Frequent Query Matching in Dynamic Data Warehousing

Businesses collect large amounts of data from transactions in order to gain insight into their performance and growth. Decisions must be made based on this data to support the everchanging needs of customers and business requirements. In present days’ the data warehouse has become a popular tool for decision making among businesses. Traditional data warehouses (DW) are subject-oriented, time variant, nonvolatile and integrated data repositories that are capable of providing decision support as data is kept in an aggregated form [16]. Commonly, the data from transactional data sources are extracted and transformed into an aggregated form via the Extract-Transform-Load (ETL) process to be stored in the data warehouse and then onwards users can either execute On-Line Analytical Processing [3, 5, 18, 19] (OLAP) queries on it or create smaller data marts from it. Several data sources may have to be integrated to create the data warehouse. The query results can then be used for analytical purposes, for example to identify customer trends, so that business owners or management can make important business decisions based on this information.