The DAEDALUS framework: progressive querying and mining of movement data

ABSTRACT

In this work we propose DAEDALUS, a formal framework and system, specifically focussed on progressive combination of mining and querying operators. The core component of DAEDALUS is the MO-DMQL query language that extends SQL in two respects, namely a pattern definition operator and the capability to uniform manipulating both raw data and unveiled patterns. DAEDALUS system is specifically focussed on movement data and has been implemented as a query execution layer on top of the Hermes Moving Object Database. The expressiveness and usefulness of the MODMQL language as well as the computational capabilities of DAEDALUS are qualitatively evaluated by means of a case study.

REFERENCES

• 1: Toon Calders , Laks V. S. Lakshmanan , Raymond T. Ng , Jan Paredaens, Expressive power of an algebra for data mining, ACM Transactions on Database Systems (TODS), v.31 n.4, p.1169-1214, December 2006 [doi>10.1145/1189769.1189770]
• 2: Fosca Giannotti , Mirco Nanni , Fabio Pinelli , Dino Pedreschi, Trajectory pattern mining, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281230]
• 3: R. H. Güting and M. Schneider. Moving Objects Databases. Elsevier, 2005.
• 4: Luca Forlizzi , Ralf Hartmut Güting , Enrico Nardelli , Markus Schneider, A data model and data structures for moving objects databases, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.319-330, May 15-18, 2000, Dallas, Texas, United States
• 5: M. Nanni, B. Kuijpers, C. Korner, M. May, and D. Pedreschi. Spatiotemporal Data Mining. In F. Giannotti and D. Pedreschi, editors, Mobility, Data Mining, and Privacy: Geographic Knoweledge Discovery. Springer-Verlag, 2008.
• 6: G. Manco, M. Baglioni, F. Giannotti, B. Kujpers, A. Raffaeta, and C. Renso. Querying and Reasoning for Spatio-Temporal Data Mining. In F. Giannotti and D. Pedreschi, editors, Mobility, Data Mining, and Privacy: Geographic Knoweledge Discovery. Springer-Verlag, 2008.
• 7: Oracle Corp. Oracle. Oracle Database Documentation Library. 10g Release 1 (10.1). http://www.acs.ilstu.edu/docs/Oracle/appdev.101/b10826/toc.htm
• 8: R. Ortale, E. Ritacco, N. Pelekis, R. Trasarti, G. Costa, F. Giannotti, G. Manco, C. Renso, Y. Theodoridis. Towards Progressively Querying and Mining Movement Data. ICAR-CNR technical Report CS-ICAR-02-2008. Available at http://150.145.63.4/biblio.
• 9: Jose A. Lema, L. Forlizzi, R. Güting, E. Nardelli, and M. Schneider. Algorithms for Moving Objects Databases. The Computer Journal, 46(6):680--712, 2003.
• 10: Ouri Wolfson , A. Prasad Sistla , Sam Chamberlain , Yelena Yesha, Updating and Querying Databases that Track Mobile Units, Distributed and Parallel Databases, v.7 n.3, p.257-387, July 1999 [doi>10.1023/A:1008782710752]
• 11: N. Pelekis, Y. Theodoridis, S. Vosinakis, and T. Panayiotopoulos. Hermes - a Framework for Location-based Data Management. In Proceedings of the 10th EDBT, pp. 1130--1134, 2006.
• 12: A. Prasad Sistla , Ouri Wolfson , Sam Chamberlain , Son Dao, Modeling and Querying Moving Objects, Proceedings of the Thirteenth International Conference on Data Engineering, p.422-432, April 07-11, 1997
• 13: Theodore Johnson , Laks V. S. Lakshmanan , Raymond T. Ng, The 3W Model and Algebra for Unified Data Mining, Proceedings of the 26th International Conference on Very Large Data Bases, p.21-32, September 10-14, 2000

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

General Terms:
Languages

Keywords:
data mining query language, movement data, moving object database