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Currently, OLAP systems capable of handling a huge amount of data tend to use two types of storage device based on NSM and DSM, respectively.Conventional approaches for query optimization in OLAP systems usually classify issued queries according to their operation type, e.g., insert, delete, or update, and thereby select the data storage device to be used. Briefly, focusing on the type of query issued to the OLAP systems makes is possible to find another approach to improve query processing time.In this paper, we propose a method for optimizing query processing in an OLAP system with two types of storage device that considers the type of each query.

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Recent OLAP systems, which are usually called HOLAP systems, are often developed using both NSM and DSM storages in a single database system for big data analytics in real-time. In HOLAP systems, a method for two types of storages usually depends on the type of SQL queries (e.g., insert, delete, or update). In short, we have the potential to find another approach to improve query processing time if we focus on the characteristics of data that an issued query handles.In this paper, we propose a method for optimizing query processing in a HOLAP system considering the four types of data characteristics such as those of the data extracted by a correlated subquery and by a join operation using tables that are different in size with an appropriate index construction.