Behavior Computing

1. Front Matter

   Pages I-XVI

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2. Behavior Modeling

   1. Front Matter

      Pages 1-1

      PDF

   2. Analyzing Behavior of the Influentials Across Social Media

      • Nitin Agarwal, Shamanth Kumar, Huiji Gao, Reza Zafarani, Huan Liu

      Pages 3-19

   3. Modeling and Analysis of Social Activity Process

      • Can Wang, Longbing Cao

      Pages 21-35

   4. Behaviour Representation and Management Making Use of the Narrative Knowledge Representation Language

      • Gian Piero Zarri

      Pages 37-56

   5. Semi-Markovian Representation of User Behavior in Software Packages

      • Prateeti Mohapatra, Howard Michel

      Pages 57-68

3. Behavior Analysis

   1. Front Matter

      Pages 69-69

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   2. P-SERS: Personalized Social Event Recommender System

      • Yun-Hui Hung, Jen-Wei Huang, Ming-Syan Chen

      Pages 71-89

   3. Simultaneously Modeling Reply Networks and Contents to Generate User’s Profiles on Web Forum

      • Zhao Zhang, Weining Qian, Aoying Zhou

      Pages 91-108

   4. Information Searching Behavior Mining Based on Reinforcement Learning Models

      • Liren Gan, Yonghua Cen, Chen Bai

      Pages 109-126

   5. Estimating Conceptual Similarities Using Distributed Representations and Extended Backpropagation

      • Peter Dreisiger, Wei Liu, Cara MacNish

      Pages 127-142

   6. Scoring and Predicting Risk Preferences

      • Gürdal Ertek, Murat Kaya, Cemre Kefeli, Özge Onur, Kerem Uzer

      Pages 143-163

   7. An Introduction to Prognostic Search

      • Nithin Kumar M, Vasudeva Varma

      Pages 165-175

4. Behavior Mining

   1. Front Matter

      Pages 177-177

      PDF

   2. Clustering Clues of Trajectories for Discovering Frequent Movement Behaviors

      • Chih-Chieh Hung, Ling-Yin Wei, Wen-Chih Peng

      Pages 179-196

   3. Linking Behavioral Patterns to Personal Attributes Through Data Re-Mining

      • Gürdal Ertek, Ayhan Demiriz, Fatih Cakmak

      Pages 197-214

   4. Mining Causality from Non-categorical Numerical Data

      • Joaquim Silva, Gabriel Lopes, António Falcão

      Pages 215-227

   5. A Fast Algorithm for Mining High Utility Itemsets

      • Show-Jane Yen, Chia-Ching Chen, Yue-Shi Lee

      Pages 229-240

   6. Individual Movement Behaviour in Secure Physical Environments: Modeling and Detection of Suspicious Activity

      • Robert P. Biuk-Aghai, Yain-Whar Si, Simon Fong, Peng-Fan Yan

      Pages 241-253

   7. A Behavioral Modeling Approach to Prevent Unauthorized Large-Scale Documents Copying from Digital Libraries

      • Evgeny E. Ivashko, Natalia N. Nikitina

      Pages 255-265

'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence. With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications. Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.

• Behavior Impact Analysis
• Behavior Network Analysis
• Behavior Pattern Analysis
• Behavior Relation Analysis
• Behavior Representation

Jair Merlo, Accenture, São Paulo, Brazil

Excerpts from full review posted Oct 12 2012 to Computing Reviews [Review #: CR140595 (1302-0087)]

...In spite of the spectrum of subjects and diversity of authors, the book’s editors have been extremely successful in providing coherent and integrated content...

After a thorough depiction of the theoretical state of the art in behavior modeling, chapter 4 offers a gold mine for practitioners: a semi-Markovian algorithm for user behavior analysis that is very easy to grasp, even for novices, and implemented in ActiveX...

Part 4 continues to explore applications of behavior computing with a plethora of diverse cases that illustrate the importance of this novice discipline.

The book provides valuable knowledge for both academia and industry.

• Advanced Analytics Institute, University of Technology, Ultimo, Sydney, Australia

  Longbing Cao

• Department of Computer Science, University of Illinois, Chicago, USA

  Philip S. Yu

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