Topic modeling for OLAP on multidimensional text databases: topic cube and its applications

Abstract

Abstract: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. Although 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 study 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 stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.

Of Cubes, DAGs and Hierarchical Correlations: A Novel Conceptual Model for Analyzing Social Media Data

“…In topic cube [15] the authors propose a topic dimension with a hierarchical topic tree (specified by the analyst) for the text field, and probabilistic distributions as the content measure of text documents in the hierarchical topic dimension. On one hand this allows the analyst to drill-down and roll-up on the text dimension and on the other, to accommodate different kinds of summaries of the content of the text documents.…”

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

“…DWs are mainly used for OLAP (online analytical processing) operations. OLAP is the approach to provide report data from DW through multi-dimensional queries and it is required to create a multi-dimensional database [24].…”

“…The dimension tables represent characteristics that describe the fact table. When star schemas become too complex to be queried efficiently they are transformed into multi-dimensional arrays of data called OLAP cubes (for more information on how this transformation is performed the reader can consult the following references [24] [25]).…”

“…A star schema is transformed to a cube by putting the fact table on the front face that we are facing and the dimensions on the other faces of the cube [24]. For this reason, cubes can be equivalent to star schemas in content, but they are accessed with more platform-specific languages than SQL that have more analytic capabilities (e.g.…”

“…By doing this, the user obtains another perspective of the data, which requires the queries to have a different structure but can be more beneficial for specific queries. For instance, he can slice and dice the cube away to get the results he needed, but sometimes with a pivot most of those operations can be avoided by perceiving a common structure on future queries and pivoting the cube in the correct fashion [23] [24]. Figure 7 (bottom-right) shows a pivot operation where years are arranged vertically and countries horizontally.…”