Query Processing In Text Mining - BhanuPrakash, N; Kesavulu Reddy, E (International Journal of Computer Sciences and Engineering, 2021)
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.