PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING
Tzung‐Pei Hong, Ching-Yao Wang, Jerry Chun‐Wei Lin · International Journal of Information Technology & Decision Making · 2010
Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.