Memory-adaptive association rules mining

New application areas resulted in an increase of the diversity of the workloads that Data Base Management Systems have to confront. Resource management for mixed workloads is attained with the prioritization of their tasks, which during their execution may be forced to release some of their resources. In this paper, we consider workloads that consist of mixtures of OLTP transactions and association rule mining queries. We propose and evaluate a new scheme for memory-adaptive association rule mining. It is designed to be used in the case of memory fluctuations, which are due to OLTP transactions that run with higher priority. The proposed scheme uses dynamic adjustment to the provided buffer space. Thus, it avoids the drawbacks of simple but naive approaches; namely the thrashing due to large disk accesses that can be caused by the direct use of virtual memory or long delay times due to suspension. Detailed experimental results, which consider a wide range of factors, indicate the superiority of the proposed scheme. r 2003 Elsevier Ltd. All rights reserved.