Behavior Informatics and Analytics: Let Behavior Talk

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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
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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
  retainers 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 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
• robots behavior
• organisms 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 properties
• social 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
• Subjects 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 analysis
• Behavioral 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 analysis
• behavioral 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 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

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• Behavioral simulation
• observe 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 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

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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
(1) indicating the direction, probability and
size of an order to be traded, (2) reflecting an
orders dynamics during its lifecycle

• Cao L., Ou, Y. Market microstructure patterns
  powering trading and surveillance agents. Journal
  of Universal Computer Sciences, 14(14)
  2288-2308, 2008.

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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 IT
• University of Technology, Sydney, Australia
• Tel [phone]
• Fax [phone]
• email lbcao_at_it.uts.edu.au
• Homepage www-staff.it.uts.edu.au/lbcao/
• The Smart Lab datamining.it.uts.edu.au