Faculty of Information Technology University of Technology, Sydney, Australia

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 • What’s the problem? • Objectives • Related work • Research methodology • Key research work • Significance and contributions • Evaluation • Conclusions & Future work lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 Australian Technology Park: Redfern; FIT,UTS (Diff. Providers: AC3, HK market, CSFB, etc. Diff. Formats: FAV, ODBC, JDBC, OLEDB, etc.) Data& resources Researchers (Data mining, financial researchers, financial analysis, decision support analysis…) Data mining Jiaqi & Li’s work What’s the problem? lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

What’s the problem? • What’s my specific problem? • Key linkage: build a comprehensive and powerful infrastructure • Trading and miningsupports • 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 Miningin Capital Markets • Testbed of both research and applications for the project lbcao@it.uts.edu.au

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 & post-processing • Human system interaction could be similar • System and knowledge management could be unified • Software complexities are similar lbcao@it.uts.edu.au

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 componentsinto 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 lbcao@it.uts.edu.au

Objectives lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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. lbcao@it.uts.edu.au

Related work • Research methods • Objects, components, services and agents • Object-oriented, component-based, service-oriented, agent-oriented lbcao@it.uts.edu.au

Related work lbcao@it.uts.edu.au

Research methodology • Research methodology in my work • Agent Service-Oriented Approach • Agent-oriented methodology + service-oriented architecture • agent service-oriented analysis and design lbcao@it.uts.edu.au

Research methodology • Agent-oriented methodology • MASE Methodology • MESSAGE methodology • TROPOS methodology • Gaia methodology lbcao@it.uts.edu.au

Research methodology lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

Research methodology • Why agent services-oriented approach • large scale open agent-based system • Open • Large scale • Interoperable, enterprise applications-oriented • web-based environment • Service of quality: interactions, flexibility, autonomy, reliability, security lbcao@it.uts.edu.au

Key research work • System functional & nonfunctional requirements • Goal-oriented organizational modeling • System architecture • Agent service ontology and semantic relationships • Agent service-oriented analysis & design lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

Key research work lbcao@it.uts.edu.au

Key research work • Service-oriented architecture • organizational framework & design patterns lbcao@it.uts.edu.au

Key research work • EAI architecture • Administration Center, Algorithms Center, Control Center, Services Center, and User Center lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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))))))) lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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) lbcao@it.uts.edu.au

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. lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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); } lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

Key research work • Agent service transport • representation and transport of messages • agent service message model • transport protocol • specifications for transport service lbcao@it.uts.edu.au

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); } lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

Key research work • Framework & patterns lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

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 lbcao@it.uts.edu.au

Faculty of Information Technology University of Technology, Sydney, Australia