sarka
    This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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