Heuristics Gleaning from Massive Databases - A Model for a New Rapid Decision Era

Next era a massive database requires advanced computing mechanisms for gleaning knowledge from heterogeneous subordinate databases. Future decisions will increasingly rely on the ability to search through a large set of possibilities to optimally find, and easily formulate knowledge. Establishing such computing mechanisms is a vital development for future sustainability.This research involves in importance of a heuristic as being a category of knowledge that is convenient for the next era knowledge-based rapid decisions era. It introduced a model delves into massive databases to glean heuristics. The feasibility of model deployment is manifested via simulating case study. This work exploited inference and verification methodologies for a hidden heuristic crystallization. This research is elaborated in the following sections: section 1 introduces the basic concepts used within this research context. Section 2 discusses the difficulties and issues of deployment a heuristic-gleaning mechanism with massive databases. Section 3 exemplifies the exercised case study. Section 4 introduces heuristic gleaning system, which contains a schematic approach, major functions and interoperable components, and a main line algorithm. The conclusion and future work are given in section 5.