50 Great Examples of Data Visualization… Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information. And trying to find related content can also be difficult, depending on what data you’re looking for. But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner. Below are 50 of the best data visualizations and tools for creating your own visualizations out there, covering everything from Digg activity to network connectivity to what’s currently happening on Twitter. Music, Movies and Other Media Narratives 2.0 visualizes music. Liveplasma is a music and movie visualization app that aims to help you discover other musicians or movies you might enjoy. Tuneglue is another music visualization service. MusicMap is similar to TuneGlue in its interface, but seems slightly more intuitive. Digg, Twitter, Delicious, and Flickr Internet Visualizations
Data Visualization, Design and Informat… Perl Perl 5.005 or greater and the following CPAN modules GD compiled with True Type font support Config::General DBI File::Basename Pod::Usage Math::Bezier, if you would like to use Bezier curves to draw your table links SQL::Translator, if you would like to parse SQL schema dumps Generic SQL database support You will need access to a SQL database and permission to list tables and table fields for the database you wish to generate a schema ball. MySQL Shemaball was designed originally for MySQL and works with MySQL out of the box. True Type Fonts Your version of GD must be compiled with support for True Type fonts.
Walrus - Gallery Visualization & Naviga… Walrus - Gallery: Visualization & Navigation These screenshots and animations are simply intended to give a feel for what visualization and navigation in Walrus are like. The data shown are not necessarily meaningful in themselves. Click on a thumbnail for the larger version. Skitter Monitors champagne (9,175 nodes and 15,519 links) riesling (54,893 nodes and 79,409 links) lhr (535,102 nodes and 601,678 links) CVS Repository (18,474 nodes and 18,473 links) Various Directory Trees Round-Trip Time Measurements (63,631 nodes and 63,630 links) A description of this data is available. CodeRed Infections (321,986 nodes and 321,985 links) A description of this data is available. Animations These animated GIFs show graph navigation in Walrus. The CVS repository. [1.0MB] The lhr skitter monitor. [3.0MB] The riesling skitter monitor. [3.7MB] The riesling skitter monitor. [5.0MB] [Warning: 32.5MB] [Warning: 15.4MB]
Microsoft OLAP Blog by Hilmar Buchta dimensional modeling When it comes to dimension design a common question is about dealing with attributes that are changing over time. A frequently used pattern for such attributes was developed by Ralph Kimball with his concept of slowly changing dimensions (SCD). One aspect is to handle source systems that simply overwrite their master data, while you want to preserve past attribute properties for your analytics. Type 1:changes are directly overwritten in the dimension table. Type 2: Past values for dimension data is kept using date from/to columns and/or a current row indicator There are more types and also combinations of types (hybrid types). When talking to other BI architects I frequently hear the opinion that type 2 should be used for almost every attribute. 1. Usually, we don’t want corrections, like a typo in a customer’s name, to be kept historically. Instead of we want to see So, the problem with corrections is obvious. In special cases, things might be more complicated. 2. 3.
List of suggested datasets to test PowerPivot Do you want to test what PowerPivot can do, but cannof find data that you could use for your tests and blog about? Might I suggest for you a few websites: Microsoft Contoso BI Demo Dataset for Retail Industry - Microsoft SQL Server 2008 database backup. The Contoso BI Demo dataset is used to demonstrate DW/BI functionalities across the entire Microsoft Office product family. This dataset includes C-level, sales/marketing, IT, and common finance scenarios for the retail industry and support map integration. In addition, this dataset offers large volumes of transactions from OLTP and well-structured aggregations from OLAP, along with reference and dimension data.List of available ODATA services (data feeds): workbook with sample Contoso BI data.