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Ting-Peng Liang

Researcher at National Sun Yat-sen University

Publications -  201
Citations -  11845

Ting-Peng Liang is an academic researcher from National Sun Yat-sen University. The author has contributed to research in topics: Information system & Decision support system. The author has an hindex of 48, co-authored 198 publications receiving 10335 citations. Previous affiliations of Ting-Peng Liang include Purdue University & City University of Hong Kong.

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Journal ArticleDOI

Integrating model management with data management in decision support systems

TL;DR: A general framework for model management, which can integrate model management and data management and handle issues in model management such as model creation, model modification and model use is proposed.
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FILM: a fuzzy inductive learning method for automated knowledge acquisition

TL;DR: A fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductivelearning processes, which converts a decision tree induced from regular method into a fuzzy decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy.
Proceedings ArticleDOI

Discovering user interests from Web browsing behavior: an application to Internet news services

TL;DR: This paper presents an approach that analyzes the browsing content and time to determine user interests and shows that the proposed system outperforms the traditional headline news compiled by the news editor in both objective performance indices and customer satisfaction.
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Critical success factors of decision support systems: An experimental study

TL;DR: A framework, based on Simons three-stage decision process model and Fishbein s intention-behavior model, is proposed to integrate factors and describe the decision process of system adoption and found that quality of the system was the most critical factor.
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Guidelines for Neuroscience Studies in Information Systems Research

TL;DR: Five guidelines for planning and evaluating NeuroIS studies are offered to advance IS research, to apply the standards of neuroscience, to justify the choice of a neuroscience strategy of inquiry, to map IS concepts to bio-data, and to relate the experimental setting to IS-authentic situations.