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Behavior Informatics and Analytics: Let Behavior Talk

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Title: Behavior Informatics and Analytics: Let Behavior Talk


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

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.

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

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)
6
  • Market price trend/movement estimation

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.
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
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
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
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
    retainers intention, activity change, usage
    dynamics, and payment profile
  • disclosing reasons and drivers of churners and
    their loyalty change

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
    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

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
  • robots behavior
  • organisms behavior in game systems

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

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.

16
  • behavior instance behavior vector
  • basic properties
  • social and organizational factors
  • vector-based behavior sequences,
  • vector-oriented patterns.

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
  • Subjects goal (g), belief (b), plan (l)
  • Behavior performance
  • Impact (f), status (u)
  • New methods for vector-based behavior pattern
    analysis

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.

19
Research map of BIA
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

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

22
  • Behavior model
  • Behavior interaction
  • Collective behavior
  • Action selection
  • Behavior convergence and divergence
  • Behavior representation
  • Behavioral language
  • Behavior dynamics
  • Behavioral sequencing

23
  • Behavioral impact analysis
  • Behavioral instances that are associated with
    high impact on business processes and/or outcomes
  • modeling of behavioral impact

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

25
  • Behavioral pattern analysis
  • behavioral patterns without the consideration of
    behavioral impact,
  • analyze the relationships between behavior
    sequences and particular types of impact

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

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

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

29
  • Behavioral simulation
  • observe the dynamics,
  • the impact of rules/protocols/patterns,
    behavioral intelligence emergence, and
  • the formation and dynamics of social behavioral
    network

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

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

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

33
BIA general process
34
Theoretical Underpinnings
  • Methodological support,
  • Fundamental technologies, and
  • Supporting techniques and tools

35
Applications
  • Trading Behavior Analysis
  • Customer-Officer Interaction Analysis in Social
    Security Areas
  • Facial behavior analysis
  • Online user behavior analysis

36
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.

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.

38
Facial behavior analysis
Pohsiang Tsai Tom Hintz, Tony Jan, Longbing Cao.
A New Multimodal Biometrics for Personal
Identification, Pattern Recognition Letters (to
appear)
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.

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_at_it.uts.edu.au
  • Homepage www-staff.it.uts.edu.au/lbcao/
  • The Smart Lab datamining.it.uts.edu.au
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