Keyword Search in Large-Scale Databases with Topic Cluster Units

To solve the inefficiency of the existing keyword search methods in large databases, this paper proposes TCU-based query, an offline query method based on topic cluster units. First, topic cluster units (TCUs) are constructed through vertical grouping and horizontal grouping on tables and tuples. In contrast to traditional keyword query methods, this offline method cannot only reduce the query response time, but also return results comprising richer and more complete semantic information. In order to further improve the efficiency of data preprocessing, an optimized solution for table join ordering based on the genetic algorithm is presented. Second, we select index terms using the association rule, and then we build an index on every topic cluster; by doing so we can improve the query speed significantly. Finally, we conduct extensive experiments to demonstrate that our approach greatly improves the performance of keyword search

A Semantic Approach for Keyword Search on Relational Databases

Today’s search engines make it easier for the user to browse and query the online available data. But when it comes to structured data, the queries have to be structured too, in order to retrieve the data. This makes it difficult for novice users, with no knowledge of the underlying schema or query language, to access the relational data. Therefore, to query the structured data in an unstructured language of web, there is a need to map the user keyword queries to their equivalent SQL format. This research is intended to bridge the gap by introducing a framework named STRUCT. Unlike most of the existing work which pays very little attention to the contextual information provided by the user, our approach takes these details into account to elucidate the implied structural information necessary for constructing the SQL clauses. One fundamental issue on keyword search in traditional databases is how to interpret users’ information needs behind keywords they provided. A common approach of many prototype systems is to make such interpretation as a designer’s choice (such as imposing AND or OR semantics, or a combination), leaving no choice to users. A much more meaningful approach would be allowing users themselves to specify the required semantics through contextual information. So can we build a system which stays with the simplicity of Keyword search, yet can incorporate the contextual information provided in the user query? STRUCT answers this question by taking English language queries involving intended keywords. Instead of resorting on a full-fledged natural language processing, the unneeded words in the queries are discarded. Only the specific contextual information along with the keywords containing database contents will be used to construct SQL queries. The contextual information is used to interpret the meaning of the queries, including the semantics involving AND,OR and NOT. In this thesis we describe the architecture of STRUCT, procedure of English query processing (parsing), basic idea of the grouping algorithm, SQL query construction and sample results of experiments