Download Ontology Services-Based Information Integration in Mining

Agent, Services and Organization Oriented
Analysis and Design
---- Building Open Enterprise Infrastructure
Supporting Trading and Mining
Longbing Cao
Faculty of Information Technology
University of Technology, Sydney, Australia
Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
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What’s the problem?
• How does the problem emerge?
– Project from Capital market CRC
– Industrial requirements for capital markets and
financial services
– Bridge linking both industrial and research
requirements
– Problems having industry value and research value
Industrial requirements-driven & interestingness-driven
research
2017/5/22
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What’s the problem?
Datamining Program
Team Leader: Prof Chengqi Zhang
FIT, UTS: Broadway
F-Trade Infrastructure
(integrating DataSources, trading/mining
algorithms, offers personalized services
in terms of system, data and algorithms)
Infrastructure
Longbing & Jiarui’s work
ITR&D-Enabled Finance
Multi-agent & Data-mining
Internet
Multiple *
Remote
Data Sources
CMCRC: CBD of Sydney
(Industrial requirements;
Users: Anybody, anytime, anywhere,
from KDD & Finance;
Services: System, algorithms, data)
Industry
Brokers, retailors
Applications like Wanli’s work
Researchers
(Data mining, financial researchers,
financial analysis, decision support
analysis…)
Australian Technology Park:
Redfern; FIT,UTS
(Diff. Providers: AC3, HK
market, CSFB, etc.
Diff. Formats: FAV, ODBC,
JDBC, OLEDB, etc.)
Data& resources
2017/5/22
Data mining
Jiaqi & Li’s work
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What’s the problem?
• What are specific problems from financial
markets?
– Evaluating trading and mining strategies from industry
& research
– Accessing real huge capital data crossing markets
– Stock & rules association, selection, and optimization
and integration
– Pattern discovery in stock markets
– Cross market analysis
– Applications as investment decision support
ITR&D-Enabled Finance & Teamwork
2017/5/22
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What’s the problem?
• What’s my specific problem?
– Key linkage: build a comprehensive and powerful
infrastructure
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Trading and mining supports
Online, flexible, automated, enterprise-oriented, open
Plug and play soft components
Personalized and customized in different granularities
Reporting & visualization
looks like a virtual service provider
– Automated Enterprise Infrastructure Supporting
Trading and Mining in Capital Markets
– Testbed of both research and applications for the
project
2017/5/22
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
7
Objectives
• Can trading and mining be supported in one
system?
– Differences between trading and mining
– Mutual features or requirements for trading and
mining support systems
• Algorithms
• Requirements for dataset, data pre-processing & postprocessing
• Human system interaction could be similar
• System and knowledge management could be unified
• Software complexities are similar
2017/5/22
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Objectives
• What is expected for a system supporting both
trading and mining?
– first satisfy the above mutual features
– support integration of heterogeneous and distributed data
sources, and transparent to operational systems
– support multiform of algorithms both from trading and mining,
multiform of data sources, multiform of user types and profiles
– Algorithms, system modules, user information, information and
knowledge resource can be plugged into and removed from the
system locally and remotely
– automatically registration of algorithms or other components into
the system
– Variant user profiles and financial domain concepts can be
supported
– expandable for future finance-oriented research and applications
– privacy of plugged algorithms can be kept
2017/5/22
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Objectives
Trading Services
Mining Services
Back-testing
Data preprocessing
Signal alert
Feature selection
Market replay
Methods selection (like classification, regression, clustering, or others)
Basic charting
Training/testing/deploying process
Draw tools
Evaluation & refinement
Reporting
Knowledge presentation or visualization
Technical analysis/ Fundamental Analysis
Interpreting mined patterns
Stock recommendation*
Prediction or description
Integration of trading strategies*
Deployment in the real world*
E-training/learning & applications*
Method optimization*
Automated execution*
Integration of multiple methods*
Cross markets*
Multiple data sources*
2017/5/22
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Objectives
• What are main research objectives?
– concrete functions can be available in the automated
enterprise infrastructure
– research methodology and methods are required for
building such an infrastructure
– what are key research problems, and what research
values are there? How to solve these problems?
– Supporting both research and development in
academic and industrial projects in CMCRC project
– Research papers and PhD thesis can be generated
Agent service-oriented analysis and design
2017/5/22
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
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Related work
• System classification
– Black Box, White Box, Glass Box, Grey Box
• Similar systems
– Computerized trading systems
• TradeStation, E-Trade, TradeTech
– Data mining
• IM, EM, Clementine, Angoss, WEKA
None of them can do the work we proposed.
2017/5/22
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Related work
• Research methods
– Objects, components, services and agents
– Object-oriented, component-based,
service-oriented, agent-oriented
2017/5/22
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Related work
Object-oriented
Component-based
Agent-oriented
Origin
Semantic net
Semantic net
Symbolic AI & behaviour-based AI
Computational entity
Objects
Components
Agents
State parameters for an entity
unlimited
unlimited
Intentional stance, like belief, desire of an agent
Activity
Passive
Passive
Proactive with self control thread, automated
Computational process
Message passing and method response
Message passing and method response
Message passing and method response
Message types
Unlimited, handle messages by method
vocation
Unlimited, handle messages by method
vocation
Speech act;
Language abstraction elements
Objects, classes, modules
Reuse, design patterns, application framework
Agents, class, modules, design patterns, framework,
organization, roles, society, goal
Modelling abstraction
mechanism
Fine object as an action entity, method
invocation used for describing
interaction. static organizational
modelling, no semantics
More strong abstraction mechanism, e.g.
component, reuse, design patterns,
application frameworks,
Agents as coarse and automated computing entity, social
ability (organization, roles, etc), dynamic
organizational modeling
Analysis and design
abstraction in fine granularity; Object model,
dynamic model, function model;
Abstraction in more coarser granularity;
component library, framework library,
object bus
More coarser abstraction; Role model, interaction model,
agent model, service model, acquaintance model
encapsulation
State and behaviour
State and behaviour, application framework
State and behaviour, behaviour activation
Organizational relationship
Static syntactic inheritance
Static syntactic and structural inheritance
An interactive network with inter- and intra-subsystem
and subsystem elements interaction, multiple
organizational relationships (hierarchy, marketing,
etc)
Interaction
Syntactic interaction, invoking methods or
functions, simple message passing
Syntactic interaction, invoking methods or
functions, message passing
Interaction on knowledge and social levels
System problem solving
Event/behaviour-driven; design-time
decision; no automated problem
solving; predefined execution
Event-driven; design-time decision making
Goal-driven, automated and flexible problem solving;
active decision making at runtime, reasoning
ability
Complexity in problem solving
Generic system, with predefined interactive
relationships
Not strong enough for modelling complex
systems
Building complex distributed systems(data, ability, and
control)
System property
Somehow encapsulation, autonomy,
passivity and interaction
Somehow encapsulation, autonomy, passivity
and interaction
Autonomy, reactivity, sociality, proactiveness; loose
control, bigger freedom, uncertainty and
indeterminism
Evolution (specialization) of OO
Evolution (specialization) of OO and CB
2017/5/22
Inter-relationship
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
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Research methodology
• Research methodology in my work
– Agent Service-Oriented Approach
• Agent-oriented methodology + service-oriented
architecture
– agent service-oriented analysis and design
2017/5/22
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Research methodology
• Agent-oriented methodology
– MASE Methodology
– MESSAGE methodology
– TROPOS methodology
– Gaia methodology
2017/5/22
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Research methodology
Gaia
Metaphor
Organization
Modeling
late requirements
engineering,
modeling of the
environment
Analysis phase
MASE
MESSAGE
TROPOS
Organization
Organization
use-cases
UML, AUML
early and late requirements
engineering
role model, protocols
application goals and subgoals, agent roles,
interaction
Structure, role, topological
relations,
Role, organizational
structure, functional
and nonfunctional
requirements,
structural
dependencies
Design phase
global organizational rules
agent classes, agent
interaction protocols,
system architecture
control entity, workflow
structure
Agent systems
Open agent systems
closed agent systems
Open agent systems
Limitations
early requirements analysis
Open
modeling the organizational
rules, design the
organizational
structure
2017/5/22
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global laws
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Research methodology
• Agent services-oriented approach
– Organization-oriented metaphor: abstraction
– FIPA Abstract Architecture: architectural elements and
their relationships
– Organization-oriented modeling: RA
– Agent-oriented methodology: analysis & design
– Service-Oriented Architecture: architecture
– Java Web Services: architecture & implementation
– Java Agent Services: architecture & implementation
agent service-oriented analysis and design
2017/5/22
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Research methodology
• Why agent services-oriented approach
– large scale open agent-based system
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2017/5/22
Open
Large scale
Interoperable, enterprise applications-oriented
web-based environment
Service of quality: interactions, flexibility, autonomy,
reliability, security
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Content
•
•
•
•
•
•
•
•
What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
22
Key research work
• System functional & nonfunctional
requirements
• Goal-oriented organizational modeling
• System architecture
• Agent service ontology and semantic
relationships
• Agent service-oriented analysis &
design
2017/5/22
[email protected]
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Key research work
• Representation and registry of agent
services
• Agent service directory
• Agent service communication
• Agent service transport
• Mediation of agent services
• Discovery of agent services
2017/5/22
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Key research work
• System functional & nonfunctional
requirements
– Data services support
– Algorithm services support
– System services support
– Trading support
– Mining support
– Quality of service, development objectives or
architectural constraints
2017/5/22
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Key research work
• Goal-oriented organizational modeling
– Visual modeling
• extended i* framework
– Formal modeling
• first-order logic + scenario analysis
– Integrative modeling
• Visual modeling + Formal modeling
2017/5/22
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Key research work
Integrative Modeling: Hybrid representation with
visual modeling and formal specifications
Visual Modeling:
Informal box-and-arrow
notations, minimal
syntax, an ontology, no
semantics
Formal Modeling:
Formal conceptual
model, with assertion
language for specifying
and constraints
Informal box-and-arrow
notations, minimal
syntax, no ontology, no
semantics
Formal box-and-arrow
notations, with an
ontology, syntax, and
semantics
2017/5/22
Integrative Modeling
Formal conceptual
model, with an
assertion language
for specifying rules
and constraints
Global qualities like
flexibility, availability,
security, adaptability,
etc.
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Formal Modeling
Informal Modeling
Nonfunctional
Requirements
Functional
Requirements
Informal box-andarrow notations,
minimal syntax,
with or no
ontology, with or
no semantics
Organizational model
includes actors, goals
and inter-dependencies
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Key research work
2017/5/22
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Key research work
2017/5/22
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Key research work
• Service-oriented architecture
– organizational framework & design patterns
Service
Requesters
2017/5/22
ServiceNet
Bind
h
lis
Pub
Fin
Mat d/
ch
Service
Brokers
(Dispatchers)
Service
Providers
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Key research work
• EAI architecture
– Administration Center, Algorithms Center, Control Center, Services
Center, and User Center
F-TRADE
User Roles
F-TRADE
Applications
Subscriber
Users
Technical
Analyses
University
Coordinators
CMCRC
Users
Public Users
Administrator
Users
Algorithms
Providers
Fundamental
Analyses
Risk
Analyses
Cross Market
Mining
Investment
Analyses
Stock Data
Services
F-TRADE
Function Centers
Administration
Center
Algorithms
Center
Control
Center
Services
Center
User
Center
Web
Server
F-TRADE
Services Support
Control Engine
W
e
b
S
e
r
v
e
r
Organization Framework
Design Patterns
Data Services
Algorithm Services
F-TRADE
Gateway Agents
Data
Resource
Interface&
Operation
Gateways
/Adapters
Algorithm
Mediator
Agent
Metadata
Management
Data Mining
Algorithm Base
Trading Signals
Algorithm Base
Ontology Base
System Services
Algorithm
Interface
Agent
F-TRADE Data
&System Resources
System
Resource
Interface&
Operation
Gateways
/Adapters
Data Mining & Trading Algorithms in Stock Markets
Knowledge Base
Local DataSource
Legacy Systems
Users DataSource
AC3 DataSource
WSDL/UDDI/SOAP &Internet & Intranet & Extranet Infrastructure
2017/5/22
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Key research work
• Agent service ontology and semantic
relationships
– Ontology profiles
• Domain ontology
• Task-method ontology
• Ontological commitment
– Ontological engineering
• Agent service ontology, Ontology specifications,
semantic relationship, Ontology transformation,
Naming of agent services, Representation of agent
services
2017/5/22
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Key research work
Financial Organization
Bank
Savings And Loans
...
Exchange
Credit Union
Exchange Type
Price
Futures
Index
Option
Stock
OTC
Exchange Vendor Exchange Exchange Exchange
StockMarket
Auction Market
...
OpenPrice ClosePrice BidPrice AskPrice TradePrice
Dealer Market
Fixed
Income
Stock
su
bc
pa
ta
la
rtnc
ss
of
e-o
of
f
in
s
2017/5/22
Money
Market
Futures
FinancialOrder
Limit Order
LimitPrice
...
Instrument Type
Foreign Index
Exchange
...
Market Order
Options
Price
Enter
Dealer
FinancialOrder
...
Stop Order
MarketOrder LimitOrder
OrderOperation
Amend
StopPrice StrikePrice StockPrice
Trade
Delete
Date
Time
StopOrder
Cantr
Volume
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Key research work
;; definition of LimitOrder
(subclass LimitOrder FinancialOrder)
(documentation LimitOrder "An order to a &%Broker to buy a specified quantity
of a &%Security at or below a specified price, or to sell it at or above a
specified &%limitPrice.")
;; definition of bidPrice
(instance bidPrice TernaryPredicate)
(domain bidPrice 1 Object)
(domain bidPrice 2 CurrencyMeasure)
(domain bidPrice 3 Agent)
(documentation bidPrice "(bidPrice ?Obj ?Money ?Agent) means that ?Agent offers to buy
?Obj for the amount of ?Money.")
(=>
(bidPrice ?Obj ?Money ?Agent)
(exists (?Offering)
(and
(instance ?Offering Offering)
(patient ?Offering (exists (?Buying)
(and
(instance ?Buying Buying)
(agent ?Buying ?Agent)
(patient ?Buying ?Obj)
(transactionAmount ?Buying ?Money)))))))
2017/5/22
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Key research work
• Agent service-oriented analysis &
design
– Role Model, Interaction Model, Environment
Model, Organizational Rules, Organizational
Structure, Agent Model, Service Model, and
Agent Service-oriented Architecture
2017/5/22
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Key research work
Goal RegisterAlgo
InformalDef When an algorithm component has been coded and the algorithm isn’t available from
the system at the moment, this algorithm component can be registered into the
system by calling plug-in interfaces, filling in algorithm registration ontologies, and
upload the algorithm module.
FormalDef
Actor Provider
Mode achieve
Attribute constant ca: CodeAlgo
Attribute constant algo: Algorithm
registered: boolean
Creation condition
● Fulfilled(ca)  ¬ Existed(algo)
Invariant ca.actor = actor
Fulfillment condition
 ac: AlgorithmComponent (ac.algo = algo 
 t1  cpi: CallPluginInterfaces (cpi.actor = actor  Fulfilled(cpi)  pi.Called) 
 t2(  faro: FillinAlgoRegisterOntologies (faro.depender = actor  Fulfilled(faro) 
aro.Filled)
2017/5/22
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Key research work
CodeAlgo (ca1)
Created
Fulfilled
AlgorithmComponent
(ac1)
CallPluginInterfaces
(api1)
Created
Fulfilled
Created
FillinAlgoRegisterOntologies
(faro1)
Fulfilled
Created
UploadAlgoComponent
(uac1)
Fulfilled
Created
True
False
AlgorithmComponent
(ac1).registered
t0
2017/5/22
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t1
t2
t3
t4
t5
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Key research work
Role Schema: PLUGINPERSON
Description:
This preliminary role involves applying registering a nonexistent algorithm, typing
in attribute items of the algorithm, and submitting plug in request to F-TRADE.
Protocols and Activities:
ReadAlgorithm, ApplyRegisteration, FillinAttributeItems,
SubmitAlgoPluginRequest
Permissions:
reads
Algorithms
// an algorithm will be registered
changes AlgoApplicationForms // algorithm registration application form
changes AttributeItems
// all attribute items of an algorithm
Responsibilities
Liveness:
PLUGINPERSON = (ReadAlgorithm).(ApplyRegisteration).
(FillinAttributeItems)+.(SubmitAlgoPluginRequest)
Safety:
The algorithm agent has been programmed by implementing AlgoInterface agent
and ResourceInterface agent, and is available for plug in.
This algorithm hasn’t been plugged into the algorithm base.
2017/5/22
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38
Key research work
• Representation and registry of agent
services
– Namespace and service root
– specifications for agents and services
registration
– representation and registration management
• micro-level + macro-level
2017/5/22
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Key research work
AgentService
RegisterAlgorithm(algoname;inputlist;inputconstraint;outputlist;outputconstraint;)
Description:
This agent service involves accepting registration application submitted by role
PluginPerson, checking validity of attribute items, creating name and directory of the
algorithm, and generating universal agent identifier and unique algorithm id.
Role: PluginPerson
Pre-conditions:
-A request of registering an algorithm has been activated by protocol
SubmitAlgoPluginRequest
-A knowledge base storing rules for agent and service naming and directory
Type: algorithm.[datamining/tradingsignal]
Location: algo.[algorithmname]
Inputs: inputlist
InputConstraints: inputconstraint[;]
Outputs: outputlist
OutputConstraints: outputconstraint[;]
Activities: Register the algorithm
Permissions:
-Read supplied knowledge base storing algorithm agent ontologies
-Read supplied algorithm base storing algorithm information
Post-conditions:
-Generate unique agent identifier, naming, and locator for the algorithm agent
-Generate unique algorithm id
Exceptions:
-Cannot find target algorithm
-There are invalid format existing in the input attributes
2017/5/22
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40
Key research work
• Agent service directory
– agent directory service
• agents directory entries
• Discovery of agent directory-entries
– service directory service
• service directory entries
• Discovery of service directory-entries
– specification of directory service
– position of agent service directory
2017/5/22
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Key research work
public interface AgentDirectory extends Directory{
AgentDescription[] getAgentDescription();
Vector getDirectoryEntry();
void register(AgentDescription ad) throws DirectoryFailure;
void update(AgentDescription ad) throws DirectoryFailure;
void delete(AgentDescription ad) throws DirectoryFailure;
void execute(AgentDescription ad) throws DirectoryFailure;
AgentDescription[] search(AgentDescription ad) throws
DirectoryFailure;
void setDirectoryEntry(Vector de);
}
2017/5/22
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42
Key research work
• Agent service communication
– communication model, communicative act,
and communication control in agent and
service communication
– Message-based communication model, agent
service message model
2017/5/22
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43
Key research work
• Agent service transport
– representation and transport of messages
– agent service message model
– transport protocol
– specifications for transport service
2017/5/22
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44
Key research work
public interface AgentTransport extends Transport{
AgentLocator getSender();
AgentLocator getReceiver();
String getTransportType();
Locator getTransportAddress();
Message getTransportMessage();
void setSender(AgentLocator sender);
void setReceiver(AgentLocator receiver);
void setTransportType(String ttype);
void setTransportAddress(Locator taddress);
void setTransportMessage(Message tmessage);
}
2017/5/22
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Key research work
• Mediation of agent services
– mediation and management of agent services
• local meditation
• global mediation
• multi-tier mediation strategies
– mediation protocols
– mediation logic
2017/5/22
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46
Key research work
• Framework & patterns
2017/5/22
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47
Key research work
• Discovery of agent services
– search for an ontology
– query an agent or service
• agent/service directory service
– search for a message
• search for original message or encoded message
2017/5/22
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48
Key research work
Component OntologySearchService
Description:
This component deals with the search of a corresponding ontology concept in target ontology space
according to a user defined key word or ontology term from source concept space in user profile.
Pre-conditions:
-Ontology spaces enclosing the possible target ontology concepts must be prepared
-A knowledge base storing all existing matching rules
-A knowledge base storing transformation rules
Services:
-S1: UserProfileTransformer
//other properties are omitted for limited space
-S2: UserProfileMatcher
//other properties are omitted for limited space
- S3: AutomaticOntologyMatcher
-Actor: OntologySearchService
-Role: Based on ontology concepts in ontology space of user profile, look for corresponding ontology
concepts in target ontology space
-Pre-conditions:
-Ontology concepts in ontology space of user profile found by service
UserProfileMatcher
-Activity: search
-Permissions:
-Read supplied knowledge base storing transformation rules
-Read supplied knowledge base storing matching rules from ontology concepts in user profile to
concepts in target ontology space
-Post-conditions:
-Find existing matching records, or
-Find and output ontology concepts of target ontology space
-Store new query matching rule into knowledge base if available
-Similar value: simValue = 1
-Exception: Cannot find relevant ontology concepts in target ontology space
-S4: ManualOntologyMatcher
//other properties are omitted for limited space
Post-conditions:
-Output ontology concepts into target ontology space
Exceptions:
-No existing target ontology space
-No knowledge base storing transformation rules
-No knowledge base storing matching rules
2017/5/22
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
50
Significance and contributions
• Research value
– investigation of agent services-oriented analysis and
design for building open enterprise infrastructure
– some interesting research work
• agent services-based modelling, integrative modeling,
domain-specific ontologies, ontological engineering
• representation, directory, transport, transformation, mediation
and discovery of agent services
– research testbed and platform
• multi-agent, high frequency data mining, cross market mining,
stock stream data mining
2017/5/22
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51
Significance and contributions
• Application value
– Industrial requirements from CMCRC
– A prototype supporting trading and mining
• Development from researches
– financial, data mining, intelligent systems, multi-agent…
• Benefit users
– brokers, retailers, data applicants, services applicants…
• Applications testbed and platform
– data mining, stock stream data processing, technical
analysis, fundamental analysis, risk analysis, investment
risk, market replay, signal alerts, cross market mining…
2017/5/22
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
2017/5/22
[email protected]
53
Evaluation
• Functional & nonfunctional evaluation
– As an agent-based system
• Distributed and Interoperable, Flexible, Open and Dynamic,
Automated, User-friendly, Adaptive, Privacy-keeping
– As a trading/mining support system
• Supporting plug in and online registration of data sources,
system modules, and algorithms
• Providing data gateway for supporting data linkage and cross
markets
• Supporting user profiles-oriented and problem domainoriented interaction
• Supporting online system customization and reconstruction
• Supporting comprehensive add-on applications from capital
markets
2017/5/22
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54
Evaluation
• Empirical evaluation
– system comparison
– practical testing in the real world
– computational performance
– customer feedbacks
2017/5/22
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55
Function Items
TradeStation
F-TRADE
Trading supports
Back-testing
Yes
Yes
Indicators
Yes
Yes, but not enough
Basic charting
Yes
Yes, but not strong
Draw tools
Yes
Can support
Reporting
Yes
Yes, but not strong
Signal alert
Yes
Can support
Automated execution
Yes
No
Market replay
No
Can support
Stock recommender
No
Can support
E-training & learning
No
Can support
Technical analysis
Yes
Yes
Fundamental Analysis
No
Can support
Data formats
Some commercial stock data
JDBC, ODBC, FAV
Intraday
Yes
Yes
Inter-day
Yes
Yes
Real time
Yes
No
Downloadable
No
Yes
Cross markets
N/A
Yes
Web-based
Yes
Yes
Downloadable pack
Yes
N/A
Formula disclosed
No
Can support
Rule customizing
Yes, using EasyLanguage
N/A
Rule optimization
Yes
Yes
Rule integration
No
Yes
Inner language
Yes, EasyLanguage
N/A
Data plug in
No
Yes
Module plug in
No
Yes
Algorithm plug in
No
Yes
Interface creating
Yes
Yes, automated
Data gateway
No
Yes, data link and access
System reconstructing
No
Yes
Multi-level outputs
N/A
Yes
Visual output
Yes
Yes, not strong
Data supports
System supports
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System logging
Yes
Privacy keeping
N/A
[email protected]
Yes, Algorithm, system
Yes
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Content
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What’s the problem?
Objectives
Related work
Research methodology
Key research work
Significance and contributions
Evaluation
Conclusions & Future work
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Conclusions & Future work
• An automated enterprise infrastructure
integrating both stock trading and data
mining
• Agent services-oriented approach as
design paradigm for building open agentbased systems
• Agent services-driven enterprise
infrastructure supporting trading and
mining
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Conclusions & Future work
• Teamwork
• Refinement of agent services-oriented
analysis and design
• System implementation
• Publications
• Thesis preparation
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[email protected]
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List of Publications
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Longbing Cao, Jiarui Ni, Jiaqi Wang, Chengqi Zhang. Agent Services-Driven Plug-in Support in F-TRADE. 17th
Australian Joint Conference on Artificial Intelligence, December 2004, Queensland, Australia.
Longbing Cao, Dan Luo, Chao Luo, Li Liu. Ontology Discovery in Multiple Ontology Domains. 17th Australian Joint
Conference on Artificial Intelligence, December 2004, Queensland, Australia.
Longbing Cao, Chao Luo, Dan Luo, and Chengqi Zhang. Integration of Business Intelligence Based on ThreeLevel Ontology Services. proceedings of the 2004 IEEE/WIC/ACM International Conference on Web
Intelligence(WI'04),IEEE Computer Society Press, 20-24 September, 2004, Beijing, China.
Longbing Cao, Jiaqi Wang, Li Lin, and chengqi zhang. Agent Services-Based Infrastructure for Online Assessment
of Trading Strategies. proceedings of the 2004 IEEE/WIC/ACM International Conference on Intelligent Agent
Technology (IAT'04), IEEE Computer Society Press, 20-24 September, 2004, Beijing, China.
Longbing Cao, Chao Luo, Dan Luo, Li Liu. Ontology Services-Based Information Integration in Mining Telecom
Business Intelligence. Proceeding of PRICAI04, Springer Press, 2004.
Longbing CAO, Dan LUO, Chao LUO, and Chengqi ZHANG, Systematic Engineering in Designing Architecture of
Telecommunications Business Intelligence System, Design and Application of Hybrid Intelligent System
(Proceedings of International Conferencec on Hybrid Intelligent System), Melbourne, Australia, 14-17 Dec 2003.
pp. 1084-1093. (ISBN: 1 58603 3948 [IOS Press]).
Longbing Cao, Chao Luo, Chunsheng Li, Chengqi Zhang, and Ruwei Dai, Open giant intelligent information
systems and its agent-oriented abstraction mechamism, In: Proceedings of the fifteenth International Conference
on Software Engineering and Knoledge Engineering (SEKE 2003), San Francisco, California, USA, July 1-3, 2003.
pp.85-89. (ISBN: 1-891706-12-8)
Li Lin, Longbing Cao, Jiaqi Wang, Chengqi Zhang, The Applications of Genetic Algorithms in Stock Market Data
Mining Optimisation, Proceedings of Fifth International Conference on Data Mining, Text Mining and their Business
Applications, September 15-17, 2004, Malaga, Spain.
Longbing Cao, Chunsheng Li, Chengqi Zhang, and Ruwei Dai, Open Giant Intelligent Information Systems and Its
Agent-Oriented Analysis and Design, Proceedings of The 2003 International Conference on Software Engineering
Research and Practice (SERP'03), Vol.2, pp. 816-822, Las Vegas, Nevada, USA, June 23-26, 2003. CSREA
Press. (ISBN: 1-932415-20-3).
L.B. Cao, R.W. Dai. Agent-Oriented Metasynthetic Engineering for Decision Making, International Journal of
Information Technology and Decision Making, 2(2):197-215, World Scientific Publishing, 2003.
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Thank you for your attention!
Comments & suggestions?
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