Application of clustering-based decision tree approach in SQL query error database

Lino, Adriano Del Pino; Rocha, Álvaro; Macedo, Luís; Sizo, Amanda

doi:10.1016/j.future.2018.10.038

Cited by 11 publications

(22 citation statements)

References 10 publications

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“…In this section, the experimental results of the proposed BIBSQLQC technique and existing methods [19][20][21] are discussed with different metrics such as DA, FPR and TC. Performances of all the 3 methods are estimated and the results are reported in the Table and a 2-dimensional graph.…”

Section: Resultsmentioning

confidence: 99%

“…The designed method failed to improve antipattern detection accuracy and minimizes the time complexity. A cluster-based decision tree approach was introduced [21] to identify the most relevant patterns and syntax error from the database.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental evaluation of BIBSQLQC technique and the existing methods namely feature engineering technique [19], pattern detection method [20] and clustering based decision tree approach [21] are implemented using Java language. The SQL query log such as IIT Bombay DS and UB Exam DS are used for conducting the experiments.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Totally 10 different runs are performed using 2 different DS with a number of queries. The performance of BIBSQLQC technique and existing methods namely, feature engineering technique [19], pattern detection method [20] and clustering based decision tree approach [21] are evaluated with certain metrics such as DA, FPR and TC.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The DA of normal and antipattern is portrayed in Tables 1 and 2 with IIT Bombay DS and UB exam DS. Tables 1 and 2 clearly show DA for 3 different methods namely BIBSQLQC and the existing methods [19][20][21] with the number of queries (i.e. patterns).…”

Section: Performance Analysis Of Detection Accuracymentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%