Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

IQL: A Proposal for an Inductive Query Language

  • Conference paper

Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 4747))

Abstract

The overall goal of this paper is to devise a flexible and declarative query language for specifying or describing particular knowledge discovery scenarios. We introduce one such language, called IQL. IQL is intended as a general, descriptive, declarative, extendable and implementable language for inductive querying that supports the mining of both local and global patterns, reasoning about inductive queries and query processing using logic, as well as the flexible incorporation of new primitives and solvers. IQL is an extension of the tuple relational calculus that includes functions as primitives. The language integrates ideas from several other declarative programming languages, such as pattern matching and function typing. We hope that it will be useful as an overall specification language for integrating data mining systems and principles.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, Springer, Heidelberg (2006)

    Google Scholar 

  2. Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.): Constraint-Based Mining and Inductive Databases. LNCS (LNAI), vol. 3848. Springer, Heidelberg (2006)

    Google Scholar 

  3. Braga, D., Campi, A., Ceri, A., Lanzi, S., Klemetinen, M.: Mining association rules from XML data. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, Springer, Heidelberg (2002)

    Google Scholar 

  4. Calders, T., Goethals, B., Prado, A.: Integrating pattern mining in relational databases. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Date, C.J.: An introduction to database systems. Addison-Wesley, Reading (2000)

    Google Scholar 

  6. Giannotti, F., Manco, G., Turini, F.: Specifying mining algorithms with iterative user-defined aggregates. IEEE Transactions Knowledge and Data Engineering , 1232–1246 (2004)

    Google Scholar 

  7. Han, J., Fu, Y., Koperski, K., Wang, W., Zaiane, O.: DMQL: A data mining query language for relational databases. In: Proceedings of the ACM SIGMOD Workshop on research issues on data mining and knowledge discovery, ACM Press, New York (1996)

    Google Scholar 

  8. Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)

    Article  Google Scholar 

  9. Imielinski, T., Virmani, A.: MSQL: A query language for database mining. Data Mining and Knowledge Discovery 2(4), 373–408 (1999)

    Article  Google Scholar 

  10. Johnson, T., Lakshmanan, L.V., Ng, R.: The 3w model and algebra for unified data mining. In: Proc. VLDB Int. Conf. Very Large Data Bases, pp. 21–32 (2000)

    Google Scholar 

  11. Kramer, S., Aufschild, V., Hapfelmeier, A., Jarasch, A., Kessler, K., Reckow, S., Wicker, J., Richter, L.: Inductive databases in the relational model: The data as the bridge. In: Bonchi, F., Boulicaut, J-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 124–138. Springer, Heidelberg (2006)

    Google Scholar 

  12. Meo, R., Psaila, G., Ceri, S.: An extension to SQL for mining association rules. Data Mining and Knowledge Discovery 2(2), 195–224 (1998)

    Article  Google Scholar 

  13. De Raedt, L.: A perspective on inductive databases. SIGKDD Explorations 4(2), 69–77 (2003)

    Article  Google Scholar 

  14. Ramakrishnan, R., Gehrke, J.: Database Management Systems. McGraw-Hill, New York (2004)

    Google Scholar 

  15. Siebes, A.: Data mining in inductive databases. In: Bonchi, F., Boulicaut, J-F. (eds.) KDID 2005. LNCS, vol. 3933, Springer, Heidelberg (2006)

    Google Scholar 

  16. Tang, Z., MacLennan, J.: Data Mining with SQL Server 2005. Wiley, Chichester (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Informatik, Albert-Ludwidgs-Universität, Georges-Köhler-Allee, Gebäude 097, D-79110, Freiburg im Breisgau, Germany

    Siegfried Nijssen & Luc De Raedt

  2. Departement Computerwetenschappen, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001, Leuven, Belgium

    Siegfried Nijssen & Luc De Raedt

Authors
  1. Siegfried Nijssen

    You can also search for this author inPubMed Google Scholar

  2. Luc De Raedt

    You can also search for this author inPubMed Google Scholar

Editor information

Sašo Džeroski Jan Struyf

Rights and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nijssen, S., De Raedt, L. (2007). IQL: A Proposal for an Inductive Query Language. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_12

Download citation

Publish with us

[8]ページ先頭
©2009-2025 Movatter.jp