Service Oriented Architecture (SOA) Framework Overview
Challenge: Turning Data into Knowledge

The Vision:

Real-time capture and store of Email, Document, Media and Transactions into single, compliant and eco-friendly database for extreme analytics needed by Fraud, eDiscovery, Security, Compliance and other tools to instantly complete previously impossible tasks.

Technical Concept:

EFN Enterprises (EFN) delivers a Service-Oriented Architecture (SOA) that allows for the data fusion, integration, communication, visualization, manipulation and evaluation of disparate data sources. Communication of the SOA Architecture involves data-passing between EFN’s Commercial, off-the-shelf (COTS) software applications coupled customized routines for specialized entity data types. Inter-application communication for data integrity is performed via applets which are tightly coupled between the applications. EFN SOA architecture leverages multiple programming interfaces (APIs) for .NET, Java, and C++ for this tightly coupled applet integration. These APIs not only include detailed documentation and examples but also a series of high- level visual controls that make it easy for even the casual programmer to integrate their routines into the application interface.

EFN SOA framework allows for management of both structured and unstructured data. As a SOA environment, EFN framework is an open standard, extensible framework with a rich library of core services, as well as the ability to plug in additional third party services. In addition, the EFN SOA framework provides a familiar desktop metaphor with contemporary browser-based Web 2.0 technology, and allows analysts to be in control of their data.

EFN SOA Framework provides for the flexible capture, storage and management of data to allow the discovery, sharing and collaborative evolution of knowledge. It can store any type of data. This allows all experimental inputs, processing scripts and directives, processed data etc. to be catalogued, found and acted upon. The ability to access and analyze data from different sources, or to federate access to data that arrives from disparate sources (subject to the security policy) using a common platform allows for an integrated view of all data from multiple sources.

Metrics

Metadata templates can be defined, refined and re-used at any time. Controlled vocabularies and classifications schemes can be defined. On-line meetings can be established and other researchers invited to view and comment on research data. Collaboration may be real-time, ad-hoc, or a combination of both. Searches, data collections and other configuration data can be saved with your profile for future reference.

EFN SOA Desktop enables researchers, Engineers and Analyst to manage their data from anywhere, secure in the knowledge that valuable data is stored in an enterprise repository. It is secure, backed up and may be replicated for disaster recovery.

Citable Identification

EFN SOA Framework provides services to uniquely identify and reference assets in a distributed environment. EFN SOA Framework can be configured to use a single naming authority or can use a distributed naming authority when system availability over a wide-area network is critical. Citable identification is compatible with global schemes such as HANDLE (www.handle.net) or DOI (www.doi.org).

Federation of the Data Base

A federation might occur where different organizations or departments provide separate physical storage, perhaps for specific types of data. Two or more servers may be configured in a federation allowing transparent federated queries and access to local and remote data. Federation of repositories can happen at any time that there is a need to collaborate. A server may participate in different federations; e.g. for different virtual organizations or research projects.

Federated View to Bridge Content Silos

The key criterion is for all data to be in a non-proprietary, extensible environment to allow quick and programmatic federation between disparate data sources. EFN SOA Framework is precisely this. The database itself is a unique hybrid of binary-XML in an object data cloud. There is no need for a rigid schema or table structure. Thus, additions to the metadata schema or incorporation of additional types of metadata from new sources can be easily absorbed into the system without needing to modify any existing data. As binary-XML, performance of the database is orders of magnitude faster than a comparable relational database, and occupies about 10% of the storage footprint.

This ability to scale and the ability to aggregate multiple metadata schemas are key to providing solutions that bridge content silos. The problem is that each silo is designed and built primarily to be the best solution for its mission. The result is each content source has its own metadata schema and conventions, imagery is in multiple and sometimes incompatible formats and each silo have its own purpose- built IT infrastructure. Each of these silos is mission specific, and tailored to the needs of its primary mission and users. The problem, however, is that the mission-specific attributes that make each of the silos effective also create serious impediments to timely collaboration between them.

Thus, an area of interest in one image library could be easily cross referenced from other sources to provide analysts with:

1) Immediate access to richer spectrum of primary results,
2) The ability to layer additional SOA services to aid in metadata generation and refining the queries down to actionable results.

Not only would this enable collaboration on real-time content streams, but also it would enable queries to correlate disparate data from archives as well.

Distributed Database

Another key attribute of the system is that the database itself can be distributed. Thus, remote EFN SOA nodes at Silo A, Silo B and Silo C for example could be configured to pull in metadata from each. The source silo’s database and operations would not need to change to accommodate this. The extent of data and metadata shared would be determined according to the source and missions, and would be implemented in either a push or pull methodology, depending on the source.

Data Types

Our SOA Architecture processes all forms of data; regardless of source location and or data type.

• Over 30 various email archive formats
• Automated Speech Recognition
• Real-time automatic transcription of voicemails and other audio records to text
• Videos
  
  • Index Video (Intelligent indexing of video and audio files an images; i.e., movie, surveillance video, static images, etc.)
• Automated and function-rich scanning of paper documents into electronic format with OCR
• Structured Data BasesUnstructured Data Bases
• Social Networking ForumsComplex Networking Forums
• Grid DataText Data and Text Datasets
• Hyper-Spectral DataMulti-Spectral Data
• Acoustic DataSensor Array Data
• Rich Spatial Functionality Data
• 3D Data
  
  • This includes satellite imagery for the world; a worldwide shaded relief map; and a seamless, multiscale street map with highway data for the world and local street-level data for the United States and Canada as well as political world, physical world, world protected areas, and historical world globes and basemaps.
  • Users can view large datasets from multiple viewpoints, query a surface, create realistic perspective images.
  • Users can also drape raster and vector data over a surface and extrude two-dimensional features to three dimensions using attribute data.
  • Users can perform viewshed and line-of-sight analysis, spot height interpolation, profiling, and steepest path determination.
  • Users can model subsurface features, such as wells, mines, and groundwater, and construct models to analyze results in three dimensions.
  • User-defined animations, using flight paths, key frames, and other interactive tools, allow the user to convey a message or concept that can easily be saved to standard video formats such as MPEG, AVI, or QuickTime.
• Military Data
• Spatial Data
• Specialized Modeling Ingestion
  
  Spatial Modeling
  Sensitivity Modeling
  Site Suitability Modeling Solar

  Capability Modeling
  Predictive Modeling
  Radiation Modeling
• Specialized Analysis Ingestion Data
  
  Site Location Analysis
  Crop Yield Analysis
  Hydrologic Modeling/Analysis

  Trade Area Mapping
  Demographic Analysis
  Historical or Real-Time Data
• Provides temporal data visualization and analysis capabilities by defining events including time and location.
  
  • Users can record, play back, and conduct temporal analysis, including data clock charting and temporal offset.
  • Monitoring and analysis actions are available, such as highlight, suppress and filter.
  • View and analyze existing temporal data, which can be set up with future time windows (for mission planning) or past time windows (for historical data analysis).
• Advanced geoprocessing and gis analytic capabilities such as viewshed analysis and terrain profiling.
• Optional extensions for data interoperability, network-based spatial analysis, spatial modeling, and three-dimensional modeling.

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