4 December, 2012, Macau SAR, China, joint with WI-IAT 2012

The 2012 International Workshop on Behavior Informatics (BI2012) will be held in conjunction with The 2012 IEEE/WIC/ACM International Conference on Web Intelligence and The 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (WI-IAT 2012).

What is New

Important Dates

  • Workshop: December 4, 2012
  • Camera-Ready Deadline: September 7, 2012 (Final Version Submission)
  • Author Notification: August 24, 2012
  • Paper Submission Deadline (Extended): August 15, 2012 (Submission System)

General Information

Due to the behavior implication in normal transactional data, the requirement of deep and quantitative behavior analysis have outstripped the capability of traditional methods and techniques in behavioral science. It is more imperative than ever to develop new behavioral analytics technologies that can derive an accurate understanding of human behaviors beyond the demographic and historical tracking. This leads to the emergence of inter-disciplinary Behavior Representation, Modeling, Analysis and Management (namely Behavior Informatics).

The main goal of Behavior Informatics 2012 Workshop is to provide an international forum for researchers and industry practitioners to share their ideas, original research results, as well as potential challenges and prospects encountered in Behavior Informatics. The topics of BI2012 workshop papers fall into the categories which will include but are not limited to the following:

  • Behavior modeling: formalizing behaviors, relationships, impact and networks.
  • Impact-oriented behavior mining: behaviors associated with high impacts are of particular importance, while impact-oriented behaviors are often sparse, rare and imbalanced isolated in business and data; identify impact-oriented behavior patterns involves different pattern types and computational challenges.
  • Analysis of behavior social networks: handling challenging issues such as convergence and divergence of behavior, and the evolution and emergence of hidden groups and communities.
  • Extracting discriminative behavior patterns from high-dimensional, high-frequency, high-density, and huge amount of data.
  • Large intra-class variance between behaviors: Due to the highly overlapped nature of behavior data, it is extremely difficult to build a robust behavior model which is tolerant for one behavior category while differentiate amongst other categories.
  • Behavior data processing from transactional space to behavior feature space: Customer demographic and transactional data is generally privacy-oriented, distributed and not organized in terms of behavior but entity relationships. In such transactional entity spaces, behavioral elements are dispersed and hidden within complex business applications with weak or no direct linkages. As a result, current behavior analysis which focuses on exterior features in demographic and service usage data cannot effectively and explicitly scrutinize human behavior patterns and impacts on businesses. To support genuine behavior analysis on behavior interior, a challenging task is to extract and transform transactional behavior-related elements into explicit behavior features.