Integrative text mining and management in multidimensional text databases
type: CreativeWork
description: 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 effectively analyze the text data with consideration of the associated non-textual data and mine knowledge underlying the text information. All of these functions are integrative analysis of the structured data and the unstructured text data within a multidimensional text database. I proposed and studied different novel models and infrastructures to support all the above functions. First, I proposed a novel model called Topic Cube which combines the OLAP technology for traditional data warehouses with probabilistic topic modeling approaches for text mining. Given a topic hierarchy based on domain knowledge, a topic cube mines semantic topics accordingly and organizes the text informati…
abstract: 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 effectively analyze the text data with consideration of the associated non-textual data and mine knowledge underlying the text information. All of these functions are integrative analysis of the structured data and the unstructured text data within a multidimensional text database. I proposed and studied different novel models and infrastructures to support all the above functions. First, I proposed a novel model called Topic Cube which combines the OLAP technology for traditional data warehouses with probabilistic topic modeling approaches for text mining. Given a topic hierarchy based on domain knowledge, a topic cube mines semantic topics accordingly and organizes the text informati…
author: Zhang, Duo
language: en
keywords: Text Mining, Multidimensional Text Database, Topic Cube, MiTexCube, Probabilistic Topic Mapping
date published: 2013-02-03T19:36:28Z
date modified: 2026-07-22T22:25:33Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/42363