A Novel Method for Selecting and Materializing Views based on OLAP Signatures and GRASP


  • Andresson da Silva Firmino Federal University of Pernambuco
  • Rodrigo Costa Mateus Federal University of Pernambuco
  • Valéria Cesário Times Federal University of Pernambuco
  • Lucidio Formiga Cabral Federal University of Paraíba
  • Thiago Luís Lopes Siqueira São Paulo Federal Institute of Education, Science and Technology
  • Ricardo Rodrigues Ciferri Federal University of São Carlos
  • Cristina Dutra de Aguiar Ciferri University of São Paulo


Data Warehouse, Materialized Views, Performance Evaluation


Although the materialization of views reduces the execution time of OLAP queries, the materialization of a large number of views may exceed computer storage thresholds. Thus, given a certain storage cost threshold, there is a need for selecting the best views to be materialized, i.e. views that ?t the storage requirements and provide the lowest response time to process OLAP queries. Several solutions have been proposed in the literature to solve this problem. However, most studies have adopted strictly greedy or purely random approaches. Also, most of them do not encompass the entire cycle of execution of multidimensional analysis, or do not specify and implement the whole cycle of multidimensional query execution. In this paper, we address these issues by proposing a novel method for selecting and materializing views based on OLAP signatures and GRASP (Greedy Randomized Adaptive Search). On the one hand, using OLAP signatures and their relationships with descriptions of the data cube, we are able to identify which views should be materialized for being more beneficial to the user query processing. On the other hand, using GRASP allows us to de?ne a hybrid method, which traverses the solution space in a comprehensive manner as performed in purely random approaches, while examines only the regions of the search space with a great concentration of good solutions generated by a greedy approach. GRASP was compared to other VSP algorithms, namely Pick by Size (PBS) and Ant Colony Optimization (ACO), and performance tests indicated that compared to PBS, the proposed method obtained a time reduction of about 20.4% in query processing. In addition, GRASP was more scalable than PBS, since it selected and materialized a smaller set of views, even when there was a wide range of possible views to be chosen. Also, GRASP obtained nearly the same query runtime of ACO (i.e. a small performance loss of about 2.84% was obtained by GRASP), but a shorter time for the selection of views than the ACO algorithm (i.e. a gain in processing time of about 77% was produced by GRASP). 


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