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Behavior Informatics and Analytics: Let Behavior Talk

This article explores the field of Behavior Informatics and Analytics (BIA), focusing on its theoretical underpinnings, research issues, applications, and case studies. It highlights the importance of behavior analysis in various domains such as business intelligence, customer relationship management, social computing, intrusion detection, and fraud detection.

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Behavior Informatics and Analytics: Let Behavior Talk

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  1. Behavior Informatics and Analytics: Let Behavior Talk Longbing Cao Data Sciences & Knowledge Discovery Lab Centre for Quantum Computation and Intelligent Systems University of Technology, Sydney, Australia

  2. Outline • Motivation • Behavior and Behavioral Model • BIA Framework • BIA Theoretical Underpinnings • BIA Research Issues • BIA Applications & Case Studies • BIA References Cao, L: BIA at DDDM2008 Joint with ICDM2008

  3. Motivation • Behavior is an important analysis object in • Business intelligence • Customer relationship management • Social computing • Intrusion detection • Fraud detection • Event analysis • Market strategy design • Group decision-making, etc. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  4. Examples • Customer behavior analysis • Consumer behavior and market strategy • Web usage and user preference analysis • Exceptional behavior analysis of terrorist and criminals • Trading pattern analysis of investors in capital markets Cao, L: BIA at DDDM2008 Joint with ICDM2008

  5. Traditional analysis on behavior • Behavior-oriented analysis was usually conducted on customer demographic and transactional data directly • Telecom churn analysis, customer demographic data and service usage data are analyzed to classify customers into loyal and non-loyal groups based on the dynamics of usage change • outlier mining of trading behavior, price movement is usually focused to detect abnormal behavior so-called behavior-oriented analysis is actually not on customer behavior-oriented elements, rather on straightforward customer demographic data and business usage related appearance data (transactions) Cao, L: BIA at DDDM2008 Joint with ICDM2008

  6. Market price trend/movement estimation Cao, L: BIA at DDDM2008 Joint with ICDM2008

  7. Problems with traditional behavior analysis • customer demographic and transactional data is not organized in terms of behavior but entity relationships • human behavior is implicit in normal transactional data: behavior implication • cannot support in-depth analysis on behavior interior: behavior exterior • Cannot scrutinize behavioral intention and impact on business appearance and problems Such behavior implication indicates the limitation or even ineffectiveness of supporting behavior-oriented analysis on transactional data directly. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  8. behavior can make difference • behavior plays the role as internal driving forces or causes for business appearance and problems • complement traditional pattern analysis solely relying on demographic and transactional data • Disclose extra information and relationship between behavior and target business problem-solving A multiple-dimensional viewpoint and solution may exist that can uncover problem-solving evidence from not only demographic and transactional but behavioral (including intentional, social and impact aspects) perspectives Cao, L: BIA at DDDM2008 Joint with ICDM2008

  9. support genuine behavior analysis • make behavior ‘explicit’ by squeezing out behavior elements hidden in transactional data • a conversion from transactional space to behavior feature space is necessary • behavior data: • behavior modeling and mapping • organized in terms of behavior, behavior relationship and impact Explicitly and more effectively analyze behavior patterns and behavior impacts than on transactional data Cao, L: BIA at DDDM2008 Joint with ICDM2008

  10. main goals and tasks of behavior informatics and analytics (BIA) • behavioral data construction • behavior modeling and representation, • behavior impact modeling, • Behavior pattern analysis, and • behavior presentation BIA is mainly from the perspectives of information technology and data analysis rather than from social behavior aspect Cao, L: BIA at DDDM2008 Joint with ICDM2008

  11. BIA makes difference • Case study: churn analysis of mobile customers • analysis on demographic and service usage data • behavior sequences of a customer • activities happened from his/her registration and activation of a new account into a network • Characteristics of making payments to the date leaving the network • Know deep knowledge about mobile service retainer’s intention, activity change, usage dynamics, and payment profile • disclosing reasons and drivers of churners and their loyalty change Cao, L: BIA at DDDM2008 Joint with ICDM2008

  12. So, what is behavior • Under the scope of Behavior Informatics and Analytics, behavior refers to those activities that present as actions, operations or events, and activity sequences conducted by human beings under certain context and environment, as well as behavior surroundings. • the informatics and analytics for symbolic behaviorand the analytics of mapped behavior. • symbolic behavior • Those social activities recorded into computer systems, which present as symbols representing human interaction and operation with a particular object or object system; • place an order • game user behavior • intelligent agent behavior; Cao, L: BIA at DDDM2008 Joint with ICDM2008

  13. mapped behavior • direct or indirect mapping of physical behavior in a virtual world. • Those physical activities recorded by sensors into computer systems, • human activities captured by video surveillance systems; • robot’s behavior • organism’s behavior in game systems; Cao, L: BIA at DDDM2008 Joint with ICDM2008

  14. An Abstract Behavioral Model • Behavior attributes and properties: • Subject (s): The entity (or entities) that issues the activity or activity sequence; • Object (o): The entity (or entities) on which a behavior is imposed on; • Context (e): The environment • Goal (g): Goal represents the objectives • Belief (b): Belief represents the informational state and knowledge • Action (a): Action represents what the behavior subject has chosen to do or operate; • Plan (l): Plans are sequences of actions • Impact (f): The results led by the execution of a behavior on its object or context; • Constraint (c): Constraint represents what conditions are taken on the behavior; constraints are instantiated into specific factors in a domain; • Time (t): When the behavior occurs; • Place (w): Where the behavior happens; • Status (u): The stage where a behavior is currently located; • Associate (m): Other behavior instances or sequences of actions that are associated with the target one; Cao, L: BIA at DDDM2008 Joint with ICDM2008

  15. An abstract behavior model • Demographics of behavioral subjects and objects • Associates of a behavior may form into certain behavior sequences or network; • Social behavioral network consists of sequences of behaviors that are organized in terms of certain social relationships or norms. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  16. behavior instance: behavior vector • basic properties • social and organizational factors • vector-based behavior sequences, • vector-oriented patterns. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  17. vector-oriented behavior pattern analysis is much more comprehensive • Behavior performer: • Subject (s), action (a), time (t), place (w) • Social information: • Object (o), context (e), constraints (c), associations (m) • Intentional information: • Subject’s: goal (g), belief (b), plan (l) • Behavior performance: • Impact (f), status (u) • New methods for vector-based behavior pattern analysis Cao, L: BIA at DDDM2008 Joint with ICDM2008

  18. The concept of BIA • BIA aims to develop methodologies, techniques and practical tools for • representing, modeling, analyzing, understanding and/or • utilizing symbolic and/or mapped behavior, • behavioral interaction and network, behavioral patterns, • behavioral impacts, • the formation of behavior-oriented groups and collective intelligence, and • behavioral intelligence emergence. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  19. Research map of BIA Cao, L: BIA at DDDM2008 Joint with ICDM2008

  20. BIA research issues • Behavioral data • Behavioral elements hidden or dispersed in transactional data • behavioral feature space • Behavioral data modeling • Behavioral feature space • Mapping from transactional to behavioral data • Behavioral data processing • Behavioral data transformation Cao, L: BIA at DDDM2008 Joint with ICDM2008

  21. Behavioral representation (behavioral modeling) • describing behavioral elements and the relationships amongst the elements • presentation and construction of behavioral sequences • unified mechanism for describing and presenting behavioral elements, behavioral impact and patterns Cao, L: BIA at DDDM2008 Joint with ICDM2008

  22. Behavior model • Behavior interaction • Collective behavior • Action selection • Behavior convergence and divergence • Behavior representation • Behavioral language • Behavior dynamics • Behavioral sequencing Cao, L: BIA at DDDM2008 Joint with ICDM2008

  23. Behavioral impact analysis • Behavioral instances that are associated with high impact on business processes and/or outcomes • modeling of behavioral impact Cao, L: BIA at DDDM2008 Joint with ICDM2008

  24. Behavior impact analysis • Behavioral measurement • Organizational/social impact analysis • Risk, cost and trust analysis • Scenario analysis • Cause-effect analysis • Exception/outlier analysis and use • Impact transfer patterns • Opportunity analysis and use • Detection, prediction, intervention and prevention Cao, L: BIA at DDDM2008 Joint with ICDM2008

  25. Behavioral pattern analysis • behavioral patterns without the consideration of behavioral impact, • analyze the relationships between behavior sequences and particular types of impact Cao, L: BIA at DDDM2008 Joint with ICDM2008

  26. Emergent behavioral structures • Behavior semantic relationship • Behavior stream mining • Dynamic behavior pattern analysis • Dynamic behavior impact analysis • Visual behavior pattern analysis • Detection, prediction and prevention • Customer behavior analysis • Behavior tracking • Demographic-behavioral combined pattern analysis • Cross-source behavior analysis • Correlation analysis • Social networking behavior • Linkage analysis • Evolution and emergence • Behavior clustering • Behavior network analysis • Behavior self-organization • Exceptions and outlier mining Cao, L: BIA at DDDM2008 Joint with ICDM2008

  27. Behavioral intelligence emergence • behavioral occurrences, evolution and life cycles • impact of particular behavioral rules and patterns on behavioral evolution and intelligence emergence • define and model behavioral rules, protocols and relationships, and • their impact on behavioral evolution and intelligence emergence Cao, L: BIA at DDDM2008 Joint with ICDM2008

  28. Behavioral network • intrinsic mechanisms inside a network • behavioral rules, interaction protocols, convergence and divergence of associated behavioral itemsets • effects such as network topological structures, linkage relationships, and impact dynamics • Community formation, pattern, dynamics and evolution Cao, L: BIA at DDDM2008 Joint with ICDM2008

  29. Behavioral simulation • observe the dynamics, • the impact of rules/protocols/patterns, behavioral intelligence emergence, and • the formation and dynamics of social behavioral network Cao, L: BIA at DDDM2008 Joint with ICDM2008

  30. Large-scale behavior network • Behavior convergence and divergence • Behavior learning and adaptation • Group behavior formation and evolution • Behavior interaction and linkage • Artificial behavior system • Computational behavior system • Multi-agent simulation Cao, L: BIA at DDDM2008 Joint with ICDM2008

  31. Behavioral presentation • presentation means and tools • describe the motivation and the interest of stakeholders on the particular behavioral data • Traditional behavior pattern presentation • visual behavioral presentation Cao, L: BIA at DDDM2008 Joint with ICDM2008

  32. Rule-based behavior presentation • Flow visualization • Sequence visualization • Parallel visualization • Dynamic group formation • Dynamic behavior impact evolution • Visual behavior network • Behavior lifecycle visualization • Temporal-spatial relationship • Dynamic factor tuning, configuration and effect analysis • Behavior pattern emergence visualization • Distributed, linkage and collaborative visualization Cao, L: BIA at DDDM2008 Joint with ICDM2008

  33. BIA general process Cao, L: BIA at DDDM2008 Joint with ICDM2008

  34. Theoretical Underpinnings • Methodological support, • Fundamental technologies, and • Supporting techniques and tools Cao, L: BIA at DDDM2008 Joint with ICDM2008

  35. Applications • Trading Behavior Analysis • Customer-Officer Interaction Analysis in Social Security Areas • Facial behavior analysis • Online user behavior analysis • … Cao, L: BIA at DDDM2008 Joint with ICDM2008

  36. Trading Behavior Analysis Cao L., Ou, Y. Market microstructure patterns powering trading and surveillance agents. Journal of Universal Computer Sciences, 14(14): 2288-2308, 2008. (1) indicating the direction, probability and size of an order to be traded, (2) reflecting an order’s dynamics during its lifecycle Cao, L: BIA at DDDM2008 Joint with ICDM2008

  37. Customer-Officer Interaction Analysis in Social Security Areas • Cao, L., Zhao, Y., Zhang, C. (2008), Mining Impact-Targeted Activity Patterns in Imbalanced Data, IEEE Trans. Knowledge and Data Engineering, IEEE, , Vol. 20, No. 8, pp. 1053-1066, 2008. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  38. Facial behavior analysis Pohsiang Tsai; Tom Hintz, Tony Jan, Longbing Cao. A New Multimodal Biometrics for Personal Identification, Pattern Recognition Letters (to appear) Cao, L: BIA at DDDM2008 Joint with ICDM2008

  39. References • Cao L. From Behavior to Solutions: the Behavior Informatics and Analytics Approach, Information Sciences, to appear. • Cao, L., Zhao, Y., Zhang, C. Mining impact-targeted activity patterns in imbalanced data, IEEE Trans. on Knowledge and Data Engineering, Vol. 20, No. 8, pp. 1053-1066, 2008 • Cao, L., Zhao, Y., Zhang, C., Zhang, H. Activity mining: from activities to actions, International Journal of Information Technology & Decision Making, 7(2), pp. 259 - 273, 2008 • Cao L., Ou, Y. Market microstructure patterns powering trading and surveillance agents. Journal of Universal Computer Sciences, 2008. Cao, L: BIA at DDDM2008 Joint with ICDM2008

  40. Thank you! Longbing CAO Faculty of Engineering and IT University of Technology, Sydney, Australia Tel: 61-2-9514 4477 Fax: 61-2-9514 1807 email: lbcao@it.uts.edu.au Homepage: www-staff.it.uts.edu.au/~lbcao/ The Smart Lab: datamining.it.uts.edu.au Cao, L: BIA at DDDM2008 Joint with ICDM2008

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