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.
['Takamitsu Shioi', 'Kenji Hatano']
Query processing optimization using disk-based row-store and column-store