Storage structures for efficient query processing in a stock recommendation system

Rule discovery is an operation that uncovers useful rules from a given database. By using the rule discovery process in a stock database, we can recommend buying and selling points to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investment types. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure performs best in query processing and improves the performance of other ones in orders of magnitude.