Behavior Informatics and Analytics: Let Behavior Talk

Behavior Informatics and Analytics: Let Behavior Talk

Longbing Cao

Data Sciences & Knowledge Discovery LabCentre for Quantum Computation and Intelligent Systems

University of Technology, Sydney, Australia

15 December 2008Cao, L: BIA at DDDM2008 Joint with ICDM2008

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The Smart Lab: datamining.it.uts.edu.auBIA

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Outline

Motivation Behavior and Behavioral Model BIA Framework BIA Theoretical Underpinnings BIA Research Issues BIA Applications & Case Studies BIA References


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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.


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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


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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)


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Market price trend/movement estimation


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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.


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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


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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


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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


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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


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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 behavior and 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;


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mapped behaviordirect 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;


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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;


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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.


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behavior instance: behavior vector

basic propertiessocial and organizational factors

vector-based behavior sequences,

vector-oriented patterns.


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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


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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.


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Research map of BIA


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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


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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


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Behavior model Behavior interaction Collective behavior Action selection Behavior convergence and divergence Behavior representation Behavioral language Behavior dynamics Behavioral sequencing


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Behavioral impact analysisBehavioral instances that are

associated with high impact on business processes and/or outcomes

modeling of behavioral impact


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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


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Behavioral pattern analysisbehavioral patterns without the

consideration of behavioral impact, analyze the relationships between

behavior sequences and particular types of impact


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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


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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


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Behavioral networkintrinsic 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


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Behavioral simulationobserve the dynamics,the impact of rules/protocols/patterns,

behavioral intelligence emergence, and

the formation and dynamics of social behavioral network


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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


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Behavioral presentationpresentation means and tools

• describe the motivation and the interest of stakeholders on the particular behavioral data

• Traditional behavior pattern presentation• visual behavioral presentation


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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


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BIA general process


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Theoretical Underpinnings

Methodological support, Fundamental technologies, and Supporting techniques and tools


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Applications

Trading Behavior Analysis Customer-Officer Interaction Analysis

in Social Security Areas Facial behavior analysis Online user behavior analysis …


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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


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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.


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Facial behavior analysis

Pohsiang Tsai; Tom Hintz, Tony Jan, Longbing Cao. A New Multimodal Biometrics for Personal Identification, Pattern Recognition Letters (to appear)


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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.


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Thank you!

Longbing CAO

Faculty of Engineering and ITUniversity of Technology, Sydney, Australia

Tel: 61-2-9514 4477 Fax: 61-2-9514 1807email: [email protected]: www-staff.it.uts.edu.au/~lbcao/The Smart Lab: datamining.it.uts.edu.au

Behavior Informatics and Analytics: Let Behavior Talk

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