SINDBAD and SiQL: Overview, Applications and Future Developments

  • Jörg WickerEmail author
  • Lothar Richter
  • Stefan Kramer


The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.


Acute Myeloid Leukemia Data Mining Query Language Simple Object Access Protocol APriori Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    R. Agrawal, T. Bollinger, C.W. Clifton, S. Dzeroski, J.-C. Freytag, J. Gehrke, J. Hipp, D.A. Keim, S. Kramer, H.-P. Kriegel, B. Liu, H. Mannila, R. Meo, S. Morishita, R.T. Ng, J. Pei, P. Raghavan, R. Ramakrishnan, M. Spiliopoulou, J. Srivastava, V. Torra, and A. Tuzhilin. Data mining: The next generation. Report based on a Dagstuhl perspectives workshop organized by R. Agrawal, J-C. Freytag, and R. Ramakrishnan, 2005.Google Scholar
  2. 2.
    H. Blockeel, T. Calders, É. Fromont, B. Goethals, and A. Prado. Mining views: Database views for data mining. In Proceedings of the International Workshop on Constrained-Bawsed Mining andLearning, 2007.Google Scholar
  3. 3.
    H. Blockeel, T. Calders, É. Fromont, B. Goethals, and A. Prado. Mining views: Database views for data mining. In Proceedings of the IEEE International Conference on Data Engineering, 2008.Google Scholar
  4. 4.
    H. Blockeel, T. Calders, E. Fromont, B. Goethals, A. Prado, and C. Robardet. An inductive database prototype based on virtual mining views. In KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1061–1064, New York, NY, USA, 2008. ACM.Google Scholar
  5. 5.
    M. Botta, Boulicaut J.-F., C. Masson, and R. Meo. Query languages supporting descriptive rule mining: A comparative study. In Database Support for Data Mining Applications, pages 24–51, 2004.Google Scholar
  6. 6.
    C. J. Date. An Introduction to Database Systems. Addison Wesley, 4th edition, 1986.Google Scholar
  7. 7.
    L. De Raedt and S. Kramer. The levelwise version space algorithm and its application to molecular fragment finding. In Proc. 17th International Joint Conference on Artificial Intelligence (IJCAI 2001, Seattle, USA), pages 853–862. Morgan Kaufmann, San Francisco, CA, USA, 2001.Google Scholar
  8. 8.
    P. Domingos. Structured machine learning: Ten problems for the next ten years. In Proceedings of Seventeenth International Conference on Inductive Logic Programming, Corvallis, Oregon, 2007. Springer.Google Scholar
  9. 9.
    C. Ferris, D. Booth, M. Champion, H. Haas, D. Orchard, E. Newcomer, and F. McCabe. Web services architecture. W3C note, W3C, 2004.
  10. 10.
    J. Fischer, V. Heun, and S. Kramer. Fast frequent string mining using suffix arrays. In Proceedings of the Fifth IEEE International Conference on Data Mining. IEEE Computer Society Press, 2005.Google Scholar
  11. 11.
    J. Fischer, V. Heun, and S. Kramer. Optimal string mining under frequency constraints. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2006), pages 139–150, 2006.Google Scholar
  12. 12.
    S. Fröhler and S. Kramer. Inductive logic programming for gene regulation prediction. Machine Learning, 70(2-3):225–240, 2008.CrossRefGoogle Scholar
  13. 13.
    M. Garofalakis, D. Hyun, R. Rastogi, and K. Shim. Efficient algorithms for constructing decision trees with constraints. In KDD ’00: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 335–339, New York, NY, USA, 2000. ACM.Google Scholar
  14. 14.
    T.R. Golub, D.K. Slonim, P. Tamayo, P. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531–7, 1999.CrossRefGoogle Scholar
  15. 15.
    J. Han, Y. Fu, W. Wang, K. Koperski, and O. Zaiane. DMQL: A data mining query language for relational databases. In SIGMOD’96 Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD’96), Montreal, Canada, 1996.Google Scholar
  16. 16.
    T. Imielinski and A. Virmani. MSQL: A query language for database mining. Data Min. Knowl. Discov, 3(4):373–408, 1999.CrossRefGoogle Scholar
  17. 17.
    Boulicaut J.-F. and C. Masson. Data mining query languages. In O. Maimon and L. Rokach, editors, The Data Mining and Knowledge Discovery Handbook, pages 715–727. Springer, 2005.Google Scholar
  18. 18.
    S. Kramer, V. Aufschild, A. Hapfelmeier, A. Jarasch, K. Kessler, S. Reckow, J. Wicker, and L. Richter. Inductive databases in the relational model: The data as the bridge. In Francesco Bonchi and Jean-François Boulicaut, editors, Proceedings of the Fourth International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005), volume 3933 of Lecture Notes in Computer Science, pages 124–138. Springer, 2005.Google Scholar
  19. 19.
    S. Kramer, L. De De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136–143, 2001.Google Scholar
  20. 20.
    R. Meo, G. Psaila, and S. Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2(2):195–224, 1998.CrossRefGoogle Scholar
  21. 21.
    J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239, 1990.Google Scholar
  22. 22.
    L. Richter, J. Wicker, K. Kessler, and S. Kramer. An inductive database and query language in the relational model. In Proceedings of the 10th International Conference on Extending Database Technology (EDBT 2008), pages 740–744. ACM Press, 2008.Google Scholar
  23. 23.
    O.S. Weislow, R. Kiser, D.L. Fine, J.P. Bader, R.H. Shoemakerand, and M.R. Boyd. New soluble formazan assay for HIV-1 cytopathic effects: application to high flux screening of synthetic and natural products for aids antiviral activity. Journal of the National Cancer Institute, 81:577–586, 1989.CrossRefGoogle Scholar
  24. 24.
    J. Wicker, C. Brosdau, L. Richter, and S. Kramer. SINDBAD SAILS: A service architecture for inductive learning schemes. In Nada Lavrač, Joost Kok, Jeroen de Bruin, and Vid Podpečan, editors, Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, 2008.Google Scholar
  25. 25.
    J.Wicker, L. Richter, K. Kessler, and S. Kramer. SINDBAD and SiQL: An inductive database and query language in the relational model. In Walter Daelemans, Bart Goethals, and Katharina Morik, editors, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II, pages 690–694. Springer, 2008.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institut für Informatik I12Technische Universität MünchenGarching b. MünchenGermany

Personalised recommendations