NDSMMV—A New Dynamic Selection Strategy of Materialized Views for MultiDimensional Data

The selection strategy of materialized view is one of the important issues of data warehouse research. Its goal is to elect a group of materialized views, which could cut down the cost of the query greatly on the basis of the limited storage space. The cost model is proposed at first. Then, a new dynamic selection strategy of materialized views for multidimensional data (NDSMMV) is presented, which is composed of four algorithms: CVGA (candidate view generation algorithm), IGA (improved greedy algorithm), MAMV (modulation algorithm of materialized views) and DMAMV (dynamic modulation algorithm of materialized views). CVGA generates the candidate view set based on multidimensional data lattice, which reduces the number of candidate views to decrease the space search cost and time consumption of the following algorithm. IGA selects materialized views taking account of view query, view maintenance and space constraint. MAMV modulate the materialized views according to the change of the materialized view profit, which improves the capability of querying materialized views. DMAMV uses the sample space to judge whether it is necessary to change the view set which can avoid sharp dither. The comparative experiment indicates that NDSMMV operates more effectively than BPUS and FPUS in the respect that CVGA reduces the amount of views beforehand. IGA selects the materialized views quickly, MAMV modulates the materialized views accurately, and the query expense decreases further with the modulation of the DMAMV on line, which validates the efficiency of NDSMMV.