With the use of databases over the past decades, large volumes of data have been accumulated. To integrate and manage the data effectively and systematically, data warehouses have emerged. In addition, OLAP and data mining, which use the data warehouse, have become important research topics. OLAP allows users to easily analyze the data in the data warehouse in order to acquire information necessary for decision making. Data mining extracts unknown useful knowledge from the data warehouse. Data warehousing is a collection of decision making techniques aimed at enabling the knowledge worker to make better and faster decisions.

Data warehousing techniques can be classified into three categories: data warehouses, OLAP, and data mining. Research issues in the first are data cleaning, data warehouse refreshment, physical and logical design of a data warehouse, and meta data management. Research issues in OLAP are multidimensional data models, OLAP query languages, query processing, and system architectures---ROLAP (Relational OLAP) using relational databases, MOLAP (Multidimensional OLAP) using multidimensional indexes, and HOLAP (Hybrid OLAP) combining ROLAP and MOLAP. Data mining involves various techniques such as association rules, classification, clustering, and similarity search. Many new mining techniques are continuously developed.

We are focusing on the processing techniques for processing aggregation queries in OLAP and time-series subsequence matching and clustering in data mining. In particular, we emphasize the approaches using multidimensional indexes, such as the Multi-Level Grid File (MLGF), in OLAP and data mining. The results so far have proven outstanding. The current research topics are as follows:

• Aggregate algorithms using multidimensional files in MOLAP
• Efficient time-series subsequence matching using duality in constructing windows
• Index interpolation : a subsequence matching method supporting preprocessing transforms
• A fast clustering method using multidimensional indexes