Ontologies And Process Mining

George [email protected]

Business Process Mining © 2009

Ontologies and their use in Semantic Process Mining

Semantic Process Mining

•Based on Ontologies

•How do we discover Ontologies?

•Start with OOBE Business Patterns®


OOBE Business Patterns

•Process Business Objects

•Entity Business Objects

•Event Business Objects


Process Business Objects

•Business Verbs

•Represent Processes


Process Business Objects• Seven Process Business Objects from which all others can be defined


Process BusinessObject

Developing Procuring Producing Promoting Selling Delivering Billing

Entity Business Objects

•Business Nouns

•People, Places & Things

•Resources, Players & Constructs


Entity Business Objects


EntityBusinessObject

Resource Product or Service Party Transfer Agreement Plan Location

Entity Object - Agreement


Agreement

Compensation Agreement

EmploymentAgreement

Risk Assumption

Contract

Insurance Policy Lease Guarantee

QuoteSupplier Contract Licence Policy

Legislation

Entity Object – Business Party


Business

Party

Individual

Consumer

Beneficiary Buyer Payee Recipient

Employee

Direct

Permanent

Full Time

Part Time

Temporary

Seconded

Organisation

Supplier Owner Employer

Regulator

Event Business Objects

•Business Events

•Business Time, Product & Life Cycle events

•Event Business Objects are triggers for or results of steps in Business Process Objects


Event Business Objects• Seven Event Business Objects from which all others can be defined


EventBusinessObject

Calendar Time Clock Time Business

TimeElapsed

TimeEnvironment

Time Bound Occurrence

Business Objects and Ontologies • Semantic Business Process Mining is based on Ontologies

http://www.ip-super.org

• OOBE Business Patterns® create a framework for the Ontologieshttp://www.promendo.com

• Incorporate Business Patterns into WSML (ontologies)http://www.wsmostudio.org

• Ontologies then annotate process mining data (ProM)http://prom.win.tue.nl/tools/prom/

• Patterns are important to ensure that nothing is missed


Process_ID EventName EventType Timestamp Originator Type Server Outcome

33116 VRU start 1/01/1999 0:00:31 No_Server PS No_Server Hang

33116 VRU complete 1/01/1999 0:00:36 No_Server PS No_Server Hang

33117 VRU start 1/01/1999 0:34:12 No_Server PS No_Server Hang

33117 VRU complete 1/01/1999 0:34:23 No_Server PS No_Server Hang

33118 VRU start 1/01/1999 6:55:20 Michael PS Michael Agent

33118 VRU complete 1/01/1999 6:55:26 Michael PS Michael Agent

33118 Regular_Service start 1/01/1999 6:55:44 Michael PS Michael Agent

33118 Regular_Service complete 1/01/1999 6:56:37 Michael PS Michael Agent

33119 VRU start 1/01/1999 7:41:16 Jack PS Jack Agent

33119 VRU complete 1/01/1999 7:41:26 Jack PS Jack Agent

33119 Regular_Service start 1/01/1999 7:41:27 Jack PS Jack Agent

33119 Regular_Service complete 1/01/1999 7:44:53 Jack PS Jack Agent

Process Discovery

Example of an ontology candidate


Semantic Data


Operations Dept manages human resources- by team

StatisticalData


Analyse performance by task & abstraction

Semantic Data


Payroll Deptmanages human resources by – Business Party

Statistical Data


Analyse performance using ‘Business Party’ abstraction

Summary•Ontologies are needed for Semantic Process Mining

•Business Patterns provide a template when you need to create an Ontology

•Ontologies provide different perspectives for the same data

•Same total result (Time & Frequency) in all Ontologies – just a different view
