Revisting SQL Query Recommender System Using Hierarchical Classification

Abstract

For analytical purposes, lots of data are gathered which are gathered and explored in data warehouses. Even to handle such a large data is a tough task for expert people. For non-expert users or for users who are not familiar with the database schema, handling such a voluminous data is more difficult task. The aim of this paper is to facilitate this class of users by recommending them SQL queries that they may use. By following the users past behavior and comparing them with other users, these SQL recommendations are selected. Initially, users may not know from where they can start their exploration. Secondly, users may overlook queries which help them to retrieve important data. Using hierarchical classification, the queries are recorded and compared which is then re-ranked according to relevance. Using users querying behavior, the relevant queries are retrieved. To issue a series of SQL queries, users use a query interface which aim to analyze the data and mine it for interesting information.
DOI: 10.17762/ijritcc2321-8169.1506141