Abstract
We study query evaluation within a framework of inductive databases. An inductive database is a concept of the next generation database in that the repository should contain not only persistent and derived data, but also the patterns of stored data in a unified format. Hence, the database management system should support both data processing and data mining tasks. Having provided with a tightly-coupling environment, users can then interact with the system to create, access, and modify data as well as to induce and query mining patterns. In this paper, we present a framework and techniques of query evaluation in such an environment so that the induced patterns can play a key role as semantic knowledge in the query rewriting and optimization process. Our knowledge induction approach is based on rough set theory. We present the knowledge induction algorithm driven by a user’s query and explain the method through running examples. The advantages of the proposed techniques are confirmed with experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD, pp. 207–216. ACM Press, New York (1993)
Agrawal, R., Shim, K.: Developing tightly-coupled data mining applications on a relational database system. In: Proc. KDD, pp. 287–290 (1996)
Bonchi, F.: Frequent pattern queries: Language and optimizations. Ph.D. Thesis, Computer Science Department, University of Pisa, Italy (2003)
Boulicaut, J.-F., Klemettinen, M., Mannila, H.: Querying inductive databases: A case study on the MINE RULE operator. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 194–202. Springer, Heidelberg (1998)
Boulicaut, J.-F., Klemettinen, M., Mannila, H.: Modeling KDD processes within the inductive database framework. In: Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 293–302. Springer, Heidelberg (1999)
Charkravarthy, U.S., Grant, J., Minker, J.: Logic-based approach to semantic query optimization. ACM Transactions on Database Systems 15(2), 162–207 (1990)
De Raedt, L.: A perspective on inductive databases. SIGKDD Explorations 4(2), 69–77 (2002)
De Raedt, L., Jaeger, M., Lee, S., Mannila, H.: A theory of inductive query answering. In: Proc. IEEE ICDM, pp. 123–130. IEEE Computer Society Press, Los Alamitos (2002)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI Press, Stanford, California, USA (1996)
Hammer, M.M., Zdonik, S.B.: Knowledge base query processing. In: Proc. VLDB, pp. 137–147 (1980)
Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: DMQL: A data mining query language for relational databases. In: Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 27–34. ACM Press, New York (1996)
Han, J., Huang, Y., Cercone, N., Fu, Y.: Intelligent query answering by knowledge discovery techniques. IEEE Trans. on Knowledge and Data Engineering 8(3), 373–390 (1996)
Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)
Imielinski, T., Virmani, A.: MSQL: A query language for database mining. Data Mining and Knowledge Discovery 2(4), 373–408 (1999)
King, J.: QUIST: A system for semantic query optimization in relational databases. In: Proc. VLDB, pp. 510–517 (1981)
Komorowski, J., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Rough Fuzzy Hybridization: A New Trend in Decision-Making, pp. 3–98. Springer, Heidelberg (1999)
Lin, T., Cercone, N., Hu, X., Han, J.: Intelligent query answering based on neighborhood systems and data mining techniques. In: Proc. IEEE IDEAS, pp. 91–96. IEEE Computer Society Press, Los Alamitos (2004)
Mannila, H.: Inductive databases and condensed representations for data mining. In: Proc. Int. Logic Programming Symp., pp. 21–30 (1997)
Meo, R.: Inductive databases: Towards a new generation of databases for knowledge discovery. In: Proc. DEXA Workshop, pp. 1003–1007 (2005)
Meo, R., Psaila, G., Ceri, S.: A tightly-coupled architecture for data mining. In: Proc. IEEE ICDE, pp. 316–323. IEEE Computer Society Press, Los Alamitos (1998)
Necib, C., Freytag, J.: Semantic query transformation using ontologies. In: Proc. IEEE IDEAS, pp. 187–199. IEEE Computer Society Press, Los Alamitos (2005)
Pawlak, Z.: Rough sets. Int. Jour. Information and Computer Science 11(5), 341–356 (1982)
Sarawagi, S., Thomas, S., Agrawal, R.: Integrating association rule mining with relational database systems: Alternatives and implications. In: Proc. ACM SIGMOD, pp. 343–354. ACM Press, New York (1998)
Siegel, M., Sciore, E., Salveter, S.: A method for automatic rule derivation to support semantic query optimization. ACM Trans. on Database Systems 17(4), 563–600 (1992)
Sun, J., Kerdprasop, N., Kerdprasop, K.: Relevant rule discovery by language bias for semantic query optimization. Jour. Comp. Science and Info. Management 2(2), 53–63 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kerdprasop, N., Kerdprasop, K. (2007). Semantic Knowledge Integration to Support Inductive Query Optimization. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-74553-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74552-5
Online ISBN: 978-3-540-74553-2
eBook Packages: Computer ScienceComputer Science (R0)