iNextCube: Information Network-Enhanced Text Cube

Nowadays, most business, administration, and/or scientific databases contain both structured attributes and text attributes. We call a database that consists of both multidimensional structured data and narrative text data as multidimensional text database. Searching, OLAP, and mining such databases pose many research challenges. To enhance the power of data analysis, interesting entities and relationships can be extracted from such databases to derive heterogeneous information networks, which in turn will substantially increase the power and flexibility of data exploration in such databases. Based on our previous studies on TextCube [1], TopicCube [2], and information network analysis, such as RankClus [3] and NetClus [4], we construct iNextCube, an information-Network-enhanced text Cube. In this demo, we show the power of iNextCube in the search and analysis of two multidimensional text databases: (i) a DBLP-based CS bibliographic database, and (ii) an online news database.