Integrative text mining and management in multidimensional text databases
Duo Zhang · 2012
As the text information grows explosively in today’s multidimensional text databases, managing and mining this kind of databases is now playing an extremely important role in every domain. Different from traditional text mining tasks that target at single data sets, a text management system for a multidimensional database requires its text mining functions performed in different contexts specified by the structured dimensions, and the system should well support OLAP (online analytical processing) of the text information. This is a big challenge for most existing text mining techniques because of the efficiency and the scalability issues. On the other hand, the huge amount of text information in such databases also provides us an opportunity of acquiring new knowledge out of it, which could be super beneficial. In this thesis, I identified three major types of functions that a text management system should support in order to analyze multidimensional text databases: (1) effective and efficient digestion: the system should support users to digest the text information in an OLAP environment based on domain knowledge; (2) flexible exploration: the system should allow users to flexibly explore the text information based on ad hoc information needs; (3) discovery analysis: the system should