TECHNOLOGY AREAS: Information Systems

OBJECTIVE: Develop an advanced next generation database model that uses a neurologically inspired design based on data cell and link cell synaptic interactions to support new and unique advanced data analysis, entity relationship association, and associative search capabilities. This will replace the traditional relational database management system (RDBMS) indexing methods.

DESCRIPTION: Current data mining and data analysis applications are designed primarily to address commercial business intelligence requirements. Business Intelligence (BI) has been defined as, "�a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining." Conventional RDBMS-based BI applications are records-based, transaction oriented, and demand massive resource overhead for table joins and index management. A cell-based approach to database design would enable the data model to store, process, and manipulate data much like the human brain, thereby permitting more effective and flexible use of the database and the underlying data. Performance gains are possible with cell based indexing which captures, aggregates, and handles data in native "cellular format" cells vice traditional indexes. Additionally, cellular database techniques facilitate "value pooling," a dimension that aggregates clusters of cell "associations." Entity relationship of "link cells" provides a powerful link analysis and knowledge discovery function, and opens the possibility of further advances in innovative data analysis capabilities.

PHASE I: Develop a prototype cellular datebase concept demonstrator capable of advanced data mining, link analysis, associative search, and entity relationship association using both structured and non-structured data.

PHASE II: Finalize design from Phase I Concept. Evaluate the prototype in a limited objective experiment to validate database proof of concept and obtain operational feedback from analysts. Incorporate lessons learned into prototype development.

PHASE III. Based on Phase II outcome, test revised database application in major operational exercise or experiment event. Upon successful conclusion, initiate efforts to transition technology to commercial sector and military high-value knowledge discovery (KD) initiatives.

PRIVATE SECTOR COMMERCIAL POTENTIAL: Relational database technology is ill-suited for many data analysis applications. Data is a genuinely valuable asset and BI applications strive to covert previously raw, un-discovered corporate knowledge into actionable information of value to decision makers. A next generation cell structured database design presents a break through for BI because it greatly enhances existing data analysis capabilities.

REFERENCES:
- 1) Future Trends in Data Management, Michael Scofield, dated 22 Feb 2005 @ http://www.mlgsca.mlanet.org
- 2) Anthology from VLDB (Proceedings from International Conference on Very Large Data Bases, Framingham MA 22-24 Sept 1975 @ http://www.informatik.uni-trier.de
- 3) Data Mining Techniques, Neural Networks, courtesy of StatSoft @ http://www.statsoft.com/textbook/stdatmin.html

KEYWORDS: Cell-Generation; Business Intelligence (BI); Knowledge Discovery; Novel Advanced Data Mining; Link Analysis; Entity relationship association; Neurological Design.

TPOC: Mike Egnor
Phone: (757)203-5692
Fax:
Email: [email protected]
2nd TPOC: Hans Ingebrigsten
Phone: (757)836-7702
Fax:
Email: [email protected]

** TOPIC AUTHOR (TPOC) **

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