Mining knowledge from databases

Abstract

Most people consider a database is merely a data repository that supports data storage and retrieval. Actually, a database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, interconnected, heterogeneous information networks. Much knowledge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this tutorial, we introduce database-oriented information network analysis methods and demonstrate how information networks can be used to improve data quality and consistency, facilitate data integration, and generate interesting knowledge. This tutorial presents an organized picture on how to turn a database into one or a set of organized heterogeneous information networks, how information networks can be used for data cleaning, data consolidation, and data qualify improvement, how to discover various kinds of knowledge from information networks, how to perform OLAP in information networks, and how to transform database data into knowledge by information network analysis. Moreover, we present interesting case studies on real datasets, including DBLP and Flickr, and show how interesting and organized knowledge can be generated from database-oriented information networks.