Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2022F257

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 139–142 (2022)

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Abstract. Information about the existence of periodic patterns in a database workload can play a big part in the process of database tuning. However, full analysis of audit trails can be cumbersome and time-consuming. This paper discusses a heuristic algorithm that focuses on workload reconstruction based on pattern discovery in a simplified workload notation. This notation is based on multisets representing database actions (such as user queries) requiring access to specific persistent objects, but without the access cost analysis. Each action in this notation is a multiset of accessed objects, which can be tables, system files, views, etc. The theoretical model for such an approach has been discussed in detail in the authors' previous work.This work is mostly proof-of-a-concept for the theoretical approach. Additionally, in order to test the performance of the proposed algorithm, a test-data generator has been constructed. Both the previous and the current papers are parts of a research project dealing with the application of periodic pattern theory to the field of database optimization and tuning.

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