Description

Our aim to apply prediction concept on Data mining to extract the future relevant records. Our process applies the supermarket records about marks to generate the predictors on previous data records. We apply this concept to easily identify the future incident. We apply the naïve bayes algorithm. We aim to generate the efficient and accurate records predicates. Moreover, we designed a histogram-based probabilistic method of set predicate selectivity estimation, for optimizing queries with multiple predicates. The experiments verified its accuracy and effectiveness in optimizing queries. Such queries are not only difficult to write but also challenging for a database engine to optimize, thus can result in costly evaluation. This concept proposes to augment SQL with set predicate, to bring out otherwise obscured set-level semantics.