• Mr.P.Hariharan
• M.Rajanbabu

Database access Query logs are utilized in a wide heterogeneity of settings, including assessing the exhibition tuning of databases, creating benchmarks, or helping security investigators evaluate the potential as well as Percentage of a security Breach, and validating compliance. In addition, more, numerous clients driven frameworks Utilize Accessed query logs to help clients by making suggestions and harmonize the client experience. Although, logs from enterprise database systems are dreadfully huge and inconvenient, and an analyst may find it hard to extract wide patterns from the collection of queries discovered in them. Clustering a characteristic initial phase in understanding the huge logs of queries. Many clustering techniques, however, rely upon the Knowledge of pair-wise likeness, which is hard to calculate for SQL queries, especially when the fundamental data and database plan are not accessible. We plan to expand our work in a numerous ways to explore new feature extraction mechanisms such as the Weisfeiler-Lehman framework to analyze database access patterns by clustering SQL queries. Then function strategies for weighting and fresh guidelines for labeling to more readily reproduce the task behind logged queries. Exploring the correlation of inter-question highlights dependent on query request can be utilized in connection to clustering to summarize query logs. Distinctive feature sorting approach to enable the user to recognize significant and unessential features in Frequent Pattern Trees (FP Trees). In the Previous Work applied to query similarity clustering on different accessed query log datasets, we play out an intensive appraisal of three query similarity heuristics examining, representing distinct sorts of query workloads. We suggest a generic function engineering approach to enhance the exactness of the three heuristics examining, using traditional query rewriting to standardize the query structure.

Frequent Patterns, Clustering; Query Logs; association mining, algorithm, performance improvements.

"Correlation Feature Clustering For SQL Query Logs Using Frequent-Pattern Tree Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.429-440, June 2019, Available :http://www.jetir.org/papers/JETIR1907A89.pdf

"Correlation Feature Clustering For SQL Query Logs Using Frequent-Pattern Tree Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp429-440, June 2019, Available at : http://www.jetir.org/papers/JETIR1907A89.pdf