OPTIMAL QUERY RECOMMENDATION IN SPATIAL DATA WAREHOUSE USING DEMOCRATIC GREY WOLF OPTIMIZATION

Data warehouse comprises of large amounts of data of queries provided by the user. The user might ignore the part of the data in the warehouse, where required information is present. When the relevant queries are provided, then the user find easy to obtain the information they are needed. In this research, the Spatial Online Analytical Process (SOLAP) is used to exploit spatial data in the warehouse and Similarity Measurement for the Text Prediction (SMTP) is used to recommend the relevant queries. SOLAP retrieves the relevant information and recommend the queries with the help of SMTP, Probabilistic Fuzzy-c-means (PFCM) and Democratic Grey Wolf Optimization (DGWO). PFCM retrieves the similar features in between text and SMTP relate the similarity between the current queries to the previous queries in the log. DGWO provides the optimized solution for the query recommendation. The experimental result showed that this method provided higher relevant queries to the user