Towards Integrated Study of Data Management and Data Mining

Abstract

Abstract From very beginning, research and practice of database management systems (DBMSs) have been cantered on handling granulation and granularities at various levels, thus sharing common interests with granular computing (GrC). Although DBMS and GrC have different focuses, the advent of Big Data has brought these two research areas closer to each other, because Big Data requires integrated study of data storage and analysis. In this paper, we explore this issue. Starting with an examination of granularities from a database perspective, we discuss new challenges of Big Data. We then turn to data management issues related to GrC. As an example of possible cross-fertilization of these two fields, we examine the recent development of database keyword search (DBKWS). Even research in DBKWS is largely independent to GrC, DBKWS has to handle various issues related to granularity handling. In particular, aggregation of DBKWS results is closely related to studies in granularities and granulation, which echoes L. Zadeh's famous formula: Granulation = Summarization. We present our proposed approach, termed as extended keyword search, which illustrates that an integrated study of data management and data mining/analysis is not restricted to GrC or rough set theory