Discovering topical structures of databases

The increasing complexity of enterprise databases and the prevalent lack of documentation incur significant cost in both understanding and integrating the databases. Existing solutions addressed mining for keys and foreign keys, but paid little attention to more high-level structures of databases. In this paper, we consider the problem of discovering topical structures of databases to support semantic browsing and large-scale data integration. We describe iDisc, a novel discovery system based on a multi-strategy learning framework. iDisc exploits varied evidence in database schema and instance values to construct multiple kinds of database representations. It employs a set of base clusterers to discover preliminary topical clusters of tables from database representations, and then aggregate them into final clusters via meta-clustering. To further improve the accuracy, we extend iDisc with novel multiple-level aggregation and clusterer boosting techniques. We introduce a new measure on...

• Type: Conference
• Year: 2008
• Where: SIGMOD
• Authors: Wensheng Wu, Berthold Reinwald, Yannis Sismanis, Rajesh Manjrekar
• Keywords: Additional Database Representations, Database, Database Representations, Preliminary Topical Clusters, SIGMOD 2008