Logical schema-based mapping technique to reduce search space in the data warehouse for keyword-based search

• Published in: International arab journal of information technology Vol. 14; no. 1
• Main Authors: Majid, Fayyd, Shuayb, Muhammad
• Format: Journal Article
• Language: English
• Published: Zarqa, Jordan Zarqa University 2017
• Subjects: Data warehousing; Database management; Database searching; Decision support systems; Keyword searching; OLAP technology; الإدارة; البحث الإلكتروني; قواعد البيانات; مستودعات البيانات
• ISSN: 1683-3198; 1683-3198

Data warehouse systems are used for decision-making purposes. The Online Analytical Processing (OLAP) tools are commonly used to query and analysis of results on such systems. It is complex task for non-technical users (executives, managers etc.,) to query the data warehouse using OLAP tool keeping in view the schema knowledge. For such data warehouse users, a natural language interface is a viable solution that transparently access data to fulfil their requirement. As data warehouse contain several times more data (that increase with incremental refreshes) than the operational systems. So keyword-based searching in such systems cannot be performed similar to database based natural language systems. Existing natural language interfaces to data warehouse commonly explore keywords in data instances directly that takes more than sufficient time in generating results. This paper proposes a Logical Schema-based Mapping (LSM) technique to reduce search space in the data warehouse data instances. It performs mapping of the natural language query keywords with logical schema of the data warehouse to identify the elements prior to search in the data instances. The retrieved matches for a keyword are ranked based on six criteria proposed in this paper. Further, an algorithm has been presented which is developed upon the proposed criteria. Targeted search in the data instances is then performed efficiently after the identification of schema elements. The in-depth experiments have been carried out on real dataset to evaluate the system with respect to completeness, accuracy and performance parameters. The results show that LSM technique outperforms the existing systems.