Randomised Optimisation of Discrimination Networks Considering Node-sharing

Fabian Ohler, Karl-Heinz Krempels, Christoph Terwelp

2016

Abstract

Because of their ability to store, access, and process large amounts of data, Database Management Systems (DBMSs) and Rule-based Systems (RBSs) are used in many information systems as information processing units. A basic function of a RBS and a function of many DBMSs is to match conditions on the available data. To improve performance intermediate results are stored in Discrimination Networks (DNs). The resulting memory consumption and runtime cost depend on the structure of the DN. A lot of research has been done in the area of optimising DNs. In this paper, we focus on re-using network parts considering multiple rule conditions and exploiting the characteristics of equivalences. We present an approach incorporating the potential of both concepts and balance their application in a randomised fashion. To evaluate the algorithms developed, they were implemented and yielded promising results. Shortcomings of this approach are discussed and their removal constitutes our current work.

References

  1. Adl-Tabatabai, A., Kozyrakis, C., and Saha, B. (2006). Unlocking concurrency. ACM Queue, 4(10):24-33.
  2. Aouiche, K., Jouve, P.-E., and Darmont, J. (2006). Clustering-based materialized view selection in data warehouses. In Manolopoulos, Y., Pokorný, J., and Sellis, T. K., editors, Advances in Databases and Information Systems, volume 4152 of Lecture Notes in Computer Science, pages 81-95. Springer Berlin Heidelberg.
  3. Brant, D. A., Grose, T., Lofaso, B., and Miranker, D. P. (1991). Effects of database size on rule system performance: Five case studies. In Lohman, G. M., Sernadas, A., and Camps, R., editors, Proceedings of the 17th International Conference on Very Large Data Bases, pages 287-296.
  4. Brownston, L., Farrell, R., Kant, E., and Martin, N. (1985). Programming expert systems in OPS5. AddisonWesley Pub. Co., Inc., Reading, MA.
  5. Forgy, C. L. (1981). OPS5 User's Manual. Tech. Report CMU-CS-81-135. Carnegie-Mellon Univ. Pittsburgh Dept. Of Computer Science.
  6. Forgy, C. L. (1982). Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19(1):17-37.
  7. Hanson, E. N., Bodagala, S., and Chadaga, U. (2002). Trigger condition testing and view maintenance using optimized discrimination networks. IEEE Transactions on Knowledge and Data Engineering, 14(2):261-280.
  8. Hanson, E. N. and Hasan, M. S. (1993). Gator : An Optimized Discrimination Network for Active Database Rule Condition Testing. Tech. Report TR93-036, Univ. of Florida, pages 1-27.
  9. Herlihy, M. and Moss, J. E. B. (1993). Transactional memory: Architectural support for lock-free data structures. In Proceedings of the 20th Annual International Symposium on Computer Architecture. San Diego, CA, May 1993, pages 289-300.
  10. Ioannidis, Y. E. and Kang, Y. C. (1990). Randomized algorithms for optimizing large join queries. In Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, May 23-25, 1990., pages 312-321.
  11. Ioannidis, Y. E. and Wong, E. (1987). Query optimization by simulated annealing. In Proceedings of the Association for Computing Machinery Special Interest Group on Management of Data 1987 Annual Conference, San Francisco, California, May 27-29, 1987, pages 9-22.
  12. Miranker, D. P. (1987). TREAT: A Better Match Algorithm for AI Production Systems; Long Version. Technical report, Austin, TX, USA.
  13. Nahar, S., Sahni, S., and Shragowitz, E. (1986). Simulated annealing and combinatorial optimization. In DAC, pages 293-299.
  14. Ohler, F., Schwarz, K., Krempels, K., and Terwelp, C. (2013). Rating of discrimination networks for rulebased systems. In Proceedings of the 2nd International Conference on Data Technologies and Applications, pages 32-42.
  15. Ohler, F. and Terwelp, C. (2015). A notation for discrimination network analysis. In Proceedings of the 11th International Conference on Web Information Systems and Technologies, pages 566-570.
  16. Swami, A. N. and Gupta, A. (1988). Optimization of large join queries. In Proceedings of the 1988 ACM SIGMOD International Conference on Management of Data, Chicago, Illinois, June 1-3, 1988., pages 8-17.
  17. Whang, K.-Y. and Krishnamurthy, R. (1990). Query optimization in a memory-resident domain relational calculus database system.
  18. Winston, P. H. (1984). Artificial intelligence . AddisonWesley Longman Publishing Co., Inc., Boston, MA, USA.
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Paper Citation


in Harvard Style

Ohler F., Krempels K. and Terwelp C. (2016). Randomised Optimisation of Discrimination Networks Considering Node-sharing . In Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-186-1, pages 257-267. DOI: 10.5220/0005905002570267


in Bibtex Style

@conference{webist16,
author={Fabian Ohler and Karl-Heinz Krempels and Christoph Terwelp},
title={Randomised Optimisation of Discrimination Networks Considering Node-sharing},
booktitle={Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2016},
pages={257-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005905002570267},
isbn={978-989-758-186-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Randomised Optimisation of Discrimination Networks Considering Node-sharing
SN - 978-989-758-186-1
AU - Ohler F.
AU - Krempels K.
AU - Terwelp C.
PY - 2016
SP - 257
EP - 267
DO - 10.5220/0005905002570267