Dataspace: The Final Frontier : 26th British National Conference on Databases, Bncod 26, Birmingham, Uk, July 7-9, 2009, Proceedings - Alan P. Sexton

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Dataspace: The Final Frontier

26th British National Conference on Databases, Bncod 26, Birmingham, Uk, July 7-9, 2009, Proceedings

By: Alan P. Sexton (Editor)

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Paperback | 22 June 2009

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Thisvolumecontainstheproceedingsofthe26thBritishNationalConferenceon Databases (BNCOD 2009) held in July 2009 at the University of Birmingham. The theme of the conference was "Dataspace: The Final Frontier," a topic that onlyrecentlyrosetoattentionandshowspromiseofcrystallizingmanydisparate research threads into a new uni?ed vision for the future of databases. We were very grateful to have Michael Franklin of the University of Calif- nia, Berkeley, USA, and Domenico Laforenza of the Italian National Research Council (CNR) give the keynote speeches of the conference. Michael is one of the originatorsof the dataspaceconcept, and broughtus up to date with current developments and future directions in the ?eld in his opening keynote address. Domenicoisoneoftheleading?guresintheworldonthetopicofgridcomputing, an area that is tackling some of the major issues of management and mani- lation of enormous amounts of data. In his closing keynote talk, he presented XtreemOS, a European project on developing an operating system platform for grid computing. In a new departure for BNCOD, this year we held two tutorials. The ?rst, continuing the theme of dataspaces, was presented by Jens Dittrich of Saarland University,Saarbruc ¨ ken,Germany,inwhichhepresentediMeMex,oneofthe?rst dataspace management systems. System design concepts, dataspace modelling, dataspace indexing, dataspace query processing, and pay-as-you-go information integration were all discussed and important lessons learnt in the project as well as open research challenges were presented. The second tutorial was given by Wenfei Fan, Floris Geerts and Xibei Jia of the University of Edinburgh, UK. Their presentation was on a practical, e?ective and theoretically well-founded approach, based on conditional dependencies, for detecting and repairing errors in real-life data.

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