行列技術を用いた動的ネットワーク可視化

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大澤 幸生, 東京大学教授 青山 和浩, 東京大学教授 和泉 潔, 東京大学准教授 森 純一郎, 首都大学東京教授 高間 康史University of Tokyo(東京大学

Change and planning in chance discovery.

The discovery of risks and opportunities, known collectively as chances, can have a significant impact on decision making. Chances (risks or opportunities) can be discovered from our daily observations and background knowledge. A person can easily identify chances in a news article. In doing so, the person combines the new information in the article with some background knowledge. Hence, we develop a deductive system to discover relative chances with respect to a particular chance seeker. A chance discovery system that uses a general purpose knowledge base and specialized reasoning algorithms is proposed. The thesis evaluates the implementation of this chance discovery system and discusses the achievements and limitations of its elements, such as Natural Language Processing Tool, Knowledge Entry Tool, Inference Engine and Planner. Finally, A case study about a virtual transportation planning domain implemented using SHOP planner is presented. Example chances are detected in this domain. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .W89. Source: Masters Abstracts International, Volume: 44-03, page: 1418. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Text mining analysis roadmap (TMAR) for service research

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts. </jats:sec