A novel algorithm with IM-LSI index for incremental maintenance of materialized view
Keywords:I-mine item set index, FP growth, LSI index, Materialization view, Data warehousing, Selection cost
The ability to afford decision makers with both accurate and timely consolidated information as well as rapid query response times is the fundamental requirement for the success of a Data Warehouse. To provide fast access, a data warehouse stores materialized views of the sources of its data. As a result, a data warehouse needs to be maintained to keep its contents consistent with the contents of its data sources. Incremental maintenance is generally regarded as a more efficient way to maintain materialized views in a data warehouse The view has to be maintained to reflect the updates done against the base relations stored at the various distributed data sources. The proposed approach contains two modules namely, materialized view selection(MVS) and maintenance of materialized view. (MMV). In recent times, several algorithms have been proposed for keeping the views up-to-date in response to the changes in the source data. Therefore, we present an improved algorithm for MVS and MMV using IM-LSI(Itemset Mining using Latent Semantic Index) algorithm. selection of views to materialize using the IM(Itemset Mining) algorithm method to overcome the problem resulting from conventional view selection algorithms and then we consider the maintenance of materialized views using LSI. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs better than conventional algorithms.
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