Rating of Discrimination Networks for Rule-based Systems

Fabian Ohler, Kai Schwarz, Karl-Heinz Krempels, Christoph Terwelp

2013

Abstract

The amount of information stored in a digital form grows on a daily basis but is mostly only understandable by humans, not machines. A way to enable machines to understand this information is using a representation suitable for further processing, e. g. frames for fact declaration in a Rule-based System. Rule-based Systems heavily rely on Discrimination Networks to store intermediate results to speed up the rule processing cycles. As these Discrimination Networks have a very complex structure it is important to be able to optimize them or to choose one out of many Discrimination Networks based on its structural efficiency. Therefore, we present a rating mechanism for Discrimination Networks structures and their efficiencies. The ratings are based on a normalised representation of Discrimination Network structures and change frequency estimations of the facts in the working memory and are used for comparison of different Discrimination Networks regarding processing costs.

References

  1. Brant, D., Grose, T., Lofaso, B., and Miranker, D. (1991). Effects of Database Size on Rule System Performance:Five Case Studies. In Proceedings of the 17th International Conference on Very Large Data Bases (VLDB).
  2. Brownston, L., Farrell, R., Kant, E., and Martin, N. (1985). Programming expert systems in OPS5: an introduction to rule-based programming. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
  3. Cárdenas, A. F. (1975). Analysis and performance of inverted data base structures. Commun. ACM, 18(5):253-263.
  4. Forgy, C. L. (1981). OPS5 User's Manual. Technical report, Department of Computer Science, Carnegie-Mellon University.
  5. Forgy, C. L. (1982). Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19(1):17 - 37.
  6. Hanson, E. N. (1993). Gator: A Discrimination Network Structure for Active Database Rule Condition Matching. Technical report, University of Florida.
  7. Hanson, E. N. and Hasan, M. S. (1993). Gator: An Optimized Discrimination Network for Active Database Rule Condition Testing. Technical report, University of Florida.
  8. Jamocha (2006). Jamocha Project Page. http:// www.jamocha.org, http://sourceforge.net/projects/ jamocha.
  9. Miranker, D. P. (1987). TREAT: A Better Match Algorithm for AI Production Systems; Long Version. Technical report, University of Texas at Austin, Austin, TX, USA.
  10. Winston, P. H. (1992). Artificial intelligence. AddisonWesley.
  11. Yao, S. B. (1977). Approximating block accesses in database organizations. Commun. ACM, 20(4):260- 261.
Download


Paper Citation


in Harvard Style

Ohler F., Schwarz K., Krempels K. and Terwelp C. (2013). Rating of Discrimination Networks for Rule-based Systems . In Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-67-9, pages 32-42. DOI: 10.5220/0004634900320042


in Bibtex Style

@conference{data13,
author={Fabian Ohler and Kai Schwarz and Karl-Heinz Krempels and Christoph Terwelp},
title={Rating of Discrimination Networks for Rule-based Systems},
booktitle={Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,},
year={2013},
pages={32-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004634900320042},
isbn={978-989-8565-67-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Rating of Discrimination Networks for Rule-based Systems
SN - 978-989-8565-67-9
AU - Ohler F.
AU - Schwarz K.
AU - Krempels K.
AU - Terwelp C.
PY - 2013
SP - 32
EP - 42
DO - 10.5220/0004634900320042