Part I Web Service Composition Abdelmounaam Rezgui Department of Computer Science Virginia Tech rezgui@vt.edu

Outline Summary of the Objectives Preliminary Approach Status

Objectives Declarative Language for Service Composition Composers specify what the composite service will do Automatic Generation of Composition Plans Formal Verification of Composite Services Formally measure how close a generated composite service is to the specified one

Preliminary Approach Specification Matchmaking Enables high level descriptions of the desired composition Task: Develop CSSL (Composite Service Specification Language) Matchmaking Generates composition plans CP = list of component WSs and their interactions

Preliminary Approach (cont’d) Selection Define QoC parameters (e.g., time, cost, relevance) Select a plan amongst several composition plans Generation Generate a detailed description of a composite service given a selected plan: List of outsourced services Mapping between composite service and component service operations Mapping between messages and parameters Flow of control and data between component services

Declarative Composition of Web Services

Specification Phase - CSSL Language - CSSL: Composite Service Specification Language <service name=“car broker” category=“brokerage”/> <protocols name=“SOAP”/> <message name=“offer”> <parameter name=“make” type=“string”/> <parameter name=“model” type=“string”/> <parameter name=“year” type=“gYear”/> <parameter name=“mileage” type=“integer”/> <parameter name=“price” type=“float”/> </message>…… <operation name=“receiveSpecialOffers” type=“price sales catalogue” signature=“one way” category=“automobile dealer”> <input name=“offer”/> ……

Compatibility Model for Composite Services Signature Web Service One-way Notification Solicit-response Request-response Operations Component or Composite Type RosettaNet PIP e.g., price request Protocols Category e.g., SOAP, HTTP NAICS taxonomy Parameters Data Types XML Schema Category Input e.g., integer NAICS taxonomy Output e.g., insurance Messages

Syntactic Compatibility Signature Compatibility (operations) Transport Protocol Compatibility (services) One-way Notification WS 1 WS 2 Solicit-response Request-response WS1 WS2 Transport Protocols Transport Protocols

Semantic Compatibility Operation Semantics Compatibility (operations) Type (WS1.op1) = Type (WS2.op2) Category (WS1.op1) = Category (WS2.op2) Message Compatibility (operations) WS1.op1.input data type compatible WS2.op2.output

Status Scanner for WSDL files: Implemented and tested Syntactic Composability Module Checks to which extent any two operations of any two Web services are composable

Part II Reputation Management Abdelmounaam Rezgui Department of Computer Science Virginia Tech rezgui@vt.edu

Outline Supporting Privacy Preservation through Trust Approach Research tasks

Trust on the Service Web Interactions Involve Quality expectations Exchange of sensitive information Risks Require Trust

Alternatives for Trust Establishment

Proposed Approach Reputation Management Definition: Approach A service’s reputation within a community of consumers is a perception shared by some or all of the members of that community about that service Approach Consumers rate Web services Consumers collaborate to assess services’ reputation Trust in services derives from their reputation Reputation-based service selection and composition

Research Tasks Automatic Rating of Web Services Models and Techniques for Reputation Management Models for Rating Collection Reputation Assessment History-aware Reputation Assessment Prevention and Detection of Reputation Tampering Trust-based Selection and Composition

An Approach for Automatic Service Rating Rating Scheme: set of rating rules used to automatically rate services Generic E.g., The service exchanges username/password with its consumers only through encrypted communication Domain-specific E.g., The pickup location is within 5 miles from the customer’s address Specification and Interpretation of Rating Rules

Models and Metrics for Reputation Management Centralized, Clique, Arbitrary P2P, Credibility-based P2P System Metrics Cohesion degree Penetrability Sensitivity Scarcity Consumer Metrics Confidence Credibility

Techniques for Reputation Management Reputation Assessment in Regular Environments Scarce Environments Discordant Environments History-aware Reputation Assessment Prevention and Detection of Reputation Tampering

Techniques for Reputation Management Regular Reputation Environments: dense and relatively concordant developing distributed algorithms that efficiently collect ratings from consumers and generate accurate reputation values for services techniques and heuristics to optimize performance and accuracy

Techniques for Reputation Management Scarce Reputation Environments: insufficient density Statistical techniques Accurate and fair initialization assessing the reputation of newly deployed services (i.e., newcomers) for which only a few ratings are available.

Techniques for Reputation Management Discordant Reputation Environments: large numbers of discordant ratings are available for aggregation Rating Cleansing Incremental Pruning Successive iterations produce a relatively concordant environment Outlier detection techniques Clustering-merging Extracts sets of cohesive clusters Merge clusters to produce concordant environments

Techniques for Reputation Management History-aware Reputation Assessment Techniques that consider the “effect of history” Comparison with amnesic techniques Reputation Prediction

Techniques for Reputation Management Prevention and Detection of Reputation Tampering Collusion: occurs when several raters collude with the malicious intention to improve or lower the reputation of one or more services Whitewashing: occurs when a service provider suspends and redeploys a service with a different identity with the malicious intention of clearing its reputation history by acquiring a fresh reputation

Service Selection & Composition Trust Derivation Formally capture the process of trust derivation Study this process using various distribution for: Consumer’s confidence in services’ reputation Consumer’s past experience Effect of past experience on consumers’ judgment Reputation-based Selection Clients are autonomous May use different criteria for selection Reputation-based Composition Static Composition Dynamic Composition

Formal Verification of Composite Service Measures how a generated composite plan matches a specified composite service Algebraic approach for Web services Algebraic Language for the specification of Web services Calculi for formal verification