B -- KNOWLEDGE MANAGEMENT, COMPLEX DATA ANALYSIS, AND COGNITIVE TECHNOLOGIES FOR ADVANCED DECISION MAKING

Notice Date

October 5, 2001

Contracting Office

Concurrent Technologies Corporation, 100 CTC Drive, Johnstown, PA 15904-1935

ZIP Code

15904-1935

Solicitation Number

KNA001

Response Due

November 9, 2001

Point of Contact

Mr. Michael Knapp, at (814) 269-6271

E-Mail Address

Email responses are acceptable (KMRFI@ctc.com)

Description

REQUEST FOR INFORMATION: Concurrent Technologies Corporation (CTC) is providing this Request for Information to identify potential sources of existing or emerging solutions that can support the understanding of the issues in, and contribute to the development of, a highly complex, integrated system that raises the cognitive level of the analysis to a degree where senior decision makers are better equipped to perform in today s complex knowledge space. White papers for this effort will be due no later than 9 Nov 2001, 4:00pm EST. BACKGROUND: In today s fast paced environment, the technical staff/analysts tasked with supporting the senior decision makers are continuously challenged to provide clear, concise intelligence based on analysis of a wide range and depth of data derived from various sources, in various formats, and at various levels of confidence. These analysts are required to deal with a quantity and density of distributed information that is growing at an exponential rate and is more interwoven and complex than ever before. Today s data-rich economic/business environment has emerged with fundamental characteristics that far surpass the capabilities of the analytic tools, techniques, and technologies presently being employed. Current estimates project the scale and scope of the information problem the new environment to be as much as 1012 times greater than the information problem that was present just ten years ago. New approaches are needed to effectively transform a wide range and depth of data into intelligence in a format that is easily, accurately, and quickly understood by senior managers. New methodologies are needed to support this Data to Knowledge to Intelligence transformation problem. An essential step in addressing this problem is to identify applied research and development efforts that offer potential solutions in the development of methodologies, frameworks, tools, techniques, technologies, and other key issues. Traditional relational database technologies appear inadequate to solve this problem. Processing and response times associated with databases built using traditional technologies, make their use impractical, and well outside the pace of operations associated with today s dynamic business environment. In addition, traditional technology does not provide the ability to capture more complex relationships or build upon prior analysis knowledge. The non-linear behavior exhibited in real-world, complex, interdependent systems presents an extreme analytic challenge. Approaches that cause changes in the structure of the Data/Knowledge space and then evaluate the effects in the Intelligence space will assist in meeting this challenge. Additional solutions may be offered by innovatively applying or integrating commercial-off-the-shelf (COTS) and other software data analysis tools; modeling knowledge; building taxonomies, ontologies, epistemologies; visualizing relationships; and cognitively encoding information. OBJECTIVES: This RFI is soliciting ideas from the world-wide science and technology community that will be able to provide a new analytic methodology, capable of handling swiftly and efficiently the scope and scale of information that characterizes the new target environment. It is believed that the methodology will be based on disciplines such as: h Complex systems theory (or dynamical systems theory), allowing one to analyze and relate the objects, attributes, and relationships of very large and complex system infrastructures and data sets. h Conceptual/cognitive structures, essentially a new representational language that permits formation of queries, or templates to identify recognizable patterns of activity in highly dense and interrelated information environments. h Self-organizing networks where the spontaneous emergence of new structures and new forms of behavior in open systems far from equilibrium, characterized by internal feedback loops and described mathematically by nonlinear equations. h Situation-defined surveillance where the system unobtrusively monitors and tracks data sources using intelligent agent technology. h Advanced natural language technology capable of processing at near human performance levels. h Pattern matching, machine learning, data mining, and other similar technologies. Three critical focus areas for technology deployment include entity discovery, knowledge management, and intelligent applications. Entity Discovery: This area involves the processing and integration of diverse information streams for automated discovery and extraction of knowledge entities (concepts and conceptual relations). Issues for consideration include: h Automated techniques for entity recognition/identification within structured and unstructured information/data h Distributed techniques for entity classification and naming h Ontology development, representation, and maintenance techniques h Common interfacing solutions for distributed entity discovery h Entity discovery engine architectures h Entity event notification and command/control techniques for dynamic entity management h Access control, information assurance, and security in distributed environments Knowledge Management: This area involves the integration of diverse components for managing very large volumes of knowledge. Issues for consideration include: h Automated techniques for collecting, representing, and organizing very large knowledge repositories h Centralized and/or distributed techniques for storing, accessing, manipulating, and retrieving very large quantities of knowledge h Automated and/or semi-automated techniques for fusing diverse knowledge fragments and discovering additional and/or non-obvious complex entity relationships. h Automated techniques for dynamic knowledge notification, security, error control, etc. Intelligent Applications: This area is focused on developing advanced application services and tools for analysts to efficiently perform and enhance their duties and effectiveness using the new technology. Issues for consideration include: h Automated tools for browsing and navigating large knowledge repositories h Advanced visualization techniques to aid understanding and intuition h Automated inference tools to for inductive, deductive, and abductive reasoning. h Knowledge collaboration tools and services h Extensible application frameworks and/or middleware for rapid product development h Innovative, multi-lingual user interface techniques for expressing queries, testing hypothesis, and exploring complex interrelationships h Multi-lingual natural language interfacing techniques This RFI is designed to help CTC prepare to acquire external support for the development of a system that facilitates the analysis of large data systems and simplifies the presentation of analytic results. We will use the RFI to identify companies, academic institutions, and other organizations that can help us: h Develop a concept of operations for the use of these capabilities. h Assess the state of the art of these technologies. h Advance that state of the art to the point at which it can be employed in support of our concept of operations. h Employ that advanced state of the art to build and deploy capabilities using these technologies in support of our client s missions. RESPONSE INSTRUCTIONS: Companies responding to this RFI should describe: h Their experience and expertise in the development of complex systems and conceptual structures technology related to the areas listed above. Applications of this technology in support of complex missions should also be provided. h Resources they could employ. h Relationships with other organizations whose resources they could employ. Responses to this RFI should be received no later than 9 Nov 2001, 4:00pm EST, to Mr. Michael Knapp, Concurrent Technologies Corporation, 100 CTC Drive, Johnstown, Pennsylvania 15904. Email responses are acceptable at KMRFI@ctc.com. If the responder wishes to submit classified information in response to this RFI, please contact Mr. Michael Knapp, at (814) 269-6271 for instructions. Responses should consist of a white paper and any other attached material that explains relevant capabilities. CTC will treat all submissions as competitive information and will disclose their contents only for the purpose of evaluation. If proprietary data needs to be submitted, CTC will execute a Non-Disclosure Agreement. This Request for Information does not bind CTC or any other party to future solicitations. Sole source or other awards may be made. The cost of preparing white papers in response to this request is not considered an allowable direct charge to any contract. White papers shall be limited to ten (10) pages, double spaced, single-sided, 8.5 by 11 inches. CTC will contact companies whose responses appear applicable to industry needs for further discussions. Questions about this RFI can be submitted to the technical point of contact listed above.

Record

Loren Data Corp. 20011010/BSOL001.HTM (W-278 SN510210)