The 3rd International Workshop on Intelligent Data Analysis and Management [electronic resource] / edited by Lorna Uden, Leon S.L. Wang, Tzung-Pei Hong, Hsin-Chang Yang, I-Hsien Ting.

An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies -- Memory-Aware Mining of Indirect Associations over Data Streams -- Graph-based batch mode active learning -- One Pass Outlier Detection for Streaming Categorical Data -- Measuring of QoE for Cloud Applications -- Mining Weighted Partial Periodic Patterns -- Edge Selection for Degree Anonymization on K Shortest Paths -- K-Neighborhood Shortest Path Privacy in the Cloud -- The Framework of Information Processing Network for Supply Chain Innovation in Big Data Era -- Website Navigation Recommendation Based on Reinforcement Learning Technique -- An Approach for Hate Groups Detection in Facebook -- Toward Crowdsourcing Data Mining -- Wireless Security Analysis Using War Drive Investigation in Kaohsiung Areas -- Guanxi buying in the social media environment -- Introspection of unauthorized sharing on social networking sites.

These papers on Intelligent Data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.