ABSTRACT
Data management is the key component of any government, private and scientific organization. Data warehouses are maintained and used to store data from a historical perspective and to support the task of data analysis. Furthermore, data analysis is a very crucial process for the generation of valid patterns and knowledge which in turn is useful in the organizational decision-making process. Research shows that data warehousing plays a vital role in data analysis and decision-making process, but there are some unaddressed issues which make it difficult to mine relevant patterns and valuable information from a business analyst perspective. This chapter proposes the conceptual and architectural design of genetic - based optimal pattern warehousing system which includes a well-organized architecture of an Optimal Pattern Warehousing System (OPWS) and a genetic-based optimal pattern mining algorithm to effectively mine optimal and future frequent patterns from pattern warehouse and store them in Optimal Pattern Warehouse (OPWH). Furthermore, this proposed architectural design is expected to support the effective generation, storage and analysis of high quality patterns thereby, eliminating the need to deal with a massive amount of data and gives the opportunity to refer only the real, quality and optimal patterns. It also offers the flexibility to predict future frequent patterns which are completely missing from above scenario and is very important from the business community point of view.
