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Download Current Trends in Database Technology – EDBT 2006: EDBT 2006 by M. H. Ali (auth.), Torsten Grust, Hagen Höpfner, Arantza PDF

By M. H. Ali (auth.), Torsten Grust, Hagen Höpfner, Arantza Illarramendi, Stefan Jablonski, Marco Mesiti, Sascha Müller, Paula-Lavinia Patranjan, Kai-Uwe Sattler, Myra Spiliopoulou, Jef Wijsen (eds.)

This booklet constitutes the completely refereed joint post-proceedings of 9 workshops held as a part of the tenth foreign convention on Extending Database expertise, EDBT 2006, held in Munich, Germany in March 2006.

The 70 revised complete papers awarded have been chosen from a variety of submissions in the course of rounds of reviewing and revision. in line with the topical concentration of the respective workshops, the papers are prepared in sections on database expertise typically (EDBT PhD Workshop), database applied sciences for dealing with XML details on the internet (DataX 2006), inconsistency and incompleteness in databases (IIDB 2006), info integration in healthcare (IIHA 2006), semantics of series and time established information (ICSNW 2006), question languages and question processing (QLQP 2006), pervasive details administration (PIM 2006), trend illustration and administration (PaRMa 2006), and reactivity at the Web.

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Additional resources for Current Trends in Database Technology – EDBT 2006: EDBT 2006 Workshops PhD, DataX, IIDB, IIHA, ICSNW, QLQP, PIM, PaRMA, and Reactivity on the Web, Munich, Germany, March 26-31, 2006, Revised Selected Papers

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Prasad Chakka, Adam Everspaugh, and Jignesh M. Patel. Indexing Large Trajectory Data Sets with SETI. In Proc. of the Conf. on Innovative Data Systems Research, CIDR, 2003. 11. Kaushik Chakrabarti and Sharad Mehrotra. Dynamic granular locking approach to phantom protection in r-trees. In ICDE, 1998. 12. Sirish Chandrasekaran and Michael J. Franklin. Streaming Queries over Streaming Data. In VLDB, 2002. 13. Sirish Chandrasekaran and Michael J. Franklin. Psoup: a system for streaming queries over streaming data.

In SIGMOD, 1990. 7. Rimantas Benetis, Christian S. Jensen, Gytis Karciauskas, and Simonas Saltenis. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects. In IDEAS, 2002. 8. Christian Bohm and Florian Krebs. The k-Nearest Neighbor Join: Turbo Charging the KDD Process. In Knowledge and Information Systems (KAIS), in print, 2004. 9. Ying Cai, Kien A. Hua, and Guohong Cao. Processing Range-Monitoring Queries on Heterogeneous Mobile Objects. In Mobile Data Management, MDM, 2004. 10.

We also highlight the join operation over data streams due to its important role in the process of phenomenon detection and tracking. To reduce the overall power consumption of the sensor network while object tracking, [28] proposes a prediction-based strategy that focuses on regions where the moving object is likely to appear. In [30], the tree-like communication structure of the sensor network is reconfigured dynamically to reduce the number of hops between a sensor and the sink node as the sensor comes closer to the moving object.

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