Predicting database workloads through mining periodic patterns in database audit trails

Zimniak, Marcin; Getta, Janusz R.; Benn, Wolfgang

doi:10.1007/s40595-015-0042-0

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…The theory and applications of the concept of periodic patterns to the workload prediction problem were discussed in the previous works of one of the authors [5], [2]. The theory of periodic patterns is well known.…”

Section: Methodsmentioning

confidence: 99%

“…The degree of reconstruction allows to evaluate the predictions of the whole system; the methodology of the algorithm takes into account the simultaneous inclusion of the time and frequency domains in each step of the reconstruction algorithm, for all periodic patterns included in the reconstruction. The problem of workload predicting has already been discussed by the author [2] in terms of another concept of the periodic pattern. The methods of signal analysis or wavelets are based on the assumption (in short) that points in time have been assigned values (real numbers), including the use of appropriate reconstruction methods [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Heuristic algorithm for periodic patterns discovery in a database workload reconstruction

Zimniak¹,

Burzańska²,

Wiśniewski³

et al. 2022

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

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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 [1] This work is mostly proof-of-a-concept for the theoretical approach. Additionally, in order to test the performance of the proposed algorithm, a testdata 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 [2], [3], [4], [5].

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic algorithm for periodic patterns discovery in a database workload reconstruction

Zimniak¹,

Burzańska²,

Wiśniewski³

et al. 2022

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Identification of complex periodic patterns in the SQL query database was performed [9] with minimum time consumption. However, this approach also failed to discover the antipatterns based on clustering process.…”

Section: Related Workmentioning

confidence: 99%

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Ramakrishnan¹,

Palanisamy²

2020

Turk J Elec Eng & Comp Sci

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Discovery of antipatterns from arbitrary SQL query log depends on the static code analysis used to enhance the quality and performance of software applications. The existence of antipatterns reduces the quality and leads to redundant SQL statements. SQL log includes a large load on the database and it is difficult for an analyst to extract large patterns in a minimal time. Existing techniques which discover antipatterns in SQL query face a lot of innumerable challenges to discover the normal sequences of queries within the log. In order to discover the antipatterns in the log, an efficient technique called Brown infomax boosted SQL query clustering (BIBSQLQC) technique is introduced. Initially, the number of patterns (i.e. queries) are extracted from the SQL query log. After extracting the patterns, the ensemble clustering process is carried out to find out the antipatterns from the given query log. The Brown infomax boost clustering is an ensemble learning method for grouping the patterns by constructing several weak learners. The Brown clustering is used as a weak learner for partitioning the patterns into 'k' number of clusters based on the Euclidean distance measure. Then the weak learner merges the two clusters with maximum information gained to minimize the time complexity. The clustering results of weak learners are combined into strong results with minimal error rate (ER). By this way, the antipattern in the SQL query log is detected with a higher accuracy. Experimental evaluation is conducted with different parameters namely detection accuracy (DA), false positive rate (FPR) and time complexity (TC) using the two SQL query log data-sets (DS). The experimental result shows that, the BIBSQLQC technique achieves higher DA with lower TC and FPR than the conventional methods.

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“…The present work is based on a simple technique of workload estimation originally developed in [10]. The works on conformance checking in process mining has been triggered by the applications of process mining to analysis of transaction logs and audit trails.…”

Section: Previous Workmentioning

confidence: 99%

“…In [10] a carrier C is a nonempty, finite sequence of multisets of syntax trees of relational algebra expressions implementing SQL statements. A range R is a pair of the ordinal numbers of time unit where the repetitions of a carrier start and end.…”

Section: Basic Conceptsmentioning

confidence: 99%

Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

Getta¹,

Zimniak²

2016

Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering

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Analysis of historical database workloads can be used to improve the future performance of database systems. This work investigates a problem how to use information about periodic processing of database applications recorded in the past for static and dynamic estimation of the future database workloads. The new concepts of anchor events and event expressions are used to create the mappings of past periodic patterns in data processing into the future database workloads. We show that event expressions allow for both static and dynamic estimation of future database workloads. The paper presents the algorithms for static a dynamic estimation of future database workloads from information about time distributions of periodic processing of database applications and information about the events that trigger processing of the applications.

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Predicting database workloads through mining periodic patterns in database audit trails

Cited by 6 publications

References 18 publications

Heuristic algorithm for periodic patterns discovery in a database workload reconstruction

Heuristic algorithm for periodic patterns discovery in a database workload reconstruction

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Using Periodic Patterns for Dynamic Estimation of Future Database Workloads

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