Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Andersen, William

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    Advances in High Performance Knowledge Representation
    (1996) Stoffel, K.; Taylor, M.; Hendler, James A.; Saltz, J.; Andersen, William; ISR
    Real world applications are demanding that KR systems provide support for knowledge bases containing millions of assertions. We present Parka-DB, a high-performance reimplementation of the Parka KR language which uses a standard relational DBMS. The integration of a DBMS and the Parka KR language allows us to efficiently support complex queries on extremely large KBs using a single processor, as opposed to our earlier massively parallel system. In addition, the system can make good use of secondary memory, with the whole system needing less than 16MB of RAM to hold a KB of over 2,000,000 assertions. We demonstrate empirically that this reduction in primary storage requires only about 10% overhead in time, and decreases the load time of very large KBs by more than two orders of magnitude.
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    Using the Parka Parallel Knowledge Representation System (Version 3.2)
    (1995) Kettler, Brian; Andersen, William; Hendler, James A.; Luke, Sean; ISR
    Parka is a symbolic, semantic network knowledge representation system that takes advantage of the massive parallelism of supercomputers such as the Connection Machine. The Parka language has many of the features of traditional semantic net/frame-based knowledge representation languages but also supports several kinds of rapid parallel inference mechanisms that scale to large knowledge-bases of hundreds of thousands of frames or more. Parka is intended for general-purpose use and has been used thus far to support, A.I. systems for case-based reasoning and data mining.

    This document is a user manual for the current version of Parka, version 3.2. It describes the Parka language and presents some examples of knowledge representation using Parka. Details about the parallel algorithms, implementation, and empirical results are presented elsewhere.

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    Knowledge Representation in PARKA - Part 2: Experiments, Analysis, and Enhancements
    (1992) Spector, Lee; Andersen, William; Hendler, James A.; Kettler, Brian; Schwartzman, Eugene; Woods, Cynthia; Evett, Matthew; ISR
    Our research group has designed and implemented a symbolic knowledge representation system called PARKA which runs on the Connection Machine, a massively parallel SIMD computer [9]. The semantics of this system are discussed in [11]. The details of the Connection Machine implementation and discussions of performance considerations can be found in [3], [4], [5], [6] and [7]. In the past year the PARKA project has made significant advances along several fronts of both theoretical and practical significance. This paper summarizes some of this work and outlines directions for further research.