Proposal: CMS-0122193 PI: Joachim Hammer Institution: University of Florida Date: July 21, 2001

ABSTRACT CMS0122193 - "Flexible Production Networks Phase II: SEEK - Scalable Extraction of Enterprise Knowledge"

This grant provides funding to support development of new theory and technologies to allow the seamless, rapid interconnection of diverse legacy systems with enterprise decision support tools to allow extraction of data and knowledge across those legacy systems. Such a capability is critically important to allow rapid configuration of computing systems that support extended enterprise collaboration. The application domain is the flexible production network. Flexible production networks (FPNs) consist of firms that operate in a project oriented, customized production environment. Most construction (totaling approximately 8% of GNP) and growing parts of manufacturing industry operate in this form. Characterized by rapid assembly and disassembly of business networks based on project needs, FPNs are particularly well-suited users for the proposed research.

We call our technology SEEK - Scalable Extraction of Enterprise Knowledge. Knowledge extraction is difficult because data resident in the firm does not map directly to the input needed by a decision support system. Existing approaches do not support SEEK's fundamental capabilities: (1) rapid connection to legacy data and applications with little programmatic setup; and (2) linking source knowledge with decision support tools not natively available to the source. Our novel approach to provide these capabilities is to use encoded domain knowledge about the types of information that would be stored in applications to enable semiautomatic connection to those applications. Our approach is application independent, supporting extraction of legacy data from both structured and unstructured sources. This approach is inherently more scalable and robust than building application specific tools. Successful conclusion of our research will significantly increase the speed and reduce the amount of human input necessary to instantiate an extended enterprise computing platform with heterogeneous sources.

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University of Florida
United States
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