Incremental maintenance of multi-source views
ADC '01 Proceedings of the 12th Australasian database conference
Posse: A Framework for Optimizing Incremental View Maintenance at Data Warehouse
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
DSD: maintain data cubes more efficiently
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
DSD: Maintain Data Cubes More Efficiently
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
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Incremental maintenance of materialized views consists of installing changes to the view as a result of an update to one of the base relations. This generally requires information from some or all of the other base relations comprising the view, which may be obtained by probing (i.e. querying) the base relations. In a distributed context such as a data warehouse, there may be a great deal of concurrency among updates as well as probes and replies, leading to consistency problems, for which a number of solutions have been offered. These solutions, however, have been expressed as algorithms of limited practical use because they are variously limited to restricted view queries, relational semantics, and limited concurrency. We propose the POSSE framework in which many of these limitations are removed, and in which the techniques of distributed query optimization can be brought to bear on the problem. To this end, we show how for a particular method of consistent probing it is possible to have the power of SQL view queries with multiset semantics, and at the same time have available a spectrum of concurrency from none at all as in some previously proposed solutions to the maximum concurrency obtained by issuing all probes in parallel. We then show how optimization of the probing process can be used to select various degrees of concurrency for the desired tradeoffs of concurrency against processing cost and message size.