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  • The Basics of SEO - Complete Analysis

    SEO is an interesting and important concept for taskmasters. For those who are new to the website business SEO (Search Engine Optimization) is no easy task. Patience is truly a virtue when it comes to learning how to optimize search engine traffic.

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  • Patterns and conventions to document changes while developing

    - by Talysson
    Let's say I'm developing a second version of an API, and there's some changes in method names and so on from the previous version. What's a good way to document these changes ? I mean, is it better to document while changing (but, maybe, there will be more changes before the release, so I think it could be more work than necessary) or write down some topic and document after all the changes are done ?

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  • Visual Studio Code Analysis - Does Microsoft follow it themselves?

    - by Oskar Kjellin
    Did a quick search but could not find anything about this. I guess all of you know that the Visual Studio Code Analysis is quite nitpicking and gives warnings about a lot of things. Does anybody know how well Microsoft follow this themselves..? That is, if I were to run a code analysis on their assemblies, would the warnings be none or very few (perhaps surpress warning with a justification..?).

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  • Benefit cost analysis software

    - by dassouki
    I was wondering if anyone knows about a benefit cost analysis software geared towards transportation projects. I use microBENCOST, but it's old and buggy. MicroBENCOST SUMMARY. if you have ever done benefit / cost analysis, what softwre did you use and would you recommend it?

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  • Extracting Windows 8 Start Screen Patterns

    - by oreon
    Is there any way to extract the Windows 8 Start Screen patterns, in order to use them as standalone wallpapers on other systems? For example see this screenshot: I am interested in the dark blue background. I heard that this background is somehow adapted to your chosen color theme. So many different variations should exist. Engadget has an article here briefly talking about these background patterns and the different color schemes. They call them "personalization tattoos".

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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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  • Patterns to deal with with functions that can have different kinds of results.

    - by KaptajnKold
    Suppose you have an method on an object that given the some input alters the objects state if the input validates according to some complex logic. Now suppose that when the input doesn't validate, it can be due to several different things, each of which we would like to be able to deal with in different ways. I'm sure many of you are thinking: That's what exceptions are for! I've thought of this also. But my reservation against using exceptions is that in some cases there is nothing exceptional about the input not validating and I really would like to avoid using exceptions to control what is really just in the expected flow of the program. If there were only one interpretation possible, I could simply choose to return a boolean value indicating whether or not the operation resulted in a state change or not and the respond appropriately when it did not. There is of course also the option to return a status code which the client can then choose to interpret or not. I don't like this much either because there is nothing semantic about status codes. The solution I have so far is to always check for each possible situation which I am able to handle before I call the method which then returns a boolean to inform the client if the object changed state. This leaves me the flexibility to handle as few or as many as the possible situations as I wish depending on the context I am in. It also has the benefit of making the method I am calling simpler to write. The drawback is that there is quite a lot of duplication in the client code wherever I call the method. Which of these solutions do you prefer and why? What other patterns do people use for providing meaningful feedback from functions? I know that some languages support multiple return values, and I if I had that option I would surely prefer it.

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  • Better Understand the 'Strategy' Design Pattern

    - by Imran Omar Bukhsh
    Greetings Hope you all are doing great. I have been interested in design patterns for a while and started reading 'Head First Design Patterns'. I started with the first pattern called the 'Strategy' pattern. I went through the problem outlined in the images below and first tried to propose a solution myself so I could really grasp the importance of the pattern. So my question is that why is my solution ( below ) to the problem outlined in the images below not good enough. What are the good / bad points of my solution vs the pattern? What makes the pattern clearly the only viable solution ? Thanks for you input, hope it will help me better understand the pattern. MY SOLUTION Parent Class: DUCK <?php class Duck { public $swimmable; public $quackable; public $flyable; function display() { echo "A Duck Looks Like This<BR/>"; } function quack() { if($this->quackable==1) { echo("Quack<BR/>"); } } function swim() { if($this->swimmable==1) { echo("Swim<BR/>"); } } function fly() { if($this->flyable==1) { echo("Fly<BR/>"); } } } ?> INHERITING CLASS: MallardDuck <?php class MallardDuck extends Duck { function MallardDuck() { $this->quackable = 1; $this->swimmable = 1; } function display() { echo "A Mallard Duck Looks Like This<BR/>"; } } ?> INHERITING CLASS: WoddenDecoyDuck <?php class WoddenDecoyDuck extends Duck { function woddendecoyduck() { $this->quackable = 0; $this->swimmable = 0; } function display() { echo "A Wooden Decoy Duck Looks Like This<BR/>"; } } Thanking you for your input. Imran

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  • Fraud Detection with the SQL Server Suite Part 1

    - by Dejan Sarka
    While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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  • DAO/Webservice Consumption in Web Application

    - by Gavin
    I am currently working on converting a "legacy" web-based (Coldfusion) application from single data source (MSSQL database) to multi-tier OOP. In my current system there is a read/write database with all the usual stuff and additional "read-only" databases that are exported daily/hourly from an Enterprise Resource Planning (ERP) system by SSIS jobs with business product/item and manufacturing/SCM planning data. The reason I have the opportunity and need to convert to multi-tier OOP is a newer more modern ERP system is being implemented business wide that will be a complete replacement. This newer ERP system offers several interfaces for third party applications like mine, from direct SQL access to either a dotNet web-service or a SOAP-like web-service. I have found several suitable frameworks I would be happy to use (Coldspring, FW/1) but I am not sure what design patterns apply to my data access object/component and how to manage the connection/session tokens, with this background, my question has the following three parts: Firstly I have concerns with moving from the relative safety of a SSIS job that protects me from downtime and speed of the ERP system to directly connecting with one of the web services which I note seem significantly slower than I expected (simple/small requests often take up to a whole second). Are there any design patterns I can investigate/use to cache/protect my data tier? It is my understanding data access objects (the component that connects directly with the web services and convert them into the data types I can then work with in my Domain Objects) should be singletons (and will act as an Adapter/Facade), am I correct? As part of the data access object I have to setup a connection by username/password (I could set up multiple users and/or connect multiple times with this) which responds with a session token that needs to be provided on every subsequent request. Do I do this once and share it across the whole application, do I setup a new "connection" for every user of my application and keep the token in their session scope (might quickly hit licensing limits), do I set the "connection" up per page request, or is there a design pattern I am missing that can manage multiple "connections" where a requests/access uses the first free "connection"? It is worth noting if the ERP system dies I will need to reset/invalidate all the connections and start from scratch, and depending on which web-service I use might need manually close the "connection/session"

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  • Distinguishing between UI command & domain commands

    - by SonOfPirate
    I am building a WPF client application using the MVVM pattern that provides an interface on top of an existing set of business logic residing in a library which is shared with other applications. The business library followed a domain-driven architecture using CQRS to separate the read and write models (no event sourcing). The combination of technologies and patterns has brought up an interesting conundrum: The MVVM pattern uses the command pattern for handling user-interaction with the view models. .NET provides an ICommand interface which is implemented by most MVVM frameworks, like MVVM Light's RelayCommand and Prism's DelegateCommand. For example, the view model would expose a number of command objects as properties that are bound to the UI and respond when the user performs actions like clicking buttons. Many implementations of the CQRS use the command pattern to isolate and encapsulate individual behaviors. In my business library, we have implemented the write model as command / command-handler pairs. As such, when we want to do some work, such as create a new order, we 'issue' a command (CreateOrderCommand) which is routed to the command-handler responsible for executing the command. This is great, clearly explained in many sources and I am good with it. However, take this scenario: I have a ToolbarViewModel which exposes a CreateNewOrderCommand property. This ICommand object is bound to a button in the UI. When clicked, the UI command creates and issues a new CreateOrderCommand object to the domain which is handled by the CreateOrderCommandHandler. This is difficult to explain to other developers and I am finding myself getting tongue-tied because everything is a command. I'm sure I'm not the first developer to have patterns overlap like this where the naming/terminology also overlap. How have you approached distinguishing your commands used in the UI from those used in the domain? (Edit: I should mention that the business library is UI-agnostic, i.e. no UI technology-specific code exists, or will exists, in this library.)

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  • What are some ways to separate game logic from animations and the draw loop?

    - by TMV
    I have only previously made flash games, using MovieClips and such to separate out my animations from my game logic. Now I am getting into trying my hand at making a game for Android, but the game programming theory around separating these things still confuses me. I come from a background of developing non game web applications so I am versed in more MVC like patterns and am stuck in that mindset as I approach game programming. I want to do things like abstract my game by having, for example, a game board class that contains the data for a grid of tiles with instances of a tile class that each contain properties. I can give my draw loop access to this and have it draw the game board based on the properties of each tile on the game board, but I don't understand where exactly animation should go. As far as I can tell, animation sort of sits between the abstracted game logic (model) and the draw loop (view). With my MVC mindset, it's frustrating trying to decide where animation is actually supposed to go. It would have quite a bit of data associated with it like a model, but seemingly needs to be very closely coupled with the draw loop in order to have things like frame independent animation. How can I break out of this mindset and start thinking about patterns that make more sense for games?

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  • How to implement Restricted access to application features

    - by DroidUser
    I'm currently developing a web application, that provides some 'service' to the user. The user will have to select a 'plan' according to which she/he will be allowed to perform application specific actions but up to a limit defined by the plan. A Plan will also limit access to certain features, which will not be available at all for some plans. As an example : say there are 3 plans, 2 actions throughout the application users in plan-1 can perform action-1 3 times, and they can't perform action-2 at all users in plan-2 can perform action-1 10 times, action-2 5 times users in plan-3 can perform action-1 20 times, action-2 10 times So i'm looking for the best way to get this done, and my main concerns besides implementing it, are the following(in no particular order) maintainability/changeability : the number of plans, and type of features/actions will change in the final product industry standard/best practice : for future readiness!! efficiency : ofcourse, i want fast code!! I have never done anything like this before, so i have no clue about how do i go about implementing these functionalities. Any tips/guides/patterns/resources/examples? I did read a little about ACL, RBAC, are they the patterns that i need to follow? Really any sort of feedback will help.

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  • Scalable / Parallel Large Graph Analysis Library?

    - by Joel Hoff
    I am looking for good recommendations for scalable and/or parallel large graph analysis libraries in various languages. The problems I am working on involve significant computational analysis of graphs/networks with 1-100 million nodes and 10 million to 1+ billion edges. The largest SMP computer I am using has 256 GB memory, but I also have access to an HPC cluster with 1000 cores, 2 TB aggregate memory, and MPI for communication. I am primarily looking for scalable, high-performance graph libraries that could be used in either single or multi-threaded scenarios, but parallel analysis libraries based on MPI or a similar protocol for communication and/or distributed memory are also of interest for high-end problems. Target programming languages include C++, C, Java, and Python. My research to-date has come up with the following possible solutions for these languages: C++ -- The most viable solutions appear to be the Boost Graph Library and Parallel Boost Graph Library. I have looked briefly at MTGL, but it is currently slanted more toward massively multithreaded hardware architectures like the Cray XMT. C - igraph and SNAP (Small-world Network Analysis and Partitioning); latter uses OpenMP for parallelism on SMP systems. Java - I have found no parallel libraries here yet, but JGraphT and perhaps JUNG are leading contenders in the non-parallel space. Python - igraph and NetworkX look like the most solid options, though neither is parallel. There used to be Python bindings for BGL, but these are now unsupported; last release in 2005 looks stale now. Other topics here on SO that I've looked at have discussed graph libraries in C++, Java, Python, and other languages. However, none of these topics focused significantly on scalability. Does anyone have recommendations they can offer based on experience with any of the above or other library packages when applied to large graph analysis problems? Performance, scalability, and code stability/maturity are my primary concerns. Most of the specialized algorithms will be developed by my team with the exception of any graph-oriented parallel communication or distributed memory frameworks (where the graph state is distributed across a cluster).

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  • Java regex patterns - compile time constants or instance members?

    - by KepaniHaole
    Currently, I have a couple of singleton objects where I'm doing matching on regular expressions, and my Patterns are defined like so: class Foobar { private final Pattern firstPattern = Pattern.compile("some regex"); private final Pattern secondPattern = Pattern.compile("some other regex"); // more Patterns, etc. private Foobar() {} public static Foobar create() { /* singleton stuff */ } } But I was told by someone the other day that this is bad style, and Patterns should always be defined at the class level, and look something like this instead: class Foobar { private static final Pattern FIRST_PATTERN = Pattern.compile("some regex"); private static final Pattern SECOND_PATTERN = Pattern.compile("some other regex"); // more Patterns, etc. private Foobar() {} public static Foobar create() { /* singleton stuff */ } } The lifetime of this particular object isn't that long, and my main reason for using the first approach is because it doesn't make sense to me to hold on to the Patterns once the object gets GC'd. Any suggestions / thoughts?

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  • Patterns for non-layered applications

    - by Paul Stovell
    In Patterns of Enterprise Application Architecture, Martin Fowler writes: This book is thus about how you decompose an enterprise application into layers and how those layers work together. Most nontrivial enterprise applications use a layered architecture of some form, but in some situations other approaches, such as pipes and filters, are valuable. I don't go into those situations, focussing instead on the context of a layered architecture because it's the most widely useful. What patterns exist for building non-layered applications/parts of an application? Take a statistical modelling engine for a financial institution. There might be a layer for data access, but I expect that most of the code would be in a single layer. Would you still expect to see Gang of Four patterns in such a layer? How about a domain model? Would you use OO at all, or would it be purely functional? The quote mentions pipes and filters as alternate models to layers. I can easily imagine a such an engine using pipes as a way to break down the data processing. What other patterns exist? Are there common patterns for areas like task scheduling, results aggregation, or work distribution? What are some alternatives to MapReduce?

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  • An old flaw in X Window System. How does it work?

    - by Legend
    I was going through an article today when it mentioned the following: "We've found many errors over the years. One of the absolute best was the following in the X Window System: if(getuid() != 0 && geteuid == 0) { ErrorF("Only root"); exit(1); } It allowed any local user to get root access. (The tautological check geteuid == 0 was intended to be geteuid() == 0. In its current form, it compress the address of geteuid to 0; given that the function exists, its address is never 0)." The article explained what was wrong with the code but I would like to know what it means to say that "It allowed any local user to get root access". I am not an expert in C but can someone give me an exact context in which this exploit would work? Specifically, what I mean is, lets say I am the local user, how would I get root access if we assume this code to be present somewhere?

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  • Finding patterns of failure in a Unit Test

    - by Pekka
    I'm new to Unit Testing, and I'm only getting into the routine of building test suites. I have what is going to be a rather large project that I want to build tests for from the start. I'm trying to figure out general strategies and patterns for building test suites. When you look at a class, many tests come to you obviously due to the nature of the class. Say for a "user account" class with basic CRUD operations, being related to a database table, we will want to test - well, the CRUD. creating an object and seeing whether it exists query its properties change some properties change some properties to incorrect values and delete it again. As for how to break things, there are "fail" tests common to most CRUD classes like: Invalid input data types A number as the ID key that exceeds the range of the chosen data type Input in an incorrect character encoding Input that is too long And so on and so on. For a unit test concerned with file operations, the list of "breaking things" could be Invalid characters in file name File name too long File name uses incorrect protocol or path I'm pretty sure similar patterns - applicable beyond the unit test one is currently working on - can be found for most units that are being tested. Now my question is: Am I correct in seeing such "breaking patterns"? Or am I getting something completely wrong about Unit testing, and if I did it right, this wouldn't be an issue at all? Is Unit Testing as a process of finding as many ways to break the unit as possible the right way to go? If I am correct: Are there existing definitions, lists, cheat sheets for such patterns? Are there any provisions (mainly in PHPUnit, as that's the framework I'm working in) to automate such patterns? Is there any assistance - in the form of check lists, or software - to aid in writing complete tests?

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