By Vicky Nassis, R. Rajugan, Tharam S. Dillon, Wenny Rahayu (auth.), Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß (eds.)
This publication constitutes the refereed complaints of the sixth overseas convention on info Warehousing and data Discovery, DaWaK 2004, held in Zaragoza, Spain, in September 2004.
The forty revised complete papers offered have been conscientiously reviewed and chosen from over a hundred submissions. The papers are equipped in topical sections on information warehouse layout; wisdom discovery framework and XML info mining, facts cubes and queries; multidimensional schema and knowledge aggregation; inductive databases and temporal principles; commercial functions; information clustering; information visualization and exploration; facts class, extraction, and interpretation; info semantics, organization rule mining; occasion series mining; and trend mining.
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Extra info for Data Warehousing and Knowledge Discovery: 6th International Conference, DaWaK 2004, Zaragoza, Spain, September 1-3, 2004. Proceedings
57-66, 2000. 4. L. Bellatreche, K. Karlapalem, M. Schneider and M. Mohania, “What can partitioning do for your data warehouses and data marts”, in Proc. Int. Database Engineering and Application Symposium (IDEAS), pp. 437-445, 2000. 5. L. Bellatreche, M. Schneider, M. Mohania, and B. Bhargava, “Partjoin : an efficient storage and query execution design strategy for data warehousing”, Proc. Int. Conf. on Data Warehousing and Knowledge Discovery (DAWAK), pp. 296-306, 2002. 6. L. Bellatreche, M. Schneider, Lorinquer, H.
2. Basic Star-Partitioning Actual schemas are typically more complex than a pure star, resulting in not so independent node processing of partial queries and higher repartitioning and communication costs. Schemas can have several stars with cross-querying, possibly larger dimensions and possibly a less pure star design. Joins with large dimensions and with more than one fact are frequent. The most important concern in such an environment is how to partition facts and large dimensions horizontally to solve the partitioning issue described in section 2 as efficiently as possible.
Over the past there has been an large amount of work on related and intertwined issues of data allocation [1, 4, 6, 11, 16] and query processing strategies in parallel and distributed databases [3, 8, 9, 13]. Data allocation in parallel and distributed databases has been studied extensively, typically in heavy transactional environments. In an early work  compares full partitioning with clustering the relations on a single disk, concluding that partitioning is consistently better for multi-user workloads but can lead to serious performance overhead on complex queries involving joins, with high communication overheads.