Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases.

As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. While online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we propose a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and store probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose a heuristic method to speed up the iterative EM algorithm ...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: ChengXiang Zhai, Duo Zhang, Jiawei Han
• Keywords: Computer Science, Probabilistic Topic Modeling, Probabilistic Topic Models, SDM 2009, Topic Cube