TY - JOUR
T1 - Materialized View Selection as Constrained Evolutionary Optimization
AU - Choi, CH
AU - Yu, X
AU - Yao, Xin
AU - Gou, G
PY - 2003/11/1
Y1 - 2003/11/1
N2 - One of the important issues in data warehouse development is the selection of a set of views to materialize in order to accelerate a large number of on-line analytical processing (OLAP) queries. The maintenance-cost view-selection problem is to select a set of materialized views under certain resource constraints for the purpose of minimizing the total query processing cost. However, the search space for possible materialized views may be exponentially large. A heuristic algorithm often has to be used to find a near optimal solution. In this paper, for the maintenance-cost view-selection problem, we propose a new constrained evolutionary algorithm. Constraints are incorporated into the algorithm through a stochastic ranking procedure. No penalty functions are used. Our experimental results show that the constraint handling technique, i.e., stochastic ranking, can deal with constraints effectively. Our algorithm is able to find a near-optimal feasible solution and scales with the problem size well.
AB - One of the important issues in data warehouse development is the selection of a set of views to materialize in order to accelerate a large number of on-line analytical processing (OLAP) queries. The maintenance-cost view-selection problem is to select a set of materialized views under certain resource constraints for the purpose of minimizing the total query processing cost. However, the search space for possible materialized views may be exponentially large. A heuristic algorithm often has to be used to find a near optimal solution. In this paper, for the maintenance-cost view-selection problem, we propose a new constrained evolutionary algorithm. Constraints are incorporated into the algorithm through a stochastic ranking procedure. No penalty functions are used. Our experimental results show that the constraint handling technique, i.e., stochastic ranking, can deal with constraints effectively. Our algorithm is able to find a near-optimal feasible solution and scales with the problem size well.
UR - http://www.scopus.com/inward/record.url?scp=0242611926&partnerID=8YFLogxK
U2 - 10.1109/TSMCC.2003.818494
DO - 10.1109/TSMCC.2003.818494
M3 - Article
VL - 33
SP - 458
EP - 467
JO - IEEE Transactions on Systems, Man and Cybernetics, Part C
JF - IEEE Transactions on Systems, Man and Cybernetics, Part C
IS - 4
ER -