Configuration and Adaptation of Semantic Web Processes Kunal Verma Ph.D. Thesis Defense (6/13/2006)...

Configuration and Adaptation of Semantic Web

ProcessesKunal Verma

Ph.D. Thesis Defense (6/13/2006)LSDIS Lab, Dept of Computer Science, University of Georgia

Advisors: John A. Miller and Amit P. ShethAdvisory Committee: Budak Arpinar, Robert Bostrom, Ling Liu

Outline

• Motivation

• Dynamic Process Configuration

• Process Adaptation

• Empirical Evaluation

• Conclusions, Related Work and Future Agenda

Motivation• Evolution of business needs drives IT innovation

• Initial focus on automation led to workflow technology

• In order to facilitate efficient inter-organizational processes distributed computing paradigms were developed– CORBA, JMS, Web Services

• The current and future needs include:– Creating highly adaptive process that react to changing

conditions• Focus on real time events and data – RFID and ubiquitous devices

– Have the ability to quickly collaborate with new partners– Aligning business goals and IT processes

Motivation• Current Tools focus on allowing businesses to have greater

dynamism and agility– Microsoft Dynamics, IBM Websphere Business Integration, SAP

Netweaver• All of these Current focus on dynamic and agility through human

interaction using GUIs• All of them list SOA (WS) as a technology for realization

• The future– Move focus to greater automation

• Capture domain knowledge and declaratively specify criteria for process configuration (Dynamic process configuration)

• Add decision making support to process execution tools for process adaptation (Process Adaptation)

“Each enterprise will measure and aspire to its own unique level of dynamism based on its individual purpose. It is about being nimble and adaptable. A fully integrated business platform can respond faster, and completely, to change. Whether it involves fulfilling a new mandate or embracing a new market opportunity. Some organizations will push the envelope, automating event-triggered responses for highly integrated closed-loop processes, setting the stage for self-optimizing systems.”

Sandra Rogers, White Paper: Business Forces Driving Adoption of Service Oriented Architecture, Sponsored by: SAP AG

Web Services and Semantics• Web services deployment increasing in industry

– Standards based interoperability– Loosely coupled systems– Still based on manual integration

• Adding semantics can take us to the next level of automation– Use ontologies for shared understanding– Move towards semi-automated integration

Configuration and Adaptation – Roadmap

Semantic Web Enablers

Ontologies: Specification of

conceptualization. Mode of capturing concepts and their relationships, etc.

OWL: Ontology Web Language

SWRL: Semantic Web Rule Language

Annotation/Representation

WSDL-S/SAWSDL (02-06)

DiscoveryMapping WSDL-S into UDDI (02-04)

Constraint AnalysisSemantic Policies (04-06) and

Agreements (05-06)

Dynamic ExecutionService Manager

basedRuntime Binding (03-06)

WSDL

UDDI

WS-Policy, WS-Agreement

BPEL Engines (BPWS4J,

ActiveBPEL)

BPEL CompositionCreating abstract

BPEL Process (03-06)

Existing WS Standards/

Infrastructure

Semantic Web Services and

Processes

Process Adaptation

Dynamic Process Configuration

Configuration and Adaptation

Receive

Configure: Discover

VP1: getMBQuote

VP2: getRAMQuote

VP1: orderMB VP2:orderRAM

Configure: Analyze

Reply

Receive

Configure: Discover

VP1: getMBQuote

VP2: getRAMQuote


Configure: Analyze

Reply

1. Process Creation: Abstract process with virtual partner services and process constraints

2. Process Configuration: -Service discovery -Constraint analysis

3c. Adaptation: - Event based adaptation - Find a path from error state to goal state

Receive

Configure: Discover

VP1: getMBQuote

VP2: getRAMQuote


Configure: Analyze

Reply

3a. Executable Process: Virtual partners replaced by actual services

3b. Process Execution: Monitoring of processstates during execution

High Level Architecture

METEOR-S MIDDLEWARE

Workflow Engine(IBM BPWS4J)

Web Services

Discovery

Constraint Analysis

Configuration Module

Adaptation Module

MDP

Deployed Web Process

Configuration/Invocation Request Message

Configuration/Invocation Response Message

Eve

nt fr

om s

ervi

ce

Service invocation

Process and

Service Managers

Entities

Process Manager (PM): Responsible for global process configuration

Service Manager (SM): Responsible for interaction of process with service

Configuration Module (CM):Discovery and constraint analysis

Adaptation Module (AM): Process adaptation from exceptions/events

Motivating Scenario

• Consider a simplified supply chain process of a computer manufacturer– Most parts are multiple sourced (overseas and internal

suppliers)• Overseas goods cheaper but greater lead times

– There often exist part compatibility constraints• Choosing a certain motherboard restricts choices of RAMs,

processors– Must respect relationship with preferred suppliers

• Suppliers characterized as preferred or secondary– Sometimes important to maintain production schedule in

the presence of delayed orders

Dynamic Process Configuration

Dynamic Process ConfigurationDynamic configuration Problem

Find optimal partners for the process based on process constraints – cost, supply time, etc.

Conceptual Approach

1. Create framework to capture represent domain knowledge

2. Represent constraints on the domain knowledge

3. Ability to reason on the constraints and configure the process

Dynamic Process ConfigurationResearch Challenges

– Capturing functional and non-functional requirements of the Web process (Abstract process specification)

– Discovering service partners based on functional requirements (Semantic Web service discovery)

– Choosing optimal partners that satisfy non-functional requirements (Constraint Analysis)

K. Verma, R. Akkiraju, R. Goodwin, P. Doshi, J. Lee, On Accommodating Inter Service Dependencies in Web Process Flow, AAAI Spring Symposium on Semantic Web Services, 2004R. Aggarwal, K. Verma, J. A. Miller, Constraint Driven Composition in METEOR-S, SCC 2004.K. Verma, K.Gomadam, J. Miller and A. Sheth, Configuration and Execution of Dynamic Web Processes, LSDIS Lab Technical Report, 2005.

Abstract Process Specification

1. Specify process control flow by using virtual partners

2. Specify Process Constraints

3. Capture Functional Requirements of Services using Semantic Templates

Process Constraints

• Constraints can be specified on a partner, an activity or the process as a whole.

• An objective function can also be specified e.g., minimize cost and supply-time, etc.

• Two types of constraints:– Quantitative (Q) (Time < 5 sec)– Logical (L) (preferredPartner, Security, etc.)

Process Constraints

TrueSatisfyPartner 1PreferredSupplier(P1) (Logical)

TrueSatisfyProcessCompatible (P1, P2) (Logical)

Activity

Process

Process

Scope

Σ Dollars<200000SatisfyCost (Quantitative)

MAX Days< 7SatisfySupplytime (Quantitative)

Σ DollarsMinimizeCost (Quantitative)

AggregationUnitValueGoalFeature

Semantic TemplatesPIP

QueryOrderStatus (3A5)

CancelOrder (3A9)RequestPurchase

Order (3A4)

ReturnProduct (3C1)PurchaseOrderDetails

PurchaseOrderConfirmation

hasInput hasOutput

Part of Rosetta Net Ontology

Data SemanticsFunctional SemanticsNon-Functional Semantics

WSDL-S is used to capture semantic templates

• Semantic Templates capture the functionality of a Web service with the help of ontologies/other domain models

• Find a service that sells RAM in Athens, GA. It must allow the user to return and cancel, if needed

• The template can also have non-functional (QoS) requirements such as response time, security, etc.

Service Level Metadata (SLM)IndustryCategory = NAICS:ElectronicsProductCategory = DUNS:RAMLocation = Athens, GA

Operation 1 Action = Rosetta#RequestPurchaseOrderInput = Rosetta#PurchaseOrderRequestOutput = Rosetta#PurchaseConfirmationPolicy = {Encryption = RSA, ResponseTime < 5 sec}

SEMANTIC TEMPLATE

Operation 2 Action = Rosetta#CancelOrder…………..

…………<xs:element name= "processPurchaseOrderResponse" type="xs:stringwssem:modelReference="POOntology#OrderConfirmation"/></xs:schema></types><interface name="PurchaseOrder"><wssem:category name= “Electronics” taxonomyURI=http://www.naics.com/

taxonomyCode=”443112” />

<operation name=“order” pattern=wsdl:in-outmodelReference = "rosetta:#RequestPurchaseOrder" >

<input messageLabel = ”processPurchaseOrderRequest"element="tns:processPurchaseOrderRequest"/><output messageLabel ="processPurchaseOrderResponse"element="processPurchaseOrderResponse"/>

<!—Precondition and effect are added as extensible elements on an operation><wssem:precondition name="ExistingAcctPrecond"wssem:modelReference="POOntology#AccountExists"><wssem:effect name="ItemReservedEffect"wssem:modelReference="POOntology#ItemReserved"/></operation></interface>

WSDL-S Example

Rama Akkiraju, Joel Farrell, John Miller, Meenakshi Nagarajan, Amit Sheth, and Kunal Verma, Web Service Semantics, WSDL-S W3C Member Submission

K. Sivashanmugam, Kunal Verma, Amit Sheth, John A. Miller, Adding Semantic to Web Service Standards, ICWS 2003.

http://www.naics.com/

Semantic Discovery• Finds actual services matching semantic templates

• Implemented as a layer over UDDI [1]

• Current implementation based on ontological representation of operations, inputs and outputs.

• Returns ranked of services for each semantic template

• Builds upon following previous discovery implementations– Extends matching presented in [2] to consider operations and

service level metadata– Extends the approach presented “WSDL to UDDI Mapping” [3]

to support operation level discovery

[1] K. Verma, K. Sivashanmugam, A. Sheth, A. Patil, S. Oundhakar and John Miller, METEOR-S WSDI: A Scalable Infrastructure of Registries for Semantic Publication and Discovery of Web Services, JITM

[2] M. Paolucci, T. Kawamura, T. Payne and K. Sycara, Semantic Matching of Web Services Capabilities, ISWC 2002.2

[3] Using WSDL in a UDDI Registry, Version 2.0.2 - Technical Note, http://www.oasis-open.org/committees/uddi-spec/doc/tn/uddi-spec-tc-tn-wsdl-v202-20040631.pdf

Semantic Discovery

ILP Solver

SWRL Reasoner

PROCESS CONSTRAINTSQ: Cost <= $600000

Q: SupplyTime < 7 DaysL: Compat (RAM, MB)= True

L: Compat (PROC, MB)= TrueL: preferredSupplier(S1) = True

Min: Cost

ILP SOLVER RESULTS – Service Sets that satisfy all quantitative

constraints in increasing Cost order1. R1, M2, P1 Cost = $4000002. R4, M1, P3 Cost = $4100003. R4, M2,P3 Cost = $441000

SWRL REASONER RESULTS Service sets that satisfy both quantitative and non-quantitative

constraints1. R1, M2,P1 Cost = $4000002. R4, M1,P3 Cost = $410000

(REJECTED SET 3 as R4 not compatible with M2 and P3 not compatible with M2)

CONSTRAINT ANALYSIS MODULE

RAM Candidate Service 1 (R1)

Q: Cost = $45000Q: SupplyTime < 5 Days

.

.RAM Candidate Service 3

(R3)Q: Cost = $40000

Q: SupplyTime < 8 Days


Q: Cost = $41000Q: Supply Time < 8 Days

MB Candidate Service 1 (M1)



Q: Cost = $145000Q: Supply Time < 7 Days.

.

.MB Candidate Service 4

(M4)Q: Cost = $185000

Q: Supply Time <6 Days

Processor Candidate Service 1 (P1)


.

.Processor Candidate Service

3 (P3)Q: Cost = $255000

Q: Supply Time < 8 Days



SEMANTIC TEMPLATES (ST1, ST2 and ST3) from


UDDI Registry with Semantic

Layer

DISCOVERY RESULTS – List of candidate service for each template

Constraint Analysis• Operations Research has been used in industry for business

process optimization

• For process configuration our approach seeks to combine domain knowledge in ontologies with a standard optimization technique

• Multi-paradigm proposed:– Integer Linear Programming for quantitative constraints– Semantic Web Rule Language and OWL for domain constraints

• Discovered Services first given to ILP solver– It returns ranked sets of services

• Then each set is checked for logical constraints using a SWRL reasoner– Sets not satisfying the criteria are rejected

Quantitative Constraint Analysis• Create a binary variable xij for each

candidate service.1 if candidate service j is chosen for activity i

0, otherwise {ij

,x

• Set up constraints for the number of services chosen for each activity. – N(i) is the number of candidate services of activity ‘i’ and M is the number of

activities.

11

1N( i )

i M ijj

( i ) x

Quantitative Constraint Analysis• Set the cost constraint on activity1

• Set the supply time constraint

1 1 7i M j N( i ) ij ij( i ) ( j ) SupplyTime( x ) x

• Set up the objective function

1 1M N( i )

ij iji jMinimize : cos t( x ) x

11 11 200000N( )

j jj cos t( x ) x

Configuration – Quantitative Constraint Analysis

ILP Solver

SWRL Reasoner




Min: Cost









.


(R3)Q: Cost = $40000








.


(M4)Q: Cost = $185000




.


3 (P3)Q: Cost = $255000







Layer


Logical Constraint Analysis

• Use a SWRL reasoner to perform logical constraint analysis

• Domain knowledge is captured as ontologies

• Rules are created with the help of the knowledge in the ontology

• Implemented using IBM’s ABLE and SNOBASE– SNOBASE stores OWL ontologies using ABLE Rule Language

(ARL)– Our implementation is based on SWRL rules written in ARL

K. Verma, R. Akkiraju, R. Goodwin, Semantic Matching of Web Service Policies SDWP 2005 & Filed PatentN. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Based Partner Selection, WWW 2006

Domain Ontology

Supplier Part

MotherBoard

RAM

Supplies

worksWith

partnerStatus

R1

RAM1

Supplies

R5

M1

MB1

Supplies

M2

Supplies

worksWith

R4

M3

RAM2

Supplies

INSTANCES

SCHEMA

RAM Motherboard Processor

Works_withMB

Works_withCPU

Works_withCPU

RDRAM DDR2 RAM

ISAISA

Model: MR16Q162GDB0-CA2Memory_speed: 800 MHzVoltage: 2.5VRequires: dual_channel_motherboardUses: DIMMSStorage:512MB

Model: KVR533D2S4/256Memory_speed: 400 MHzVoltage:1.8 VRequires: dual_channel_motherboardUses: DIMMSStorage:512MB

Model: D875PBZCPU_Type: IntelPentium4FSB: 800 MHzType: dual_channel_motherboardRAM_Modules: DIMMSRAM_Speed:800/533/400

Model: Athlon ADA3800BVBOX Type: Athlon64FX2Clock_speed: 3.80 GhzCore_type: DualAdressing_size: 64 BitCache:1MBFSB: 800 MHz

Model: D865PERLCPU_Type: IntelPentium4FSB: 800 MHzType: dual_channel_motherboardRAM_Modules: RIMMSSupports_RAM_Speed:800/533/400

Model: Pentium 4 672Type: Pentium4Clock_speed: 3.80 GhzCore_Type: SingleAdressing_size: 32 BitCache:2MBFSB: 1200 MHz

Supplier

R1

Supplies

ISA

ISA

R3 R4

M1 M2 M4

P1 P4

Model: MR16R162GDF0-CM8Memory_speed: 800 MHzVoltage: 2.5VRequires: dual_channel_motherboardUses: RIMMSStorage:512MB

Model: A8N-SLICPU_Type: Athlon64FX/Athlon64X2FSB: 1066/800/533MHzType: dual_channel_motherboardRAM_Modules: DIMMSSupports_RAM_Speed:800/533/400

supplies

Part

Works_with

ISA

Domain Ontology – Detailed View

Rules• Supplier 1 should be a preferred supplier.

– “if S1 is a supplier and its supplier status is preferred then the S1 is a preferred supplier”.

Supplier (?S1) and partnerStatus (?S1, “preferred”) => preferredSupplier (?S1)

• Supplier 1 and supplier 2 should be compatible. – if S1 and S2 are suppliers and they supply parts P1 and P2, respectively, and

the parts work with each other, then suppliers S1 and S2 are compatible for parts P1 and P2.

Supplier (?S1) and supplies (?S1, ?P1) and Supplier (?S2) and supplies (?S2, ?P2) and worksWith (?P1, ?P2) => compatible (?S1, ?S2, ?P1, ?P2)

RAM (?P1) and MB (?P2) and worksWithMB (?P1, ?P2) =>worksWith (?P1, ?P2)

Using Rules to resolve Heterogeneities Manufacturer Process Constraint:Availability is greater than 95%

Supplier Policy:Mean Time to Recover equals 5 minutesMean Time between failures equals 15 hours

Rule: Availability = Mean Time Between Failures/(Mean Time Between Failures + Mean Time To Recover)

Availability equals 99.4%.

K. Verma, R. Akkiraju, R. Goodwin, Semantic Matching of Web Service Policies SDWP 2005 & Filed PatentN. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Based Partner Selection, WWW 2006

Configuration – Logical Constraint Analysis

ILP Solver

SWRL Reasoner




Min: Cost









.


(R3)Q: Cost = $40000








.


(M4)Q: Cost = $185000




.


3 (P3)Q: Cost = $255000







Layer


Runtime Configuration Support

SM1

M1

SM2

M2

M3

P1

P2

P3

SM1

SM3

M2

P1

Binding phase

One to Many Binding phase

One to One Binding phase

Analyze process constraints and create

a set of optimal partners

Discover partners and Get quote from all

partners

Process execution with set of optimal partners

Receive

Configure: Discover

VP1: getMBQuote

VP2: getRAMQuote


Configure: Analyze

Reply

Phases

One to Many binding( Information gathering phase): Number of services bound to same service manager. Used for information gathering for constraint analysis

Binding (Constraint Analysis Phase): Constraint Analysis and binding optimal partner to each SM

One to One binding (Execution and adaptation phase): Normal process execution with optimal partner

Process Adaptation

Process Adaptation

• Ability to adapt the processes from failures, unexpected events

• Two kinds of failures– Failures of physical components like services, processes,

network• Can replace services using dynamic configuration

– Logical failures like violation of SLA constraints/Agreements such as Delay in delivery, partial fulfillment of order

• Need additional decision making capabilities

K. Verma, A. Sheth, Autonomic Web Processes, ICSOC 2005K. Verma, P. Doshi, K. Gomadam, A. Sheth, J. Miller, Optimal Adaptation of Web Processes with Coordination Constraints, ICWS 2006.

Process AdaptationAdaptation Problem

Optimally react to events like delays in ordered goods

Conceptual Approach

1. Maintain states of the process – normal states, error states, goal states

2. Capture costs while transitioning from anomalous states to goal state

3. Ability to decide optimal actions on the basis of state

Order

Wait for Delivery

Received

Order received

Delayed

Order delayed

Optimal to change supplier

Optimal to waitOrder

received

K. Verma, A. Sheth, Autonomic Web Processes, ICSOC 2005K. Verma, P. Doshi, K. Gomadam, A. Sheth, J. Miller, Optimal Adaptation of Web Processes with Coordination Constraints, ICWS 2006.

Process Adaptation• Research Challenges

– Creating a model to recover from failures and handle future events– Model must deal with two important factors

• Uncertainty about when a failure occurs• Cost based recovery

• Scenario– After order for MB and RAM are placed, they may get delayed– The manufacturer may have severe costs if assembly is halted. – It must evaluate whether it is cheaper to cancel/return and reorder or

take the penalty of delay– Caveat: possible that reordered goods may be delayed too

• Proposed Solution– Modeling decision making capabilities of Service Managers as Markov

Decision Processes (MDPs)

Modeling Decision Making Process of Service Managers using MDPsEach Service Manager is controlled by a MDPSM-MDP = <S, A, PA, T, C, OC> , where

• S is the set of local states of the service manager.

• A is the set of actions of the service manager. The actions include invoking Web service operations and calling the configuration manager.

• PA : S → A is a function that gives the permissible actions of the service manager from a particular state.

• T : S × A × S → [0, 1] is the local Markovian transition function. The transition function gives the probability of ending in a state j by performing action a in state i.

• C : S × A → R is the function that gives the cost of performing an action from some state of the service manager.

• OC is the optimality criterion. We minimize the expected cost over a finite number of steps, N, also called the horizon.

Policy Computation• The optimal action at each state is represented using a

policy. • In order to compute the policy, a value is associated to

each state. – The value represents long term expected cost of performing

the optimal action from that state and is calculated the following dynamic programming equation.

n na PA( s )

pi ( s ) arg min Q ( s,a )

1

n na PA( s )

n ns'

V ( s ) min Q ( s,a )

Q ( s,a ) C( s,a ) T( s' | s,a )V ( s')

The policy pi : S × N → R is then computed as:

N is the number of steps to go and Gamma is the discount factorAlgorithm developed by Bellman in 57

Generating States using preconditions and effects

Operation: Order

Pre: Ordered = False

Post: Ordered = True

Operation: Cancel

Pre: Ordered = True & Received = false

Post: Canceled=True & Ordered = false

Operation: Return

Pre: Ordered = True & Received = True

Post :Returned = True & Ordered = false and

Received = false

Event: Delayed


Post: Delayed=True & Ordered = True

Event: Received


Post: Received = True

Actions Events Flags

Ordered

Received

Delayed

Cancelled

Returned

Use an algorithm similar to reachability analysis to generate states

Not possible to generate without preconditions and effects

Generated State Transition Diagram

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

<OC R Del Rec

State No.

Values of Boolean variables

Explanation

1 Ordered

2 Ordered and Canceled

3 Ordered and Delayed

4 Ordered, Received and Returned

5 Ordered, Delayed and Cancelled

6 Ordered, Delayed, Received and Returned

7 Ordered, Delayed and Received

8 Ordered and Received

s2

s3

s6 s7

s8

s4

s5

W

W

WW

O

R

Rec

Del

Rec

C

O

C

R

OO

s1

DFA = { S, s1, F, T, A}

Costs and Probabilities

• Costs of ordering taken from configuration module– From first two service sets

• Optimal supplier and alternate supplier

• Probability of delay and cost of returning and canceling taken from supplier policy– Can be represented using WS-Policy or WS-

Agreement

Supplier Policy– The supplier gives a probability of 55% for delivering the

goods on time.– The manufacturer can cancel or return goods at any

time based on the terms given below.• If the order is delayed because of the supplier, the order

can be cancelled with a 5% penalty to the manufacturer.• If the order has not been delayed, but it has not been

delivered yet, it can be cancelled with a penalty of 15% to the manufacturer.

• If the order has been received after a delay, it can be returned with a penalty of 10% to the manufacturer.

• If the order has been received without a delay, it can be returned with a penalty of 20% to the manufacturer.

Costs and Probabilities

Current State Action Next State Cost

<O CR Del Rec NOP <O CR Del Rec 0

<O CR Del Rec CANCEL <O CR Del Rec 150

<O CR Del Rec DEL <O C R Del Rec 0

<O CR Del Rec RECEIVE <O C R Del Rec 0

<O CR Del Rec ORDER <O CR Del Rec 100

<O C R Del Rec NOP <O C R Del Rec DelayCost = {200, 300, 400}

<O C R Del Rec CANCEL <O C R Del Rec 50

<O C R Del Rec RECEIVE <O C R Del Rec 0

<O CR Del Rec ORDER <O CR Del Rec 100

<O C R Del Rec ORDER <O CR Del Rec 100

<O C R Del Rec ORDER <O CR Del Rec 100

<O C R Del Rec CANCEL <O C R Del Rec 150

<O C R Del Rec NOP <O C R Del Rec 0

<O C R Del Rec RETURN <O CR Del Rec 200

<O C R Del Rec NOP <O C R Del Rec 0

s2

s3

s6 s7

s8

s4

s5

W

W

WW

O

R

Rec

Del

Rec

C

O

C

R

OO

0.45

0.35

0.85

s1

Handling Inter-Service dependencies• Since the RAM and Motherboard must be

compatible, the actions of service managers (SMs) must be coordinated

• For example, if MB delivery is delayed, and MB SM wants to cancel order and change supplier, the RAM SM must do the same

• Hence, coordination must be introduced in SM-MDPs

Centralized Approach• State space created by Cartesian product of transition

diagrams

• Joint actions from each state

• Transition probability created by multiplying states

• Costs created by adding cost per action from each state– Compatible actions given rewards– Incompatible actions given penalties

• Optimal but exponential with number of manager

Decentralized Approach

• Simple coordination mechanism

• If one service manager changes suppliers– All dependent managers must change

suppliers

• Low complexity but sub-optimal

Hybrid Approach• If the policy of some SM dictates it to change suppliers, the

following actions happen:– it sends a coordinate request to PM – PM gets the current cost of changing suppliers or current

optimal action by polling all SMs

• It takes the cheapest action (change supplier or continue)

• A bit like decentralized voting- will change suppliers if majority are delayed

• It mirrors performance of centralized approach and has complexity like the decentralized approach

Dynamic and Adaptive Processes in Healthcare

Figure and Table from a joint Amit Sheth, Prashant Doshi publication

Relevant Event Type Effects on the Pathway

1.Adverse drug reaction Stop drug therapy or reduce dosage

1.Sudden worsening of symptoms Increase dosage or modify pathway by initiating new therapy

1.New drug alert Prescribe the drug for the appropriate activity

1. Newly discovered drug-drug interaction

Add new dependency in the pathway

1.New co-morbidity Possibly modify the pathway or drug prescriptions

AM

Empirical Evaluation

Evaluating Dynamic Configuration• Evaluation with help of the supply chain

scenario

• Use the variations in currency exchange rates of China and Taiwan as the primary factor affecting supplier prices

• Assume that process is dynamically configured every fortnight

Observations

• Static binding – Configured at the first run and same partners are

persisted with for all subsequent runs– Cost changes due to variations in currency

• Dynamic binding– Dynamically configured with latest prices for all runs– With just ILP (Dynamic1) Always the lowest cost, logical

constraints not guaranteed– With ILP and SWRL (Dynamic2) Lowest cost for partners

satisfying all constraints

Results – Process Configuration

800

900

1000

1100

Jan

FebM

ar AprM

ay Jun Ju

lAug Sep O

ct

Month of configuring process

Pro

cess

co

st p

er u

nit

(in

$)

Static Process

Dynamic1 Process -only ILP

Dynamic2 Process -both ILP and SWRL

15.2%

2.73%

7.1%

Average Cost

Difference: 9.32%

Evaluating Process Adaptation• Evaluation with the help of the supply chain

scenario

• Two main parameters used for the evaluation– Probability of Delay – (probability that an item ordered

from a supplier will be delayed)– Penalty of Delay – (cost for the manufacturer for not

reacting to delay)

• Total process cost = $1000 and cost of changing suppliers (CS) =$200

Cost of Waiting = 200

900

1300

1700

2100

2500

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Probability of delay

Ave

rag

e C

ost

M-MDP

Random

Hyb. MDP

MDP-CoM

Evaluating Adaptation

KEY

M-MDP: Centralized

Random: Random process (changes suppliers for 50% of delays)

Hyb. Com: Hybrid

MDP-Com: Decentralized



900

1300

1700

2100

2500

0.1 0.2 0.3 0.4 0.5 0.6 0.7


Ave

rag

e C

ost

M-MDP

Random

Hyb. MDP

MDP-CoM



900

1300

1700

2100

2500

0.1 0.2 0.3 0.4 0.5 0.6 0.7


Ave

rag

e C

ost

M-MDP

Random

Hyb. MDP

MDP-CoM

Observations

• Results

– For Penalty = 200 (cost of CS = cost of delay), MDP always waits

– For Penalty = 300, 400 (cost of CS < cost of delay), MDP changes at lower prob., waits at higher prob.

• Conclusions

– Thus MDP makes intelligent decisions and outperforms random process that changes suppliers 50% of the time it is delayed

– Centralized MDP performs the best, followed by Hybrid MDP

Evaluating Adaptation with Extended Scenario• In previous model length of delay was not

considered• Three delay events instead of 1

– Del1 (0-7 days)– Del2 (7-21 days)– Del3 (21 days and more)

• Adaptation graph exhibits exactly the same behavior

Evaluating Adaptation with Extended Scenario

Delay Penalty = 300 Dollars (Extended Scenario)

1000

1100

1200

1300

1400

1500

1600

1700

1 2 3 4 5 6 7


Ave

rag

e C

ost

Centralized MDP

Random

Hybrid MDP

Dentralized MDP

Testing Adaptation with Configuration• Process executed in two modes

– Configuration with random adaptation– Configuration with Hybrid MDP based

adaptation

• Tested across different probabilities

• MDP based adaptation outperforms random adaptation

Testing Adaptation with Configuration

Configuration with Adaptation

0

200

400

600

800

1000

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Delay Probability

Co

st (

$)

Dynamic ProcessAverage Cost

Random Adaptation

MDP based adaptation

Static Process Cost

Static Process withRandom adaptation

Static Process with MDPbased adaptation

Architecture

METEOR-S Middleware

Axis 2.0 Based Architecture

Configuration Architecture

Adaptation Architecture

Conclusions, Related Work and Future Agenda

Summary - Dynamic Process Configuration• Showed how domain knowledge in ontologies can

be used with ILP for configuration

• Multi-paradigm approach for constraint analysis to handle broader set of constraints

• In business and scientific processes, configuration is an important problem– Especially in WS based systems where businesses are

seeking to create dynamic processes– This thesis is the first comprehensive work in this area.

Summary - Adaptation• Showed the utility of Markov Decision Processes for optimal

adaptation of Web processes

– Adaptation is need to handle logical failures and events

– Whether to adapt or not depends on the cost of the failure• For this evaluation it was the cost of the delay

• In the real world things often go wrong or not as expected– Earlier processes were static or real time events were not

available as easily– Many researchers/industry vendors seeking to create adaptive

business process frameworks– This is one of the first works that provides cost based

adaptation

Related Work• Semantic Web Services

– OWL-S, WSMO, FLOWS

• Quality driven composition [1]– Uses ILP for optimizing processes– Our work uses a multi-paradigm approach to considering a broader set of

constraints

• Support in Websphere [2] and Oracle BPEL Engine for runtime binding.– Based on replacing services with same interfaces. Service selection is not the

focus– Our focus is on finding optimal services based on process constraints

• Automated workflow composition– Plethora of work based on automatically generating processes based on high

level goals. [3]– Our focus is on configuring pre-existing processes.

[1] L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, Q. Sheng: Quality driven Web services composition, WWW 2003

[2] Dynamic service binding with WebSphere Process Choreographer, http://www-128.ibm.com/developerworks/webservices/library/ws-dbind/

[3] J. Rao and X. Su. "A Survey of Automated Web Service Composition Methods". SWSWPC 2004.

Related work• Focus on correctness of changes to control flow structure

– Adept[1], Workflow inheritance [2], METEOR

• Use of ECA rules [3] to automatically make changes

• Change of service providers based on migration rules in E-Flow [4]

• We extend previous work in this area by using:– Cost based adaptation – Coordination Constraints across services

[1] M. Reichert and P. Dadam. Adeptflex-supporting dynamic changes of workflows without losing control. Journal of Intelligent Information Systems, 10(2):93–129, 1998[2] W. van der Aalst and T. Basten. Inheritance of workflows: an approach to tackling problems related to change. Theoretical Computer Science, 270(1-2):125–203, 2002.[3] R. Muller, U. Greiner, and E. Rahm. Agentwork: a workflow system supporting rule-based workflow adaptation. Journal of Data and Knowledge Engineering, 51(2):223–256, 2004.[4] Fabio Casati, Ski Ilnicki, Li-jie Jin, Vasudev Krishnamoorthy, Ming-Chien Shan: Adaptive and Dynamic Service Composition in eFlow. CAiSE 2000: 13-31

Future Work

• To apply this framework to more business and scientific problems

• Study impact of ubiquitous computing (especially event generation) on dynamic process configuration

• Move towards autonomic Web processes

Publications• Dynamic Process Configuration

– K. Verma, R. Akkiraju, R. Goodwin, P. Doshi, J. Lee, On Accommodating Inter Service Dependencies in Web Process Flow Composition, Proceedings of the AAAI Spring Symposium on Semantic Web Services, March, 2004, pp. 37-43

– R. Aggarwal, K. Verma, J. A. Miller, Constraint Driven Composition in METEOR-S, SCC 2004.

– K. Verma, K.Gomadam, J. Miller and A. Sheth, Configuration and Execution of Dynamic Web Processes, LSDIS Lab Technical Report, 2005.

• Adaptation– K. Verma, A. Sheth, Autonomic Web Processes, ICSOC 2005– K. Verma, P. Doshi, K. Gomadam, A. Sheth, J. Miller, Optimal Adaptation of Web

Processes with Co-ordination Constraints, ICWS 2006.

• Semantic Policy/SLA Representation and Matching– K. Verma, R. Akkiraju, R. Goodwin, Semantic Matching of Web Service Policies

SDWP 2005 & Filed Patent– N. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Based Partner

Selection, WWW 2006 (nominated for best student paper)

Publications

• Semantic Web Service Discovery– K. Verma, K. Sivashanmugam, A. Sheth, A. Patil, S. Oundhakar and John Miller,

METEOR-S WSDI: A Scalable Infrastructure of Registries for Semantic Publication and Discovery of Web Services, JITM

– K. Sivashanmugam, K. Verma, A. Sheth, Discovery of Web Services in a Federated Registry Environment, ICWS04

• Semantic Annotation/Representation– Rama Akkiraju, Joel Farrell, John Miller, Meenakshi Nagarajan, Amit Sheth, and

Kunal Verma, Web Service Semantics, WSDL-S W3C Member Submission– K. Sivashanmugam, Kunal Verma, Amit Sheth, John A. Miller, Adding Semantic to

Web Service Standards, ICWS 2003.

• Semantic Web Composition– K. Sivashanmugam, J. Miller, A. Sheth, and K. Verma, Framework for Semantic

Web Process Composition, International Journal of Electronic Commerce, Winter 2004-5, Vol. 9(2) pp. 71-106

Backup Slides

Semantics for Web Services and Processes • Functional and Data Semantics

– Service (WSDL-S)[1]

• Non-Functional Semantics– Policies (Define tags to capture semantic

information 2])• Business Level Policies, Process Level Policies,

Instance Level Policies Individual Component Level Policy

– Agreements (SWAPS) [3]

• Execution Semantics– State Transitions based on exceptions/failures – Process (BPEL + Semantic Templates) [4]

• Ontologies– Domain Specific Ontologies – RosettaNet,

SUMO Finance– Domain Independent/Upper Ontologies

[1] Web Service Semantics – WSDL-S, W3C Member Submission., http://www.w3.org/Submission/WSDL-S/

[2] K. Verma, R. Akkiraju, R. Goodwin, Semantic matching of Web service policies, SDWP, 2005

[3] N. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Partner, WWW 2006

[4] K. Sivashanmugam, J. Miller, A. Sheth, and K. Verma, Framework for Semantic Web Process Composition, IJEC, 2004

Timing Overheads

• Comparison of overheads due to dynamic process configuration

• Static Binding: BPEL process with pre-defined partners run on BPWS4J engine

•Dynamic Binding: Run using Axis 2.0 based architecture and BPWS4J engine

0

200

400

600

800

1000

No Configuration. Static Binding Configuration w ith Discovery

Tim

e (m

s)

Process Execution Time Discovery Constraint Analysis

Convergence of Value Function

Marginalizing events

Hybrid Approach

State Generation AlgorithmAlgorithm: Generate States (s0) Start with initial state s0 // e.g. (ordered=false) Add s0 to a set S While s( s S ) and s is unmarked //states While Aa( a )& s satisfies pre( a ) //actions create ns by applying effect(a) to s if ( ns S ) Add ns to set S Create edge from s to ns end if end while //actions While Ee( e )& s satisfies pre( e ) //effects create ns by applying effect(e) to s if ( ns S ) Add ns to set S Create edge from s to ns end if end while //effects mark s as visited End while //states

Use of ontologies enables shared understanding between the service provider and service requestor

Semantic Publication and Template Based Discovery

WSDL

<Operation>

<Input1>

<Output1>

Service Template

Operation:buyTicket

Input1:TravelDetails

Output1:Confirmation

Annotations

Publish

Search

UDDI

Class

TravelServices

Class

DataClass

Operations

subClassOf subClassOf

subClassOfsubClassOf subClassOf subClassOf

ClassTicket

Information

ClassTicket

Booking

ClassTicket

Cancellation

ClassConfirmation

Message

Operation:cancelTicket

Input1:TravelDetails

Output1:Confirmation

For simplicity of depicting, the ontology is shown with classes for both operation and dataAdding Semantics to Web Services Standards

Syntactic, QoS, and Semantic (Functional & Data) Similarity

Name,Description,

….

Name,Description,

….

Name,Description,

…

Name,Description,

…

XY

ABC

Web Service Web Service

Similarity ?

A2A2A1A1Calendar-Date

…

…

Event

…

Similarity ?


Functional & Data Similarity

Functional & Data Similarity

]1..0[, and

],1..0[).,.().,.(

),(

21

21

21

sdSOsdSTSynDSsnSOsnSTSynNS

SOSTtySynSimilar

3 ),,(QoSdimD*),,(QoSdimD*),,(QoSdimD

),ty(OpSimilari

yreliabilitSOSTcostSOSTtimeSOST

SOST

SyntacticSimilaritySyntacticSimilarity

PurchasePurchaseBuyBuy

XY

ABC

QoS QoS


Similarity ?

QoSSimilarity

QoSSimilarity

Web ServiceDiscovery

Web ServiceDiscovery

Area

Coordinates

Forrest

{name}

{x, y}

Information Function

Get Information Get Date

METEOR-S Web Service Discovery Infrastructure (MWSDI)• MWSDI deals with adding semantics to

UDDI registries• Provides transparent access to UDDI

registries based on their domain or federation

• Implementation of UDDI Best Practices and Semantic Discovery

1 http://lsdis.cs.uga.edu/Projects/METEOR-S

Extended Registries Ontologies (XTRO)• Provides a multi-

faceted view of all registries in MWSDI – Federations– Domains– Registries

subDomainOf

supports

belongsTo

consistsOf

belongsToFederation

Ontology

Registry

Domain

RegistryFederation

Variations in Chinese and Taiwanese Currency

Source for graphs and data: www.x-rates.com

Generated State Transition Diagram

DFA = { S, s1, F, T, A}

S = set of states

s1 = start state

F = set of final states

T = Transition Function T : S × A → S

A = Finite set of actions and events

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Configuration and Adaptation of Semantic Web Processes Kunal Verma Ph.D. Thesis Defense (6/13/2006)...

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