This document provides an overview of the tools and architecture of the Data-Applied.com platform. The tools allow for data import/export, pivoting, forecasting, correlations, outlier detection, associations, decisions, clusters, and similarity analysis. The architecture includes a web client, web service, distributed backend for task management, and SQL database. It also describes how the system handles users, workspaces, security, and data.

1. 4 Data-Applied.com: Technology Insight

2. Tools: DataImport data: CSV File, Excel File, SalesForce.com, Dynamics CRM

3. Tools: DataExport Data:CSV File

4. ToolsSuper Pivots: An XML based API allowing multiple levels of grouping, binning and aggregation.Tree Maps: An aspect-ratio optimization recursive layout algorithm.

5. ToolsForecasts: An optimized formulation of a specialized neural network with monte-carlo simulationMonte 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.

6. ToolsCorrelations: 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.

7. ToolsOutliers: An optimized formulation of the Bay and Schwabacher’s outlier detection algorithmAssociations:An optimized formulation of the apriori-all association rule algorithm.

8. ToolsDecisions: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

9. ToolsClusters: 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)

10. ToolsSimilarity: 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.

11. Architecture: SystemWeb ClientRuns within a browser, uses XML requests, visualization capabilities using Microsoft Silverlight, local data caching and compression.Web Servicesecure XML-based Web API, accept and process XML requests

12. Architecture: SystemBack-EndDistributed computing, manage task priorities, detect abandoned tasks, restart failed tasks, terminate long-running tasks, and synchronize task execution between nodesDatabaseSQL-based storage system,

13. Architecture: System

14. Architecture: System DataUsers, Workspaces, Rightsvisual CAPTCHA challenge, email confirmation, workspace sharing. Databases, Tables, FieldsMaster-slave configuration in databases,

15. Architecture: System DataNodes, Jobs, TasksKeys, Licenses, LogsComments, Downloads, Images, Settings

16. Architecture: System SecurityRight EnforcementLicense RestrictionsCryptographic Validations