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• Data Quality and Master Data Management Resources

  - by Dejan Sarka

  Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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• blocking bad bots with robots.txt in 2012 [closed]

  - by Rachel Sparks

  does it still work good? I have this: # Generated using http://solidshellsecurity.com services # Begin block Bad-Robots from robots.txt User-agent: asterias Disallow:/ User-agent: BackDoorBot/1.0 Disallow:/ User-agent: Black Hole Disallow:/ User-agent: BlowFish/1.0 Disallow:/ User-agent: BotALot Disallow:/ User-agent: BuiltBotTough Disallow:/ User-agent: Bullseye/1.0 Disallow:/ User-agent: BunnySlippers Disallow:/ User-agent: Cegbfeieh Disallow:/ User-agent: CheeseBot Disallow:/ User-agent: CherryPicker Disallow:/ User-agent: CherryPickerElite/1.0 Disallow:/ User-agent: CherryPickerSE/1.0 Disallow:/ User-agent: CopyRightCheck Disallow:/ User-agent: cosmos Disallow:/ User-agent: Crescent Disallow:/ User-agent: Crescent Internet ToolPak HTTP OLE Control v.1.0 Disallow:/ User-agent: DittoSpyder Disallow:/ User-agent: EmailCollector Disallow:/ User-agent: EmailSiphon Disallow:/ User-agent: EmailWolf Disallow:/ User-agent: EroCrawler Disallow:/ User-agent: ExtractorPro Disallow:/ User-agent: Foobot Disallow:/ User-agent: Harvest/1.5 Disallow:/ User-agent: hloader Disallow:/ User-agent: httplib Disallow:/ User-agent: humanlinks Disallow:/ User-agent: InfoNaviRobot Disallow:/ User-agent: JennyBot Disallow:/ User-agent: Kenjin Spider Disallow:/ User-agent: Keyword Density/0.9 Disallow:/ User-agent: LexiBot Disallow:/ User-agent: libWeb/clsHTTP Disallow:/ User-agent: LinkextractorPro Disallow:/ User-agent: LinkScan/8.1a Unix Disallow:/ User-agent: LinkWalker Disallow:/ User-agent: LNSpiderguy Disallow:/ User-agent: lwp-trivial Disallow:/ User-agent: lwp-trivial/1.34 Disallow:/ User-agent: Mata Hari Disallow:/ User-agent: Microsoft URL Control - 5.01.4511 Disallow:/ User-agent: Microsoft URL Control - 6.00.8169 Disallow:/ User-agent: MIIxpc Disallow:/ User-agent: MIIxpc/4.2 Disallow:/ User-agent: Mister PiX Disallow:/ User-agent: moget Disallow:/ User-agent: moget/2.1 Disallow:/ User-agent: mozilla/4 Disallow:/ User-agent: Mozilla/4.0 (compatible; BullsEye; Windows 95) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows 95) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows 98) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows NT) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows XP) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows 2000) Disallow:/ User-agent: Mozilla/4.0 (compatible; MSIE 4.0; Windows ME) Disallow:/ User-agent: mozilla/5 Disallow:/ User-agent: NetAnts Disallow:/ User-agent: NICErsPRO Disallow:/ User-agent: Offline Explorer Disallow:/ User-agent: Openfind Disallow:/ User-agent: Openfind data gathere Disallow:/ User-agent: ProPowerBot/2.14 Disallow:/ User-agent: ProWebWalker Disallow:/ User-agent: QueryN Metasearch Disallow:/ User-agent: RepoMonkey Disallow:/ User-agent: RepoMonkey Bait & Tackle/v1.01 Disallow:/ User-agent: RMA Disallow:/ User-agent: SiteSnagger Disallow:/ User-agent: SpankBot Disallow:/ User-agent: spanner Disallow:/ User-agent: suzuran Disallow:/ User-agent: Szukacz/1.4 Disallow:/ User-agent: Teleport Disallow:/ User-agent: TeleportPro Disallow:/ User-agent: Telesoft Disallow:/ User-agent: The Intraformant Disallow:/ User-agent: TheNomad Disallow:/ User-agent: TightTwatBot Disallow:/ User-agent: Titan Disallow:/ User-agent: toCrawl/UrlDispatcher Disallow:/ User-agent: True_Robot Disallow:/ User-agent: True_Robot/1.0 Disallow:/ User-agent: turingos Disallow:/ User-agent: URLy Warning Disallow:/ User-agent: VCI Disallow:/ User-agent: VCI WebViewer VCI WebViewer Win32 Disallow:/ User-agent: Web Image Collector Disallow:/ User-agent: WebAuto Disallow:/ User-agent: WebBandit Disallow:/ User-agent: WebBandit/3.50 Disallow:/ User-agent: WebCopier Disallow:/ User-agent: WebEnhancer Disallow:/ User-agent: WebmasterWorldForumBot Disallow:/ User-agent: WebSauger Disallow:/ User-agent: Website Quester Disallow:/ User-agent: Webster Pro Disallow:/ User-agent: WebStripper Disallow:/ User-agent: WebZip Disallow:/ User-agent: WebZip/4.0 Disallow:/ User-agent: Wget Disallow:/ User-agent: Wget/1.5.3 Disallow:/ User-agent: Wget/1.6 Disallow:/ User-agent: WWW-Collector-E Disallow:/ User-agent: Xenu's Disallow:/ User-agent: Xenu's Link Sleuth 1.1c Disallow:/ User-agent: Zeus Disallow:/ User-agent: Zeus 32297 Webster Pro V2.9 Win32 Disallow:/

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Best way to implement user-powered data validation

  - by vegetables

  I run a product recommendation engine and I'm hitting a few snags. I'm looking to see if anyone has any recommendations on what I should do to minimize these issues. Here's how the site works: Users come to the site and are presented with product recommendations based on some criteria. If a user knows of a product that is not in our system, they can add it by providing the product name and manufacturer. We take that information, and: Hit one API to gather all the product meta-data (and to validate the product spelling, etc). If the product is not in this first API, we do not allow it in our system. Use the information from step 1 to hit another API for pricing information (gathered from many places online). For the sake of discussion, assume that I am searching both APIs in the most efficient/successful manner possible. For the most part, this works very well. I'd say ~80% of our data is perfectly accurate, but there are a few issues: Sometimes the pricing API (Step 2) doesn't have any information for the product. The way the pricing API is built, it will always return something (theoretically, the closest possible match), and there's no guarantee that the product name is spelled exactly the same way in both APIs, so there's no automated way of knowing if it's the right product. When the pricing API finds the right product, occasionally it has outdated, or even invalid pricing data (e.g. if it screen-scraped the wrong price from a website). Since the site was fairly small at first, I was able to manually verify every product that was added to the website. However, the site has grown to the point where this is taking several hours per day, and is just not efficient use of my time. So, my question is: Aside from hiring someone (or getting an intern) to validate all the data manually, what would be the best system of letting my userbase self-manage the data. Specifically, how can I allow users to edit the data while minimizing the risk of someone ambushing my website, or accidentally setting the data incorrectly.

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• Global User Experience Research: Mobile

  - by ultan o'broin

  A shout out to the usableapps.oracle.com blog article Going Native to Understand Mobile Workers. Oracle is a global company and with all that revenue coming from outside the US, international usability research is essential. So read up about how the Applications User Experience team went about this important user-centered ethnographic research. Personalization is king in the mobile space. Going native is a great way to uncover exactly what users want as they work and use their mobile devices, but you need to do it worldwide!

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• Move data from others user accounts in my user account

  - by user118136

  I had problems with compiz setting and I make multiple accounts, now I want to transfer my information from all deleted users in my current account, some data I can not copy because I am not right to read, I type in terminal "sudo nautilus" and I get the permission for read, but the copied data is available only for superusers and I must charge the permissions for each file and each folder. How I can copy the information with out the superuser rights OR how I can charge the permissions for selected folder and all files and folders included in it?

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• Conversation as User Assistance

  - by ultan o'broin

  Applications User Experience members (Erika Web, Laurie Pattison, and I) attended the User Assistance Europe Conference in Stockholm, Sweden. We were impressed with the thought leadership and practical application of ideas in Anne Gentle's keynote address "Social Web Strategies for Documentation". After the conference, we spoke with Anne to explore the ideas further. Anne Gentle (left) with Applications User Experience Senior Director Laurie Pattison In Anne's book called Conversation and Community: The Social Web for Documentation, she explains how user assistance is undergoing a seismic shift. The direction is away from the old print manuals and online help concept towards a web-based, user community-driven solution using social media tools. User experience professionals now have a vast range of such tools to start and nurture this "conversation": blogs, wikis, forums, social networking sites, microblogging systems, image and video sharing sites, virtual worlds, podcasts, instant messaging, mashups, and so on. That user communities are a rich source of user assistance is not a surprise, but the extent of available assistance is. For example, we know from the Consortium for Service Innovation that there has been an 'explosion' of user-generated content on the web. User-initiated community conversations provide as much as 30 times the number of official help desk solutions for consortium members! The growing reliance on user community solutions is clearly a user experience issue. Anne says that user assistance as conversation "means getting closer to users and helping them perform well. User-centered design has been touted as one of the most important ideas developed in the last 20 years of workplace writing. Now writers can take the idea of user-centered design a step further by starting conversations with users and enabling user assistance in interactions." Some of Anne's favorite examples of this paradigm shift from the world of traditional documentation to community conversation include: Writer Bob Bringhurst's blog about Adobe InDesign and InCopy products and Adobe's community help The Microsoft Development Network Community Center ·The former Sun (now Oracle) OpenDS wiki, NetBeans Ruby and other community approaches to engage diverse audiences using screencasts, wikis, and blogs. Cisco's customer support wiki, EMC's community, as well as Symantec and Intuit's approaches The efforts of Ubuntu, Mozilla, and the FLOSS community generally Adobe Writer Bob Bringhurst's Blog Oracle is not without a user community conversation too. Besides the community discussions and blogs around documentation offerings, we have the My Oracle Support Community forums, Oracle Technology Network (OTN) communities, wiki, blogs, and so on. We have the great work done by our user groups and customer councils. Employees like David Haimes reach out, and enthusiastic non-employee gurus like Chet Justice (OracleNerd), Floyd Teter and Eddie Awad provide great "how-to" information too. But what does this paradigm shift mean for existing technical writers as users turn away from the traditional printable PDF manual deliverables? We asked Anne after the conference. The writer role becomes one of conversation initiator or enabler. The role evolves, along with the process, as the users define their concept of user assistance and terms of engagement with the product instead of having it pre-determined. It is largely a case now of "inventing the job while you're doing it, instead of being hired for it" Anne said. There is less emphasis on formal titles. Anne mentions that her own title "Content Stacker" at OpenStack; others use titles such as "Content Curator" or "Community Lead". However, the role remains one essentially about communications, "but of a new type--interacting with users, moderating, curating content, instead of sitting down to write a manual from start to finish." Clearly then, this role is open to more than professional technical writers. Product managers who write blogs, developers who moderate forums, support professionals who update wikis, rock star programmers with a penchant for YouTube are ideal. Anyone with the product knowledge, empathy for the user, and flair for relationships on the social web can join in. Some even perform these roles already but do not realize it. Anne feels the technical communicator space will move from hiring new community conversation professionals (who are already active in the space through blogging, tweets, wikis, and so on) to retraining some existing writers over time. Our own research reveals that the established proponents of community user assistance even set employee performance objectives for internal content curators about the amount of community content delivered by people outside the organization! To take advantage of the conversations on the web as user assistance, enterprises must first establish where on the spectrum their community lies. "What is the line between community willingness to contribute and the enterprise objectives?" Anne asked. "The relationship with users must be managed and also measured." Anne believes that the process can start with a "just do it" approach. Begin by reaching out to existing user groups, individual bloggers and tweeters, forum posters, early adopter program participants, conference attendees, customer advisory board members, and so on. Use analytical tools to measure the level of conversation about your products and services to show a return on investment (ROI), winning management support. Anne emphasized that success with the community model is dependent on lowering the technical and motivational barriers so that users can readily contribute to the conversation. Simple tools must be provided, and guidelines, if any, must be straightforward but not mandatory. The conversational approach is one where traditional style and branding guides do not necessarily apply. Tools and infrastructure help users to create content easily, to search and find the information online, read it, rate it, translate it, and participate further in the content's evolution. Recognizing contributors by using ratings on forums, giving out Twitter kudos, conference invitations, visits to headquarters, free products, preview releases, and so on, also encourages the adoption of the conversation model. The move to conversation as user assistance is not free, but there is a business ROI. The conversational model means that customer service is enhanced, as user experience moves from a functional to a valued, emotional level. Studies show a positive correlation between loyalty and financial performance (Consortium for Service Innovation, 2010), and as customer experience and loyalty become key differentiators, user experience professionals cannot explore the model's possibilities. The digital universe (measured at 1.2 million petabytes in 2010) is doubling every 12 to 18 months, and 70 percent of that universe consists of user-generated content (IDC, 2010). Conversation as user assistance cannot be ignored but must be embraced. It is a time to manage for abundance, not scarcity. Besides, the conversation approach certainly sounds more interesting, rewarding, and fun than the traditional model! I would like to thank Anne for her time and thoughts, and recommend that all user assistance professionals read her book. You can follow Anne on Twitter at: http://www.twitter.com/annegentle. Oracle's Acrolinx IQ deployment was used to author this article.

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• Using Completed User Stories to Estimate Future User Stories

  - by David Kaczynski

  In Scrum/Agile, the complexity of a user story can be estimated in story points. After completing some user stories, a programmer or team of programmers can use those experiences to better estimate how much time it might take to complete a future user story. Is there a methodology for breaking down the complexity of user stories into quantifiable or quantifiable attributes? For example, User Story X requires a rich, new view in the GUI, but User Story X can perform most of its functionality using existing business logic on the server. On a scale of 1 to 10, User Story X has a complexity of 7 on the client and a complexity of 2 on the server. After User Story X is completed, someone asks how long would it take to complete User Story Y, which has a complexity of 3 on the client and 6 on the server. Looking at how long it took to complete User Story X, we can make an educated estimate on how long it might take to complete User Story Y. I can imagine some other details: The complexity of one attribute (such as complexity of client) could have sub-attributes, such as number of steps in a sequence, function points, etc. Several other attributes that could be considered as well, such as the programmer's familiarity with the system or the number of components/interfaces involved These attributes could be accumulated into some sort of user story checklist. To reiterate: is there an existing methodology for decomposing the complexity of a user story into complexity of attributes/sub-attributes, or is using completed user stories as indicators in estimating future user stories more of an informal process?

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• Data Quality Through Data Governance

  Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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• SQLAuthority News – Links to Book On Line – Data Mining Algorithms (Analysis Services – Data Mining)

  - by pinaldave

  I have quite often received request for the Data Mining Algorithms details. Book Online has wonderful resources for the same. I suggest to read them here. Data Mining Algorithms (Analysis Services – Data Mining) The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set [...]

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• The Best Ways to Lock Down Your Multi-User Computer

  - by Lori Kaufman

  Whether you’re sharing a computer with other family members or friends at home, or securing computers in a corporate environment, there may be many reasons why you need to protect the programs, data, and settings on the computers. This article presents multiple ways of locking down a Windows 7 computer, depending on the type of usage being employed by the users. You may need to use a combination of several of the following methods to protect your programs, data, and settings. How to Stress Test the Hard Drives in Your PC or Server How To Customize Your Android Lock Screen with WidgetLocker The Best Free Portable Apps for Your Flash Drive Toolkit

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• SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

  - by pinaldave

  There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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• Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

  - by Pinal Dave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Basics of Big Data Architecture – Day 4 of 21

  - by Pinal Dave

  In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Working with Temporal Data in SQL Server

  - by Dejan Sarka

  My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• Redirect network logs from syslog to another file

  - by w0rldart

  I keep logging way to much info (not needed, for now) in my syslog, and not daily or hourly... but instant. If I want to watch for something in my syslog I just can't because the network log keeps interfering. So, how can I redirect network logs to another file and/or stop logging it? Dec 10 17:01:33 user kernel: [ 8716.000587] MediaState is connected Dec 10 17:01:33 user kernel: [ 8716.000599] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:33 user kernel: [ 8716.000601] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:33 user kernel: [ 8716.000612] rt28xx_get_wireless_stats ---> Dec 10 17:01:33 user kernel: [ 8716.000615] <--- rt28xx_get_wireless_stats Dec 10 17:01:39 user kernel: [ 8722.000714] MediaState is connected Dec 10 17:01:39 user kernel: [ 8722.000729] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:39 user kernel: [ 8722.000732] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:39 user kernel: [ 8722.000747] rt28xx_get_wireless_stats ---> Dec 10 17:01:39 user kernel: [ 8722.000751] <--- rt28xx_get_wireless_stats Dec 10 17:01:44 user kernel: [ 8726.904025] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:01:45 user kernel: [ 8728.003138] MediaState is connected Dec 10 17:01:45 user kernel: [ 8728.003153] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:45 user kernel: [ 8728.003157] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:45 user kernel: [ 8728.003171] rt28xx_get_wireless_stats ---> Dec 10 17:01:45 user kernel: [ 8728.003175] <--- rt28xx_get_wireless_stats Dec 10 17:01:51 user kernel: [ 8734.004066] MediaState is connected Dec 10 17:01:51 user kernel: [ 8734.004079] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:51 user kernel: [ 8734.004082] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:51 user kernel: [ 8734.004096] rt28xx_get_wireless_stats ---> Dec 10 17:01:51 user kernel: [ 8734.004099] <--- rt28xx_get_wireless_stats Dec 10 17:01:57 user kernel: [ 8740.004108] MediaState is connected Dec 10 17:01:57 user kernel: [ 8740.004119] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:01:57 user kernel: [ 8740.004121] ==>rt_ioctl_giwfreq 11 Dec 10 17:01:57 user kernel: [ 8740.004132] rt28xx_get_wireless_stats ---> Dec 10 17:01:57 user kernel: [ 8740.004135] <--- rt28xx_get_wireless_stats Dec 10 17:01:57 user kernel: [ 8740.436021] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:03 user kernel: [ 8746.005280] MediaState is connected Dec 10 17:02:03 user kernel: [ 8746.005294] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:03 user kernel: [ 8746.005298] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:03 user kernel: [ 8746.005312] rt28xx_get_wireless_stats ---> Dec 10 17:02:03 user kernel: [ 8746.005315] <--- rt28xx_get_wireless_stats Dec 10 17:02:09 user kernel: [ 8752.004790] MediaState is connected Dec 10 17:02:09 user kernel: [ 8752.004804] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:09 user kernel: [ 8752.004808] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:09 user kernel: [ 8752.004821] rt28xx_get_wireless_stats ---> Dec 10 17:02:09 user kernel: [ 8752.004825] <--- rt28xx_get_wireless_stats Dec 10 17:02:15 user kernel: [ 8757.984031] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:15 user kernel: [ 8758.004078] MediaState is connected Dec 10 17:02:15 user kernel: [ 8758.004094] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:15 user kernel: [ 8758.004097] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:15 user kernel: [ 8758.004112] rt28xx_get_wireless_stats ---> Dec 10 17:02:15 user kernel: [ 8758.004116] <--- rt28xx_get_wireless_stats Dec 10 17:02:16 user kernel: [ 8759.492017] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:19 user kernel: [ 8762.002179] SCANNING, suspend MSDU transmission ... Dec 10 17:02:19 user kernel: [ 8762.004291] MlmeScanReqAction -- Send PSM Data frame for off channel RM, SCAN_IN_PROGRESS=1! Dec 10 17:02:19 user kernel: [ 8762.025055] SYNC - BBP R4 to 20MHz.l Dec 10 17:02:19 user kernel: [ 8762.027249] RT35xx: SwitchChannel#1(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF1, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.170206] RT35xx: SwitchChannel#2(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF1, K=0x07, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.318211] RT35xx: SwitchChannel#3(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF2, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.462269] RT35xx: SwitchChannel#4(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF2, K=0x07, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.606229] RT35xx: SwitchChannel#5(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF3, K=0x02, R=0x02 Dec 10 17:02:19 user kernel: [ 8762.750202] RT35xx: SwitchChannel#6(RF=8, Pwr0=30, Pwr1=25, 2T), N=0xF3, K=0x07, R=0x02 Dec 10 17:02:20 user kernel: [ 8762.894217] RT35xx: SwitchChannel#7(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF4, K=0x02, R=0x02 Dec 10 17:02:20 user kernel: [ 8763.038202] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:20 user kernel: [ 8763.040194] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:20 user kernel: [ 8763.040199] BAR(1) : Tid (0) - 03a3:037e Dec 10 17:02:20 user kernel: [ 8763.040387] SYNC - End of SCAN, restore to channel 11, Total BSS[03] Dec 10 17:02:20 user kernel: [ 8763.040400] ScanNextChannel -- Send PSM Data frame Dec 10 17:02:20 user kernel: [ 8763.040402] bFastRoamingScan ~~~~~~~~~~~~~ Get back to send data ~~~~~~~~~~~~~ Dec 10 17:02:20 user kernel: [ 8763.040405] SCAN done, resume MSDU transmission ... Dec 10 17:02:20 user kernel: [ 8763.047022] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:20 user kernel: [ 8763.047026] BAR(1) : Tid (0) - 03a3:03a5 Dec 10 17:02:21 user kernel: [ 8763.898130] bImprovedScan ............. Resume for bImprovedScan, SCAN_PENDING .............. Dec 10 17:02:21 user kernel: [ 8763.898143] SCANNING, suspend MSDU transmission ... Dec 10 17:02:21 user kernel: [ 8763.900245] MlmeScanReqAction -- Send PSM Data frame for off channel RM, SCAN_IN_PROGRESS=1! Dec 10 17:02:21 user kernel: [ 8763.921144] SYNC - BBP R4 to 20MHz.l Dec 10 17:02:21 user kernel: [ 8763.923339] RT35xx: SwitchChannel#8(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF4, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8763.996019] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:21 user kernel: [ 8764.066221] RT35xx: SwitchChannel#9(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF5, K=0x02, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.210212] RT35xx: SwitchChannel#10(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF5, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.215536] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.215542] BAR(1) : Tid (0) - 0457:0452 Dec 10 17:02:21 user kernel: [ 8764.244000] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.244004] BAR(1) : Tid (0) - 0459:0456 Dec 10 17:02:21 user kernel: [ 8764.253019] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.253023] BAR(1) : Tid (0) - 045c:0458 Dec 10 17:02:21 user kernel: [ 8764.256677] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.256681] BAR(1) : Tid (0) - 045c:045b Dec 10 17:02:21 user kernel: [ 8764.259785] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.259788] BAR(1) : Tid (0) - 045d:045b Dec 10 17:02:21 user kernel: [ 8764.280467] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.280471] BAR(1) : Tid (0) - 045f:045c Dec 10 17:02:21 user kernel: [ 8764.282189] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:21 user kernel: [ 8764.282192] BAR(1) : Tid (0) - 045f:045e Dec 10 17:02:21 user kernel: [ 8764.354204] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.356408] ScanNextChannel():Send PWA NullData frame to notify the associated AP! Dec 10 17:02:21 user kernel: [ 8764.498202] RT35xx: SwitchChannel#12(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x07, R=0x02 Dec 10 17:02:21 user kernel: [ 8764.642210] RT35xx: SwitchChannel#13(RF=8, Pwr0=30, Pwr1=28, 2T), N=0xF7, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.790229] RT35xx: SwitchChannel#14(RF=8, Pwr0=30, Pwr1=28, 2T), N=0xF8, K=0x04, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.934238] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.935243] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:22 user kernel: [ 8764.935249] BAR(1) : Tid (0) - 048e:0485 Dec 10 17:02:22 user kernel: [ 8764.936423] SYNC - End of SCAN, restore to channel 11, Total BSS[05] Dec 10 17:02:22 user kernel: [ 8764.936436] ScanNextChannel -- Send PSM Data frame Dec 10 17:02:22 user kernel: [ 8764.936440] SCAN done, resume MSDU transmission ... Dec 10 17:02:22 user kernel: [ 8764.940529] RT35xx: SwitchChannel#11(RF=8, Pwr0=29, Pwr1=26, 2T), N=0xF6, K=0x02, R=0x02 Dec 10 17:02:22 user kernel: [ 8764.942178] CntlEnqueueForRecv(): BAR-Wcid(1), Tid (0) Dec 10 17:02:22 user kernel: [ 8764.942182] BAR(1) : Tid (0) - 0493:048e Dec 10 17:02:22 user kernel: [ 8764.942715] CNTL - All roaming failed, restore to channel 11, Total BSS[05] Dec 10 17:02:22 user kernel: [ 8764.948016] MMCHK - No BEACON. restore R66 to the low bound(56) Dec 10 17:02:22 user kernel: [ 8764.948307] ===>rt_ioctl_giwscan. 5(5) BSS returned, data->length = 1111 Dec 10 17:02:23 user kernel: [ 8766.048073] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:23 user kernel: [ 8766.552034] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:27 user kernel: [ 8770.001180] MediaState is connected Dec 10 17:02:27 user kernel: [ 8770.001197] ==>rt_ioctl_giwmode(mode=2) Dec 10 17:02:27 user kernel: [ 8770.001201] ==>rt_ioctl_giwfreq 11 Dec 10 17:02:27 user kernel: [ 8770.001219] rt28xx_get_wireless_stats ---> Dec 10 17:02:27 user kernel: [ 8770.001223] <--- rt28xx_get_wireless_stats Dec 10 17:02:28 user kernel: [ 8771.564020] QuickDRS: TxTotalCnt <= 15, train back to original rate Dec 10 17:02:29 user kernel: [ 8772.064031] QuickDRS: TxTotalCnt <= 15, train back to original rate

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

  - by Pinal Dave

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

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• Unstructured Data - The future of Data Administration

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

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• Developing an analytics's system processing large amounts of data - where to start

  - by Ryan

  Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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

  - by Pinal Dave

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

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• Prevent Windows 7 User Accounts from accessing files in other User Accounts

  - by Mantis

  I'm trying to set up another User Account on my Windows 7 Professional laptop for use by another person. I do not want that person to have access to any of the files in my User Account on the same machine. This machine has a single hard disk formatted with NTFS. User accounts data is stored in the default location, C:\Users. I use the computer with a Standard Account (not an Administrator). Let's call my user account "User A." I have given the new user a Standard Account. Let's call the new user's account "User B." To be clear, I want User B to have the ability to log in to her account, to use the computer, but to be unable to access any of the files in the User A account on the same machine. Currently, User B cannot use Windows Explorer to navigate to the location C:\Users\User A. However, by simply using Windows Search, User B can easily find and open documents saved in C:\Users\User A\Documents. After opening a document, that document's full path appears in "Recent Places" in Windows Explorer, and the document appears as a file that can be opened using the "Recent" feature in Word 2010. This is not the desired behavior. User B should not have the ability to see any documents using Windows Search or anything else. I have attempted to set permissions using the following procedure. Using an Administrator account, navigate to C:\Users and right-click on the "User A" folder. Select "Properties." In the "User A Properties" window that appears, click the "Security" tab. Click the "Edit..." button to change permissions. IN the "Permissions for User B" window that appears, under "Group or User Names," select User B. Under "Permissions for User B", check the box under the "Deny" column for the "Full Control" row. Ensure that the "Deny" box is automatically checked for all the other rows, and then click "OK." The system should then begin working. The process could take several minutes. When I followed this procedure, I received several "Access Denied" errors, suggesting that the system was unable to set the permissions as I had directed. I think this might be one of the reasons why User B is still able to access files in User A's account folders. Is there any other way I could accomplish my goal here? Thank you.

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

  - by nikolaosk

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

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• Big Data&rsquo;s Killer App&hellip;

  - by jean-pierre.dijcks

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

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