Methods for solving the mean query execution time minimization problem

• It is shown that greedy algorithms can solve efficiently the view selection problem.
• Several algorithms for the view selection problem are comprehensively compared.
• Improvements of used algorithms for the view selection problem are considered.
• The proposed methods can be used to solve the largest problems considered so far.

One of the most significant and common techniques to accelerate user queries in multidimensional databases is view materialization. The problem of choosing an appropriate part of data structure for materialization under limited resources is known as the view selection problem. In this paper, the problem of the mean query execution time minimization under limited storage space is studied. Different heuristics based on a greedy method are examined, proofs regarding their performance are presented, and modifications for them are proposed, which not only improve the solution cost but also shorten the running time. Additionally, the heuristics and a widely used Integer Programming solver are experimentally compared with respect to the running time and the cost of solution. What distinguishes this comparison is its comprehensiveness, which is obtained by the use of performance profiles. Two computational effort reduction schemas, which significantly accelerate heuristics as well as optimal algorithms without increasing the value of the cost function, are also proposed. The presented experiments were done on a large dataset with special attention to the large problems, rarely considered in previous experiments. The main disadvantage of a greedy method indicated in literature was its long running time. The results of the conducted experiments show that the modification of the greedy algorithm together with the computational effort reduction schemas presented in this paper result in the method which finds a solution in short time, even for large lattices.