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  • Stateless game design

    - by L. De Leo
    I'm facing a challenge understanding how to program a web version of a card game that is completely stateless. I create my object graph when the game begins and distribute cards to PlayerA and PlayerB so I lay them out on the screen. At this point I could assume that HTML and the querystring is what holds at least some of my state and just keep a snapshot copy of the game state on the server-side for the sole purpose of validating the inputs I receive from the web clients. Still it appears to me that the state of the game is by its nature mutable: cards are being dealt from the deck, etc... Am I just not getting it? Or should I just strive to minimize the side-effects of my functions to the objects that I take as my input? How would you design a stateless card game?

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  • Where ORMs blur the lines between code and data, how do you decide what logic should be a stored procedure, and what should be coded?

    - by PhonicUK
    Take the following pseudocode: CreateInvoiceAndCalculate(ItemsAndQuantities, DispatchAddress, User); And say CreateInvoice does the following: Create a new entry in an Invoices table belonging to the specified User to be sent to the given DispatchAddress. Create a new entry in an InvoiceItems table for each of the items in ItemsAndQuantities, storing the Item, the Quantity, and the cost of the item as of now (by looking it up from an Items table) Calculate the total amount of the invoice (ex shipping and taxes) and store it in the new Invoice row. At a glace you wouldn't be able to tell if this was a method in my applications code, or a stored procedure in the database that is being exposed as a function by the ORM. And to some extent it doesn't really matter. Now technically none of this is business logic. You're not making any decisions - just performing a calculation and creating records. However some may argue that because you are performing a calculation that affects the business (the total amount to be invoiced) that this isn't something that should be done in a stored procedure and instead should be in code. So for this specific example - why would it be more appropriate to do one or the other? And where do you draw the line? Or does it even particular matter as long as it's sufficiently well documented?

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  • Guidelines or Design pattern to develop configurable software

    - by Rumit Parakhiya
    I want to develop an application which would have it's own framework, using which developer can implement functionality very easily. Developer shouldn't have to code for each and every page or report. But, he can do it very easily by just configuring it using the framework provided. Some nice examples of it is SAP and Tally. They have got their own framework, using which anybody having knowledge of it can customize or extend functionality of the product. But, as I am beginner in this direction, I don't have any idea about where to start. Can anybody point me to some design pattern which I can follow or some similar open source software which I can refer?

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  • How can I design multi-threaded application for larger user base

    - by rokonoid
    Here's my scenario, I need to develop a scalable application. My user base may be over 100K, every user has 10 or more small tasks. Tasks check every minute with a third party application through web services, retrieving data which is written in the database, then the data is forwarded to it's original destination. So here's the processing of a small task: while(true){ Boolean isNewInformationAvailable = checkWhetherInformationIsAvailableOrNot(); If(isNewInformationAvailable ==true){ fetchTheData(); writeToDatabase(); findTheDestination(); deliverTheData(); isAvailable =false; } } As the application is large, how should I approach designing this? I'm going to use Java to write it. Should I use concurrency, and how would you manage the concurrency?

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  • MVC Design Pattern to Combine Multiple Models for use

    - by roverred
    In my design, I have multiple models and each model has a controller. I need to use all the models to process some operation. Most examples I see are pretty simple with 1 view, 1 controller, and 1 model. How would you get all these models together? Only ways I can think of are 1) Have a top-level controller which has a reference to every controller. Those controllers will have a getter/setter function for their model. Does this violate MVC because every controller should have a model? 2) Have an Intermediate class to combine every model into a one model. Then you create a controller for that new super model. Do you know of any better ideas? Thanks.

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  • design for interruptable operations

    - by tpaksu
    I couldn't find a better topic but here it is; 1) When user clicks a button, code starts t work, 2) When another button is clicked, it would stop doing whatever it does and start to run the second button's code, 3) Or with not user interaction, an electrical power down detected from a connected device, so our software would cancel the current event and start doing the power down procedure. How is this design mostly applied to code? I mean "stop what you are doing" part? If you would say events, event handlers etc. how do you bind a condition to the event? and how do you tell the program without using laddered if's to end it's process? method1(); if (powerdown) return; method2(); if (powerdown) return; etc.

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  • Abstracting functionality

    - by Ralf Westphal
    Originally posted on: http://geekswithblogs.net/theArchitectsNapkin/archive/2014/08/22/abstracting-functionality.aspxWhat is more important than data? Functionality. Yes, I strongly believe we should switch to a functionality over data mindset in programming. Or actually switch back to it. Focus on functionality Functionality once was at the core of software development. Back when algorithms were the first thing you heard about in CS classes. Sure, data structures, too, were important - but always from the point of view of algorithms. (Niklaus Wirth gave one of his books the title “Algorithms + Data Structures” instead of “Data Structures + Algorithms” for a reason.) The reason for the focus on functionality? Firstly, because software was and is about doing stuff. Secondly because sufficient performance was hard to achieve, and only thirdly memory efficiency. But then hardware became more powerful. That gave rise to a new mindset: object orientation. And with it functionality was devalued. Data took over its place as the most important aspect. Now discussions revolved around structures motivated by data relationships. (John Beidler gave his book the title “Data Structures and Algorithms: An Object Oriented Approach” instead of the other way around for a reason.) Sure, this data could be embellished with functionality. But nevertheless functionality was second. When you look at (domain) object models what you mostly find is (domain) data object models. The common object oriented approach is: data aka structure over functionality. This is true even for the most modern modeling approaches like Domain Driven Design. Look at the literature and what you find is recommendations on how to get data structures right: aggregates, entities, value objects. I´m not saying this is what object orientation was invented for. But I´m saying that´s what I happen to see across many teams now some 25 years after object orientation became mainstream through C++, Delphi, and Java. But why should we switch back? Because software development cannot become truly agile with a data focus. The reason for that lies in what customers need first: functionality, behavior, operations. To be clear, that´s not why software is built. The purpose of software is to be more efficient than the alternative. Money mainly is spent to get a certain level of quality (e.g. performance, scalability, security etc.). But without functionality being present, there is nothing to work on the quality of. What customers want is functionality of a certain quality. ASAP. And tomorrow new functionality needs to be added, existing functionality needs to be changed, and quality needs to be increased. No customer ever wanted data or structures. Of course data should be processed. Data is there, data gets generated, transformed, stored. But how the data is structured for this to happen efficiently is of no concern to the customer. Ask a customer (or user) whether she likes the data structured this way or that way. She´ll say, “I don´t care.” But ask a customer (or user) whether he likes the functionality and its quality this way or that way. He´ll say, “I like it” (or “I don´t like it”). Build software incrementally From this very natural focus of customers and users on functionality and its quality follows we should develop software incrementally. That´s what Agility is about. Deliver small increments quickly and often to get frequent feedback. That way less waste is produced, and learning can take place much easier (on the side of the customer as well as on the side of developers). An increment is some added functionality or quality of functionality.[1] So as it turns out, Agility is about functionality over whatever. But software developers’ thinking is still stuck in the object oriented mindset of whatever over functionality. Bummer. I guess that (at least partly) explains why Agility always hits a glass ceiling in projects. It´s a clash of mindsets, of cultures. Driving software development by demanding small increases in functionality runs against thinking about software as growing (data) structures sprinkled with functionality. (Excuse me, if this sounds a bit broad-brush. But you get my point.) The need for abstraction In the end there need to be data structures. Of course. Small and large ones. The phrase functionality over data does not deny that. It´s not functionality instead of data or something. It´s just over, i.e. functionality should be thought of first. It´s a tad more important. It´s what the customer wants. That´s why we need a way to design functionality. Small and large. We need to be able to think about functionality before implementing it. We need to be able to reason about it among team members. We need to be able to communicate our mental models of functionality not just by speaking about them, but also on paper. Otherwise reasoning about it does not scale. We learned thinking about functionality in the small using flow charts, Nassi-Shneiderman diagrams, pseudo code, or UML sequence diagrams. That´s nice and well. But it does not scale. You can use these tools to describe manageable algorithms. But it does not work for the functionality triggered by pressing the “1-Click Order” on an amazon product page for example. There are several reasons for that, I´d say. Firstly, the level of abstraction over code is negligible. It´s essentially non-existent. Drawing a flow chart or writing pseudo code or writing actual code is very, very much alike. All these tools are about control flow like code is.[2] In addition all tools are computationally complete. They are about logic which is expressions and especially control statements. Whatever you code in Java you can fully (!) describe using a flow chart. And then there is no data. They are about control flow and leave out the data altogether. Thus data mostly is assumed to be global. That´s shooting yourself in the foot, as I hope you agree. Even if it´s functionality over data that does not mean “don´t think about data”. Right to the contrary! Functionality only makes sense with regard to data. So data needs to be in the picture right from the start - but it must not dominate the thinking. The above tools fail on this. Bottom line: So far we´re unable to reason in a scalable and abstract manner about functionality. That´s why programmers are so driven to start coding once they are presented with a problem. Programming languages are the only tool they´ve learned to use to reason about functional solutions. Or, well, there might be exceptions. Mathematical notation and SQL may have come to your mind already. Indeed they are tools on a higher level of abstraction than flow charts etc. That´s because they are declarative and not computationally complete. They leave out details - in order to deliver higher efficiency in devising overall solutions. We can easily reason about functionality using mathematics and SQL. That´s great. Except for that they are domain specific languages. They are not general purpose. (And they don´t scale either, I´d say.) Bummer. So to be more precise we need a scalable general purpose tool on a higher than code level of abstraction not neglecting data. Enter: Flow Design. Abstracting functionality using data flows I believe the solution to the problem of abstracting functionality lies in switching from control flow to data flow. Data flow very naturally is not about logic details anymore. There are no expressions and no control statements anymore. There are not even statements anymore. Data flow is declarative by nature. With data flow we get rid of all the limiting traits of former approaches to modeling functionality. In addition, nomen est omen, data flows include data in the functionality picture. With data flows, data is visibly flowing from processing step to processing step. Control is not flowing. Control is wherever it´s needed to process data coming in. That´s a crucial difference and needs some rewiring in your head to be fully appreciated.[2] Since data flows are declarative they are not the right tool to describe algorithms, though, I´d say. With them you don´t design functionality on a low level. During design data flow processing steps are black boxes. They get fleshed out during coding. Data flow design thus is more coarse grained than flow chart design. It starts on a higher level of abstraction - but then is not limited. By nesting data flows indefinitely you can design functionality of any size, without losing sight of your data. Data flows scale very well during design. They can be used on any level of granularity. And they can easily be depicted. Communicating designs using data flows is easy and scales well, too. The result of functional design using data flows is not algorithms (too low level), but processes. Think of data flows as descriptions of industrial production lines. Data as material runs through a number of processing steps to be analyzed, enhances, transformed. On the top level of a data flow design might be just one processing step, e.g. “execute 1-click order”. But below that are arbitrary levels of flows with smaller and smaller steps. That´s not layering as in “layered architecture”, though. Rather it´s a stratified design à la Abelson/Sussman. Refining data flows is not your grandpa´s functional decomposition. That was rooted in control flows. Refining data flows does not suffer from the limits of functional decomposition against which object orientation was supposed to be an antidote. Summary I´ve been working exclusively with data flows for functional design for the past 4 years. It has changed my life as a programmer. What once was difficult is now easy. And, no, I´m not using Clojure or F#. And I´m not a async/parallel execution buff. Designing the functionality of increments using data flows works great with teams. It produces design documentation which can easily be translated into code - in which then the smallest data flow processing steps have to be fleshed out - which is comparatively easy. Using a systematic translation approach code can mirror the data flow design. That way later on the design can easily be reproduced from the code if need be. And finally, data flow designs play well with object orientation. They are a great starting point for class design. But that´s a story for another day. To me data flow design simply is one of the missing links of systematic lightweight software design. There are also other artifacts software development can produce to get feedback, e.g. process descriptions, test cases. But customers can be delighted more easily with code based increments in functionality. ? No, I´m not talking about the endless possibilities this opens for parallel processing. Data flows are useful independently of multi-core processors and Actor-based designs. That´s my whole point here. Data flows are good for reasoning and evolvability. So forget about any special frameworks you might need to reap benefits from data flows. None are necessary. Translating data flow designs even into plain of Java is possible. ?

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  • Design considerations for temporarily transforming a player into an animal in a role playing game

    - by mikedev
    I am working on a role playing game for fun and to practice design patterns. I would like players to be able to transform themselves into different animals. For example, a Druid might be able to shape shift into a cheetah. Right now I'm planning on using the decorator pattern to do this but my question is - how do I make it so that when a druid is in the cheetah form, they can only access skills for the cheetah? In other words, they should not be able to access their normal Druid skills. Using the decorator pattern it appears that even in the cheetah form my druid will be able to access their normal druid skills. class Druid : Character { // many cool druid skills and spells void LightHeal(Character target) { } } abstract class CharacterDecorator : Character { Character DecoratedCharacter; } class CheetahForm : CharacterDecorator { Character DecoratedCharacter; public CheetahForm(Character decoratedCharacter) { DecoratedCharacter= decoratedCharacter; } // many cool cheetah related skills void CheetahRun() { // let player move very fast } } now using the classes Druid myDruid = new Druid(); myDruid.LightHeal(myDruid); // casting light heal here is fine myDruid = new CheetahForm(myDruid); myDruid.LightHeal(myDruid); // casting here should not be allowed Hmmmm...now that I think about it, will myDruid be unable to us the Druid class spells/skills unless the class is down-casted? But even if that's the case, is there a better way to ensure that myDruid at this point is locked out from all Druid related spells/skills until it is cast back to a Druid (since currently it's in CheetahForm)

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  • Implementation/interface inheritance design question.

    - by Neil G
    I would like to get the stackoverflow community's opinion on the following three design patterns. The first is implementation inheritance; the second is interface inheritance; the third is a middle ground. My specific question is: Which is best? implementation inheritance: class Base { X x() const = 0; void UpdateX(A a) { y_ = g(a); } Y y_; } class Derived: Base { X x() const { return f(y_); } } interface inheritance: class Base { X x() const = 0; void UpdateX(A a) = 0; } class Derived: Base { X x() const { return x_; } void UpdateX(A a) { x_ = f(g(a)); } X x_; } middle ground: class Base { X x() const { return x_; } void UpdateX(A a) = 0; X x_; } class Derived: Base { void UpdateX(A a) { x_ = f(g(a)); } } I know that many people prefer interface inheritance to implementation inheritance. However, the advantage of the latter is that with a pointer to Base, x() can be inlined and the address of x_ can be statically calculated.

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  • Social Media Java Design Problem

    - by jboyd
    I need to put something together quickly that will take blog posts and place them on social media sites, the requirements are as follows: Blog Entries are independent records that already exist, they have a published date and a modified date, the blog entry application cannot be changed, at least not substantially A new blog entry, or update needs to be sent to social media sites I currently do not need to update or delete social media communications if the blog entry is edited, or deleted, though I may need to later My design problems here are as follows: how do I know the status of each update how can I figure out what blog entry updates and postings have already been sent out? how can I quickly poll the blog entry table for postings that haven't yet been sent out? Avoiding looking at each Entry record from the DB as an object and asking if it's been sent already. That would be too slow. I cannot hook into any Blog Entry update code, my only option would be to create a trigger that an update queues something to be processed I'm looking for general guiding principles here, the biggest problem I'm having is coming up with any reasonable way to figure out if a blog entry should be sent to our social media sites in the first place

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  • Issue with class design to model user preferences for different classes

    - by Mulone
    Hi all, I'm not sure how to design a couple of classes in my app. Basically that's a situation: each user can have many preferences each preference can be referred to an object of different classes (e.g. album, film, book etc) the preference is expressed as a set of values (e.g. score, etc). The problem is that many users can have preferences on the same objects, e.g.: John: score=5 for filmid=apocalypsenow Paul: score=3 for filmid=apocalypsenow And naturally I don't want to duplicate the object film in each user. So I could create a class called "preference" holding a score and then a target object, something like: User{ hasMany preferences } Preference{ belongsTo User double score Film target Album target //etc } and then define just one target. Then I would create an interface for the target Classes (album, film etc): Interface canBePreferred{ hasMany preferences } And implement all of those classes. This could work, but it looks pretty ugly and it would requires a lot of joins to work. Do you have some patterns I could use to model this nicely? Cheers, Mulone

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  • Object model design choice

    - by spinon
    I am currently working on a ASP.NET MVC reporting application using C#. This is a redesign from a PHP application that was just initially thrown together and is now starting to gain some more traction. SowWe are in the process of reworking the backend to have a more OO approach. One of the descisions I am currently wrestling with is how to structure the domain objects. Since 95% of the site is readonly I am not sure if the typical approaches are practical. Should I create domain objects for the primary pieces of the application (ticket, assignment, assignee) and then create static methods off of these areas to pull the reporting data? Or should I just skip that part and create the chart data classes and have some get method off of these classes? It's not a real big application and currenlty I am the only one developing on it. But I feel torn as to which approach. I feel that the first one is the better choice but maybe overkill given that the majority of uses is for aggregate reporting. Anybody have some good insight on why I should go one way or another?

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  • Abstract Design Pattern implementation

    - by Pathachiever11
    I started learning design patterns a while ago (only covered facade and abstract so far, but am enjoying it). I'm looking to apply the Abstract pattern to a problem I have. The problem is: Supporting various Database systems using one abstract class and a set of methods and properties, which then the underlying concrete classes (inheriting from abstract class) would be implementing. I have created a DatabaseWrapper abstract class and have create SqlClientData and MSAccessData concrete class that inherit from the DatabaseWrapper. However, I'm still a bit confused about how the pattern goes as far as implementing these classes on the Client. Would I do the following?: DatabaseWrapper sqlClient = new SqlClientData(connectionString); This is what I saw in an example, but that is not what I'm looking for because I want to encapsulate the concrete classes; I only want the Client to use the abstract class. This is so I can support for more database systems in the future with minimal changes to the Client, and creating a new concrete class for the implementations. I'm still learning, so there might be a lot of things wrong here. Please tell me how I can encapsulate all the concrete classes, and if there is anything wrong with my approach. Many Thanks! PS: I'm very excited to get into software architecture, but still am a beginner, so take it easy on me. :)

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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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  • Domain driven design: Manager and service

    - by ryudice
    I'm creating some business logic in the application but I'm not sure how or where to encapsulate it, I've used the repository pattern for data access, I've seen some projects that use DDD that have some classes with the "Service" suffix and the "manager" suffix, what are each of this clases suppose to take care of in DDD?

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  • Question about design (inheritance, polymorphism)

    - by Dan
    Hi, I have a question about a problem I'm struggling with. Hope you can bear with me. Imagine I have an Object class representing the base class of a hierarchy of physical objects. Later I inherit from it to create an Object1D, Object2D and Object3D classes. Each of these derived classes will have some specific methods and attributes. For example, the 3d object might have functionality to download a 3d model to be used by a renderer. So I'd have something like this: class Object {}; class Object1D : public Object { Point mPos; }; class Object2D : public Object { ... }; class Object3D : public Object { Model mModel; }; Now I'd have a separate class called Renderer, which simply takes an Object as argument and well, renders it :-) In a similar way, I'd like to support different kinds of renderers. For instance, I could have a default one that every object could rely on, and then provide other specific renderers for some kind of objects: class Renderer {}; // Default one class Renderer3D : public Renderer {}; And here comes my problem. A renderer class needs to get an Object as an argument, for example in the constructor in order to retrieve whatever data it needs to render the object. So far so good. But a Renderer3D would need to get an Object3D argument, in order to get not only the basic attributes but also the specific attributes of a 3d object. Constructors would look like this: CRenderer(Object& object); CRenderer3D(Object3D& object); Now how do I specify this in a generic way? Or better yet, is there a better way to design this? I know I could rely on RTTI or similar but I'd like to avoid this if possible as I feel there is probably a better way to deal with this. Thanks in advance!

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  • C# MultiThread Safe Class Design

    - by Robert
    I'm trying to designing a class and I'm having issues with accessing some of the nested fields and I have some concerns with how multithread safe the whole design is. I would like to know if anyone has a better idea of how this should be designed or if any changes that should be made? using System; using System.Collections; namespace SystemClass { public class Program { static void Main(string[] args) { System system = new System(); //Seems like an awkward way to access all the members dynamic deviceInstance = (((DeviceType)((DeviceGroup)system.deviceGroups[0]).deviceTypes[0]).deviceInstances[0]); Boolean checkLocked = deviceInstance.locked; //Seems like this method for accessing fields might have problems with multithreading foreach (DeviceGroup dg in system.deviceGroups) { foreach (DeviceType dt in dg.deviceTypes) { foreach (dynamic di in dt.deviceInstances) { checkLocked = di.locked; } } } } } public class System { public ArrayList deviceGroups = new ArrayList(); public System() { //API called to get names of all the DeviceGroups deviceGroups.Add(new DeviceGroup("Motherboard")); } } public class DeviceGroup { public ArrayList deviceTypes = new ArrayList(); public DeviceGroup() {} public DeviceGroup(string deviceGroupName) { //API called to get names of all the Devicetypes deviceTypes.Add(new DeviceType("Keyboard")); deviceTypes.Add(new DeviceType("Mouse")); } } public class DeviceType { public ArrayList deviceInstances = new ArrayList(); public bool deviceConnected; public DeviceType() {} public DeviceType(string DeviceType) { //API called to get hardwareIDs of all the device instances deviceInstances.Add(new Mouse("0001")); deviceInstances.Add(new Keyboard("0003")); deviceInstances.Add(new Keyboard("0004")); //Start thread CheckConnection that updates deviceConnected periodically } public void CheckConnection() { //API call to check connection and returns true this.deviceConnected = true; } } public class Keyboard { public string hardwareAddress; public bool keypress; public bool deviceConnected; public Keyboard() {} public Keyboard(string hardwareAddress) { this.hardwareAddress = hardwareAddress; //Start thread to update deviceConnected periodically } public void CheckKeyPress() { //if API returns true this.keypress = true; } } public class Mouse { public string hardwareAddress; public bool click; public Mouse() {} public Mouse(string hardwareAddress) { this.hardwareAddress = hardwareAddress; } public void CheckClick() { //if API returns true this.click = true; } } }

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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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  • how to design a schema where the columns of a table are not fixed

    - by hIpPy
    I am trying to design a schema where the columns of a table are not fixed. Ex: I have an Employee table where the columns of the table are not fixed and vary (attributes of Employee are not fixed and vary). Nullable columns in the Employee table itself i.e. no normalization Instead of adding nullable columns, separate those columns out in their individual tables ex: if Address is a column to be added then create table Address[EmployeeId, AddressValue]. Create tables ExtensionColumnName [EmployeeId, ColumnName] and ExtensionColumnValue [EmployeeId, ColumnValue]. ExtensionColumnName would have ColumnName as "Address" and ExtensionColumnValue would have ColumnValue as address value. Employee table EmployeeId Name ExtensionColumnName table ColumnNameId EmployeeId ColumnName ExtensionColumnValue table EmployeeId ColumnNameId ColumnValue There is a drawback is the first two ways as the schema changes with every new attribute. Note that adding a new attribute is frequent. I am not sure if this is the good or bad design. If someone had a similar decision to make, please give an insight on things like foreign keys / data integrity, indexing, performance, reporting etc.

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  • Using design-patterns to transform web-service model classes into local model classes and vise versa

    - by Daniil Petrov
    There is a web-application built with play framework 1.2.7. It contains less than 10 model classes. The main purpose of the application is a lightweight access to a complex remote application (more than 50 model classes). The remote application has its own SOAP API and we use it for synchronization of data. There is a scheduled job in the web-app which makes requests to the remote app. It gets bunches of objects from the remote model and populates corresponding objects of the local model. Currently, there are two groups of classes - the local model and the remote model (generated from wsdl schema). It is not allowed to make any modifications to the remote model. Transformations are being made in the scheduled job class. When it gets objects from the remote app it creates local objects. Recently, it was decided to add a possibility to modify the remote objects. It requires more transformations on our side. We need to transform from remote to local model when reading objects and from local to remote when changing objects. I wonder if this would be possible to use some design-patterns to reduce a number of transformations?

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  • Design pattern for window management in a Java Swing app

    - by Lord Torgamus
    I've just started creating my very first little Java Swing app. When the program opens, it brings up a single, simple window with a label and a couple buttons. Clicking one of those buttons is supposed to wipe out the welcome screen and replace it with a totally different panel. I'm not sure what the best way to create that functionality is. One method would be to pass my JFrame as an argument into... just about every other component, but that feels hacky to me. Or, there's making each panel double as an action listener, but that doesn't seem right, either. Is there a design pattern I should be applying here? "Replace the contents of the main — and only — window" must be a reasonably common operation. A name for the pattern would be enough; I can use Google on my own from there. (I wouldn't say no to a longer explanation, though.)

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