Tools Super Pivots: An XML based API allowing multiple levels of grouping, binning and aggregation. Tree Maps: An aspect-ratio optimization recursive layout algorithm.
Tools Forecasts: An optimized formulation of a specialized neural network with monte-carlo simulation Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems.
Tools Correlations: A parallel formulation of Pearson product-moment correlation coefficient algorithm Pearson product-moment correlation coefficient is a measure of the correlation between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables.
Tools Outliers: An optimized formulation of the Bay and Schwabacher’s outlier detection algorithm Associations:An optimized formulation of the apriori-all association rule algorithm.
Tools Decisions:A parallel formulation of an algorithm based on information gain (discrete decision trees). A formulation of the Kruskal-Wallis statistic test (numeric trees) The Kruskal–Wallis one-way analysis of variance by ranks (named after William Kruskal and W. Allen Wallis) is a non-parametric method for testing equality of population medians among groups. It is identical to a one-way analysis of variance with the data replaced by their ranks
Tools Clusters: An optimized formulation for the BIRCH clustering algorithm. BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints)
Tools Similarity: A parallel formulation of a Kohonen artificial neural network. Kohonen self-organizing network is a self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these.
Architecture: System Web Client Runs within a browser, uses XML requests, visualization capabilities using Microsoft Silverlight, local data caching and compression. Web Service secure XML-based Web API, accept and process XML requests
Architecture: System Back-End Distributed computing, manage task priorities, detect abandoned tasks, restart failed tasks, terminate long-running tasks, and synchronize task execution between nodes Database SQL-based storage system,
Architecture: System Data Users, Workspaces, Rights visual CAPTCHA challenge, email confirmation, workspace sharing. Databases, Tables, Fields Master-slave configuration in databases,
Architecture: System Data Nodes, Jobs, Tasks Keys, Licenses, Logs Comments, Downloads, Images, Settings
Architecture: System Security Right Enforcement License Restrictions Cryptographic Validations