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• Emacs with CEDET changes copy/paste to include trailing spaces?

  - by Paul D.

  I just started trying out CEDET today, which I really like, but it seems to do something completely worthless with respect to copying/pasting. If I highlight some stuff and copy it, when it gets pasted back the newlines are eliminated and there is just a ton of trailing whitespace on each line. This is really worthless. All I have in my .emacs right now for CEDET is the following: (load-file "~/.emacs.d/cedet-1.0pre7/common/cedet.el") (require 'semantic-ia) (require 'semantic-gcc) (semantic-load-enable-code-helpers) Does anybody know how to turn this off? I can't find anything about this except that the CEDET main page says it has "magic copy & paste".

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• How to manage css of big websites within team environment without mess?

  - by jitendra

  Where multiple people can work on same css. is it possible to follow semantic name rules even in large websites. If I would write all main css first time with semantic names . then what and how i should guideline/instruction to other developer to maintain css readability, validation . and to know quickly where other are adding their own css if required. Right now every one just go to down and write required css classes ot IDs at bottom. and most of the time they don't write semantic names.

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• Multidimensional Thinking–24 Hours of Pass: Celebrating Women in Technology

  - by smisner

  It’s Day 1 of #24HOP and it’s been great to participate in this event with so many women from all over the world in one long training-fest. The SQL community has been abuzz on Twitter with running commentary which is fun to watch while listening to the current speaker. If you missed the fun today because you’re busy with all that work you’ve got to do – don’t despair. All sessions are recorded and will be available soon. Keep an eye on the 24 Hours of Pass page for details. And the fun’s not over today. Rather than run 24 hours consecutively, #24HOP is now broken down into 12-hours over two days, so check out the schedule to see if there’s a session that interests you and fits your schedule. I’m pleased to announce that my business colleague Erika Bakse ( Blog | Twitter) will be presenting on Day 2 – her debut presentation for a PASS event. (And I’m also pleased to say she’s my daughter!) Multidimensional Thinking: The Presentation My contribution to this lineup of terrific speakers was Multidimensional Thinking. Here’s the abstract: “Whether you’re developing Analysis Services cubes or creating PowerPivot workbooks, you need to get into a multidimensional frame of mind to produce a model that best enables users to answer their business questions on their own. Many database professionals struggle initially with multidimensional models because the data modeling process is much different than the one they use to produce traditional, third normal form databases. In this session, I’ll introduce you to the terminology of multidimensional modeling and step through the process of translating business requirements into a viable model.” If you watched the presentation and want a copy of the slides, you can download a copy here. And you’re welcome to download the slides even if you didn’t watch the presentation, but they’ll make more sense if you did! Kimball All the Way There’s only so much I can cover in the time allotted, but I hope that I succeeded in my attempt to build a foundation that prepares you for starting out in business intelligence. One of my favorite resources that will get into much more detail about all kinds of scenarios (well beyond the basics!) is The Data Warehouse Toolkit (Second Edition) by Ralph Kimball. Anything from Kimball or the Kimball Group is worth reading. Kimball material might take reading and re-reading a few times before it makes sense. From my own experience, I found that I actually had to just build my first data warehouse using dimensional modeling on faith that I was going the right direction because it just didn’t click with me initially. I’ve had years of practice since then and I can say it does get easier with practice. The most important thing, in my opinion, is that you simply must prototype a lot and solicit user feedback, because ultimately the model needs to make sense to them. They will definitely make sure you get it right! Schema Generation One question came up after the presentation about whether we use SQL Server Management Studio or Business Intelligence Development Studio (BIDS) to build the tables for the dimensional model. My answer? It really doesn’t matter how you create the tables. Use whatever method that you’re comfortable with. But just so happens that it IS possible to set up your design in BIDS as part of an Analysis Services project and to have BIDS generate the relational schema for you. I did a Webcast last year called Building a Data Mart with Integration Services that demonstrated how to do this. Yes, the subject was Integration Services, but as part of that presentation, I showed how to leverage Analysis Services to build the tables, and then I showed how to use Integration Services to load those tables. I blogged about this presentation in September 2010 and included downloads of the project that I used. In the blog post, I explained that I missed a step in the demonstration. Oops. Just as an FYI, there were two more Webcasts to finish the story begun with the data – Accelerating Answers with Analysis Services and Delivering Information with Reporting Services. If you want to just cut to the chase and learn how to use Analysis Services to build the tables, you can see the Using the Schema Generation Wizard topic in Books Online.

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• Does your analytic solution tell you what questions to ask?

  - by Manan Goel

  Analytic solutions exist to answer business questions. Conventional wisdom holds that if you can answer business questions quickly and accurately, you can take better business decisions and therefore achieve better business results and outperform the competition. Most business questions are well understood (read structured) so they are relatively easy to ask and answer. Questions like what were the revenues, cost of goods sold, margins, which regions and products outperformed/underperformed are relatively well understood and as a result most analytics solutions are well equipped to answer such questions. Things get really interesting when you are looking for answers but you don’t know what questions to ask in the first place? That’s like an explorer looking to make new discoveries by exploration. An example of this scenario is the Center of Disease Control (CDC) in United States trying to find the vaccine for the latest strand of the swine flu virus. The researchers at CDC may try hundreds of options before finally discovering the vaccine. The exploration process is inherently messy and complex. The process is fraught with false starts, one question or a hunch leading to another and the final result may look entirely different from what was envisioned in the beginning. Speed and flexibility is the key; speed so the hundreds of possible options can be explored quickly and flexibility because almost everything about the problem, solutions and the process is unknown.  Come to think of it, most organizations operate in an increasingly unknown or uncertain environment. Business Leaders have to take decisions based on a largely unknown view of the future. And since the value proposition of analytic solutions is to help the business leaders take better business decisions, for best results, consider adding information exploration and discovery capabilities to your analytic solution. Such exploratory analysis capabilities will help the business leaders perform even better by empowering them to refine their hunches, ask better questions and take better decisions. That’s your analytic system not only answering the questions but also suggesting what questions to ask in the first place. Today, most leading analytic software vendors offer exploratory analysis products as part of their analytic solutions offerings. So, what characteristics should be top of mind while evaluating the various solutions? The answer is quite simply the same characteristics that are essential for exploration and analysis – speed & flexibility. Speed is required because the system inherently has to be agile to handle hundreds of different scenarios with large volumes of data across large user populations. Exploration happens at the speed of thought so make sure that you system is capable of operating at speed of thought. Flexibility is required because the exploration process from start to finish is full of unknowns; unknown questions, answers and hunches. So, make sure that the system is capable of managing and exploring all relevant data – structured or unstructured like databases, enterprise applications, tweets, social media updates, documents, texts, emails etc. and provides flexible Google like user interface to quickly explore all relevant data. Getting Started You can help business leaders become “Decision Masters” by augmenting your analytic solution with information discovery capabilities. For best results make sure that the solution you choose is enterprise class and allows advanced, yet intuitive, exploration and analysis of complex and varied data including structured, semi-structured and unstructured data.  You can learn more about Oracle’s exploratory analysis solutions by clicking here.

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• Circular Shifts on Strings in Bash

  - by Kyle Van Koevering

  I have a homework assignment where I need to take input from a file and continuously remove the first word in a line and append it to the end of the line until all combinations have been done. I really don't know where to begin and would be thankful for any sort of direction. The part that has me confused is that this is suppose to be performed without the use of arrays. I'm not just fishing for someone to solve the problem for me, I'm just looking for some direction. Thank you very much for your time and help. SAMPlE INPUT: Pipes and Filters Java Swing Software Requirements Analysis SAMPLE OUTPUT: Analysis Software Requirements Filters Pipes and Java Swing Pipes and Filters Requirements Analysis Software Software Requirements Analysis Swing Java

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• Statistics toolbox in Matlab

  - by bayes90

  Is the default linear discriminant analysis (LDA) in Matlab the Fisher discriminant analysis?

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• Visual Studio hangs when deploying a cube

  - by Richie

  Hello All, I'm having an issue with an Analysis Services project in Visual Studio 2005. My project always builds but only occasionally deploys. No errors are reported and VS just hangs. This is my first Analysis Services project so I am hoping that there is something obvious that I am just missing. Here is the situation I have a cube that I have successfully deployed. I then make some change, e.g., adding a hierarchy to a dimension. When I try to deploy again VS hangs. I have to restart Analysis Services to regain control of VS so I can shut it down. I restart everything sometimes once, sometimes twice or more before the project will eventually deploy. This happens with any change I make there seems to be no pattern to this behaviour. Sometimes I have to delete the cube from Analysis Services before restarting everything to get a successful deploy. Also I have successfully deployed the cube, and then subsequently successfully reprocessed a dimension then when I open a query window in SQL Server Management Studio it says that it can find any cubes. As a test I have deployed a cube successfully. I have then deleted it in Analysis Services and attempted to redeploy it, without making any changes to the cube, only to have the same behaviour mentioned above. VS just hangs with no reason so I have no idea where to start hunting down the problem. It is taking 15-20 minutes to make a change as simple as setting the NameColumn of a dimension attribute. As you can imagine this is taking hours of my time so I would greatly appreciate any assistance anyone can give me.

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• How does the Amazon Recommendation feature work?

  - by Rachel

  What technology goes in behind the screens of Amazon recommendation technology? I believe that Amazon recommendation is currently the best in the market, but how do they provide us with such relevant recommendations? Recently, we have been involved with similar recommendation kind of project, but would surely like to know about the in and outs of the Amazon recommendation technology from a technical standpoint. Any inputs would be highly appreciated. Update: This patent explains how personalized recommendations are done but it is not very technical, and so it would be really nice if some insights could be provided. From the comments of Dave, Affinity Analysis forms the basis for such kind of Recommendation Engines. Also here are some good reads on the Topic Demystifying Market Basket Analysis Market Basket Analysis Affinity Analysis Suggested Reading: Data Mining: Concepts and Technique

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• SQL Server 2008 and SP1

  - by andrew007

  Hi, I have a server where I installed SQL Server 2008 and after I applied the SP1. Now, I want also to add the Analysis Services to this instance by using the "Add or remove features...". My questions are: Is it possible to add the Analysis Services on a server with SP1 already installed? How can I apply the SP1 also to the new Analysis Service feature? THANKS!

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• Is there a better way to have a two column website with header and footer, equal height columns and stretchy column widths? [closed]

  - by Seamus

  I wrote a website a while ago that is a little messy in how it does things. I used this CSS template and this equal height columns trick. I have not one but two container divs and I can't remember what they're doing. So I'm thinking of re structuring the thing from scratch, and possibly making use of the more "semantic" html5 tags like <nav> and so on at the same time. The question is: is there a better way to achieve a site structure with these properties: 2 equal height main columns (with widths as percentages of the available real estate, not explicitly stated) both a header and footer element that stretch the whole width of the total of the two main columns That allows the use of semantic html5 tags instead of meaningless divs

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• Programming Interview : How to debug a program?

  - by Jake

  I was recently asked the following question in an interview : How do you debug a C++ program ? I started by explaining that programs may have syntax and semantic errors. Compiler reports the syntax errors which can be corrected. For semantic errors, various debuggers are available. I specifically talked about gdb, which is command line, and Visual Studio IDE's debugger, which has a GUI, and common commands. I also talked about debug and release version of code, how assertions should be used for debug build, how exceptions helps in automatic cleanup & putting the program in valid state, and how logging can be useful (e.g. using std::clog). I want to know if this answer is complete or not. Also, I want to hear how other people will go about answering this question in a structured manner ? Thanks.

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• An XEvent a Day (8 of 31) – Targets Week – synchronous_event_counter

  - by Jonathan Kehayias

  Yesterday’s post, Targets Week - Bucketizers , looked at the bucketizer Targets in Extended Events and how they can be used to simplify analysis and perform more targeted analysis based on their output.  Today’s post will be fairly short, by comparison to the previous posts, while we look at the synchronous_event_counter target, which can be used to test the impact of an Event Session without actually incurring the cost of Event collection. What is the synchronous_event_counter? The synchronous_event_count...(read more)

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• SQL SERVER – Maximize Database Performance with DB Optimizer – SQL in Sixty Seconds #054

  - by Pinal Dave

  Performance tuning is an interesting concept and everybody evaluates it differently. Every developer and DBA have different opinion about how one can do performance tuning. I personally believe performance tuning is a three step process Understanding the Query Identifying the Bottleneck Implementing the Fix While, we are working with large database application and it suddenly starts to slow down. We are all under stress about how we can get back the database back to normal speed. Most of the time we do not have enough time to do deep analysis of what is going wrong as well what will fix the problem. Our primary goal at that time is to just fix the database problem as fast as we can. However, here is one very important thing which we need to keep in our mind is that when we do quick fix, it should not create any further issue with other parts of the system. When time is essence and we want to do deep analysis of our system to give us the best solution we often tend to make mistakes. Sometimes we make mistakes as we do not have proper time to analysis the entire system. Here is what I do when I face such a situation – I take the help of DB Optimizer. It is a fantastic tool and does superlative performance tuning of the system. Everytime when I talk about performance tuning tool, the initial reaction of the people is that they do not want to try this as they believe it requires lots of the learning of the tool before they use it. It is absolutely not true with the case of the DB optimizer. It is a very easy to use and self intuitive tool. Once can get going with the product, in no time. Here is a quick video I have build where I demonstrate how we can identify what index is missing for query and how we can quickly create the index. Entire three steps of the query tuning are completed in less than 60 seconds. If you are into performance tuning and query optimization you should download DB Optimizer and give it a go. Let us see the same concept in following SQL in Sixty Seconds Video: You can Download DB Optimizer and reproduce the same Sixty Seconds experience. Related Tips in SQL in Sixty Seconds: Performance Tuning – Part 1 of 2 – Getting Started and Configuration Performance Tuning – Part 2 of 2 – Analysis, Detection, Tuning and Optimizing What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Interview Questions and Answers, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Identity

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• BigData and Customer Experience: Happy Together

  - by Isabel F. Peñuelas

  The two big buzzes of the year may lay closer than it appears. Both concepts intersect at various points: BigData and Return of Investment of Marketing Campaigns On a recent post Big Data Is The Future Of Marketing Jeff Dachis explains very clearly how “Big data analytics finally allows marketers to identify, measure, and manage what is positively impacting their Brand”. Regression analysis applied to big data volumes coming from social media will substitute the failed attempts to justify marketing investments on social media in terms of followers and likes, he continues, “the measurement models applied by marketers on TV Campaigns don´t work on social”, we need to study the data with fresh eyes and maybe then we will start understanding and measuring brand engagemet. Social CRM and BigData The real value of Social CRM start by analyzing mass of big data from social media in order of applying social intelligence techniques that allow us to classify new customer niches and communities and define appropriated strategies to contact potential customers. Gartner Says that the Market for Social CRM is on pace to surpass $1 Billion in Revenue by Year-End 2012 but in words of Zach Hofer-Shall, Analyst at Forrester Research “Social customer relationship management is hard” (The Social CRM Arms Race Heats ). To succeed brands need three things: Investing in new social tools, investing in consultancy and investing in infrastructure for massive data storage and analysis. Neither CeX or BigData are easy and cheap wins. But what are the customer benefits of such investments? Big Data and Brand Engagement Time is the most valuable asset of todays consumers: tired of information overload, exhausted by the terabytes of offering, anxious because of not having the same fast multichannel experience with their services’ marketers or preferred goods providers than the one they found on their social media. Yes, I know you have read this before- me too. But is real. The motto of the Customer Experience philosophy of providing a consistent experience through multiple touchpoints that makes the relationship customer/brand easier and valuable finds it basis on understanding customer/s preferences and context for which BigData analysis is another imperative. In summary, I believe that using BigData Analysis in combination with appropriated CeX strategies and technologies is a promising direction for achieving: efficiency and marketing cost-savings; growing the customer base; and increasing customer conversion and retention. In a world: The Direction of Future Marketing.

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• MySQL and Hadoop Integration - Unlocking New Insight

  - by Mat Keep

  “Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes. As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL. The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below The Guide details each of these stages and the technologies supporting them: Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop. Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS. Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform. Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools. So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers. Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including: - Sentiment analysis; - Marketing campaign analysis; - Customer churn modeling; - Fraud detection; - Research and Development; - Risk Modeling; - And more. As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable. Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.

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• #MDX in London and speculation about future books

  - by Marco Russo (SQLBI)

  Chris Webb, who wrote the Expert Cube Development with Microsoft SQL Server 2008 Analysis Services book with me and Alberto , is preparing another Introduction to MDX course in London, this time from October 26th to 28th. It is now a three day course (previously it was two day) and you can find every other detail here . You might be wondering whether we are writing something else... well, we don't have plan to release a new edition of the Analysis Services book - after all, all the content of the...(read more)

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• Understanding Data Science: Recent Studies

  - by Joe Lamantia

  If you need such a deeper understanding of data science than Drew Conway's popular venn diagram model, or Josh Wills' tongue in cheek characterization, "Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician." two relatively recent studies are worth reading.   'Analyzing the Analyzers,' an O'Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists -- effectively personas, in a design sense -- based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That's not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science.  And the paper 'Enterprise Data Analysis and Visualization: An Interview Study' by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there's correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I'd suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work. We've undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years -- part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities -- and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I'll share and discuss in upcoming posts.

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• Using The Data Mining Query Task in SSIS

  SQL Server Integration Services (SSIS) is a Business Intelligence tool which can be used by database developers or administrators to perform Extract, Transform & Load (ETL) operations. In my previous article Using Analysis Services Processing Task & Analysis Services ... [Read Full Article]

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• WildPackets Monitors Diverse Networks

  WildPackets offers portable network analysis products which are designed for use on enterprise networks and in test and measurement labs, plus distributed network analysis solutions for enterprise-wide applications.

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• Parent-child hierarchies and unary operators in PowerPivot

  - by Marco Russo (SQLBI)

  Alberto wrote an excellent post describing how to implement the Unary Operator feature (which is present in Analysis Services) in PowerPivot (there was a previous post about parent-child hierarchies, too). I have to say that the solution is not so easy to implement as in Analysis Services, but it just works and, from a practical point of view, it is not so difficult to implement if you understand how it works and accept its limitations (only sum and subtractions are supported). I think that many...(read more)

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• Search Engine Optimizing

  Search Engine Optimization is a process by which a web site is improved so that it can be more easily found by search engines, rank higher and be found by its target audience. The main components to SEO are: keyword analysis, content analysis, title and meta tags, relevant link building, search engine submission, and maintenance. Below are steps in the process.

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• Powerful Lessons in Data from the Presidential Election

  - by Christina McKeon

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

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• Botnet Malware Sleeps Eight Months Activation, Child Concerns

  Daily Safety Check experts used a computer forensic analysis of a significant botnet that consisted of Carberp and SpyEye malware to come up with the details for their report. The analysis found that the botnet profiled the behavior of the slave computers it infected, similar to surveillance techniques used by law enforcement agencies, for an average of eight months. During the eight months, the botnet analyzed each computer's users and assigned ratings to certain activities to form a complete profile for each. Doing so allowed those behind the scheme to determine which were the most favora...

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• Tissue Specific Electrochemical Fingerprinting on the NetBeans Platform

  - by Geertjan

  Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated, and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore becoming increasingly relevant. A bioinformatics tool has been developed at universities in Prague and Brno, in the Czech Republic, for analysis and visualization in this domain, on the NetBeans Platform: More info:  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0049654

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• Feature: Lead with Intelligence

  Business efficiency depends on business decisions, and business decisions depend on current, accurate information and powerful analysis. See how Oracle data warehousing, business intelligence, and enterprise performance management solutions deliver the information, analysis, and efficiencies to propel your business ahead of the competition.

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