Companies often use relational database management systems (RDBMS) such as Oracle and Inform mix, to store their data persistently. The database technology developed and deployed in RDBMS is relatively mature. Besides efficient storage and retrieval, this technology provides many additional features such as concurrency control, recoverability, and high availability. Thirdly, the rigid structure of relational data makes it amenable to complex queries and analysis such as on-line analytical processing (OLAP), the predecessor of data mining. There are many different techniques and algorithms for relational data that can be classified as data mining. There are roughly four broad classes i.e. clustering, classification, sequence analysis, and associations. We consider data mining for structured data from a database perspective. As a consequence in association rules will be featured more prominently than the other three classes of mining problems. Query flocks are an elegant framework for a large class of data mining problems over relational data. The main features of query flocks are declarative formulation of a large class of mining queries. Systematic optimization and processing of such queries Integration with relational DBMS, taking full advantage of existing capabilities. This paper focus mainly on the declarative formulation of mining problems as query flocks.

RDBMS, Clustering, Query flocks, Query Optimization, Relational data, Classification, Clustering, sequence Analysis

[1]. R. Agrawal, T. Imilienski, and A. Swami. “Mining association rules between sets of items in large databases” in the Proceedings of ACM SIGMOD International Conference on Management of Data, pages 207{216, May 1993.
[2]. S. Brin, R. Motwani, D. Tsur, and J. Ullman. “Dynamic itemset counting and implication rules for market basket data.” in the Proceedings of ACM SIGMOD International Conference on Management of Data, pages 255{264, Tucson,Arizona, June 1997.
[3]. R. Srikant and R. Agrawal. “Fast algorithms for mining association rules” in the Proceedings of the 21th International Conference on Very Large Data Bases, pages 407{419, Zurich, Switzerland, September 1995.
[4]. E. Han, G. Karypis, and V. Kumar “Scalable parallel data mining for association rules” in the Proceedings of ACM SIGMOD International Conference on Management of Data, pages 277{288, Tucson, Arizona, June 1997.
[5]. D. W. Cheung, J. Han, V. Ng, and C. Y. Wong. “Maintenance of discovered association rules in large databases: An incremental updating technique” in the Proceedings of ICDE, pages 106{114, New Orleans, Louisiana, February 1996.
[6]. R. Motwani, E. Cohen, M. Datar, S. Fujiware, A. Gionis, P. Indyk, J. Ullman, and C. Yang, “ Finding interesting associations without support pruning”In Proceedings of ICDE, San Diego, California, March 2000.
[7]. S. Fujiware, R. Motwani, and J. Ullman " Dynamic miss-counting algorithms: Finding Implication and similarity rules with con dence pruning”, in the Proceed-ings of ICDE, San Diego, California, March 2000.
[8]. H. Mannila “Methods and problems in data mining” in the Proceedings of International Conference on Database Theory, pages 41{55, Delphi, Greece, January 1997.
[9]. R. Ng, L. Lakshmanan, J. Han, and A. Pang. “Exploratory mining and pruning optimizations of constrained associations rules” in the Proceedings of ACM SIG-MOD International Conference on Management of Data, pages 13{24, Seattle,Washington, June 1998.
[10]. A. Chandra and P. Merlin “Optimal implementation of conjunctive queries in relational databases” in the Proceedings of 9th Annual ACM Symposium on the Theory of Computing, pages 77{90, Boulder, Colorado, May 1977.
[11]. J.D. Ullman “Principles of Database and Knowledge-Base Systems” in Volume I - Fundamental Concepts. Computer Science Press, Rockville, Maryland, 1988.
[12]. J.D. Ullman and J. Widom. A First Course in Database Systems. Addison Wesley, Reading, Massachusetts, 1997.
[13]. B. Wang, S. Yu, W. Lou, and Y. T. Hou, “Privacy-preserving multi keyword fuzzy search over encrypted data in the cloud,” in IEEE INFOCOM, 2014.
[14]. V.Kaltsa, K. Avgerinakis, A. Briassouli, I. Kompatsiaris and M. Strintzis, "Dynamic texture recognition and localization in machine vision for outdoor environments," Computers in Industry, vol. 98, 2018.