A Survey of Process Mining in ProM By Jantima Polpinij Decision Systems Lab (DSL) Seminar School of Computer Science and Software Engineering Faculty of.

Slides:



Advertisements
Similar presentations
A Workflow Engine with Multi-Level Parallelism Supports Qifeng Huang and Yan Huang School of Computer Science Cardiff University
Advertisements

From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining Nikola Trčka Mykola Pechenizkiy.
Principles of Engineering System Design Dr T Asokan
Based on: Petri Nets and Industrial Applications: A Tutorial
Software and Systems Engineering Seminar Winter 2011 Domain-specific languages in model-driven software engineering 1 Speaker: Valentin ROBERT.
/faculteit technologie management 1 Process Mining: Organizational and Conformance Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros.
Advanced Manufacturing Laboratory Department of Industrial Engineering Sharif University of Technology Session # 5.
Discrete Interaction Design Specification Prof. Dr. Matthias Rauterberg Faculty Industrial Design Technical University of Eindhoven
Use-case Modeling.
Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei ( )
Topics Creating DFD Physical and logical DFD Event driven modeling
Business Alignment Using Process Mining as a Tool for Delta Analysis Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information.
Data Mining By Archana Ketkar.
Discovering Coordination Patterns using Process Mining Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology.
Chapter 1: Overview of Workflow Management Dr. Shiyong Lu Department of Computer Science Wayne State University.
/faculteit technologie management 1 Process Mining: Extension Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.
Chapter 3 : Software Process and Other Models Juthawut Chantharamalee Curriculum of Computer Science Faculty of Science and Technology, Suan Dusit University.
Introduction to Systems Analysis and Design
Computer Science Universiteit Maastricht Institute for Knowledge and Agent Technology Data mining and the knowledge discovery process Summer Course 2005.
A university for the world real R © 2009, Chapter 17 Process Mining and Simulation Moe Wynn Anne Rozinat Wil van der Aalst Arthur.
Basic Concepts The Unified Modeling Language (UML) SYSC System Analysis and Design.
A university for the world real R © 2009, Chapter 23 Epilogue Wil van der Aalst Michael Adams Arthur ter Hofstede Nick Russell.
Insuring Sensitive Processes through Process Mining Jorge Munoz-Gama Isao Echizen Jorge Munoz-Gama and Isao Echizen.
Data Mining Techniques
1 Conceptual Modeling of User Interfaces to Workflow Information Systems Conceptual Modeling of User Interfaces to Workflow Information Systems By: Josefina.
BPMN By Hosein Bitaraf Software Engineering. Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes.
程建群 博士(Dr. Jason Cheng) 年03月
Chapter 8 Architecture Analysis. 8 – Architecture Analysis 8.1 Analysis Techniques 8.2 Quantitative Analysis  Performance Views  Performance.
Capacity analysis of complex materials handling systems.
Jorge Muñoz-Gama Universitat Politècnica de Catalunya (Barcelona, Spain) Algorithms for Process Conformance and Process Refinement.
SOFTWARE DESIGN AND ARCHITECTURE LECTURE 21. Review ANALYSIS PHASE (OBJECT ORIENTED DESIGN) Functional Modeling – Use case Diagram Description.
Chapter 1: Overview of Workflow Management Dr. Shiyong Lu Department of Computer Science Wayne State University.
Querying Business Processes Under Models of Uncertainty Daniel Deutch, Tova Milo Tel-Aviv University ERP HR System eComm CRM Logistics Customer Bank Supplier.
Framework for the Development and Testing of Dependable and Safety-Critical Systems IKTA 065/ Supported by the Information and Communication.
CY2003 Computer Systems Lecture 7 Petri net. © LJMU, 2004CY2003- Week 72 Overview Petri net –concepts –Petri net representation –Firing a transition –Marks.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
UML as a Specification Language for Embedded Systems. By, Mir Ahmed Ali, Asst. Professor, ECM department, SNIST. By, Prof. Narsiah sir, Director of School.
Process-oriented System Analysis Process Mining. BPM Lifecycle.
A university for the world real R © 2009, Chapter 9 The Runtime Environment Michael Adams.
Petri Nets Lecturer: Roohollah Abdipour. Agenda Introduction Petri Net Modelling with Petri Net Analysis of Petri net 2.
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
Modelling by Petri nets
Decision Mining in Prom A. Rozinat and W.M.P. van der Aalst Joosung, Ko.
1 Centre for Intelligent Systems and their Applications Division of Informatics, University of Edinburgh Draft for AKT July Workshop Jessica Chen-Burger.
1 Copyright  2001 Pao-Ann Hsiung SW HW Module Outline l Introduction l Unified HW/SW Representations l HW/SW Partitioning Techniques l Integrated HW/SW.
Software Engineering1  Verification: The software should conform to its specification  Validation: The software should do what the user really requires.
CSCI1600: Embedded and Real Time Software Lecture 11: Modeling IV: Concurrency Steven Reiss, Fall 2015.
SSQSA present and future Gordana Rakić, Zoran Budimac Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad
Chapter 7 Part II Structuring System Process Requirements MIS 215 System Analysis and Design.
Maikel Leemans Wil M.P. van der Aalst. Process Mining in Software Systems 2 System under Study (SUS) Functional perspective Focus: User requests Functional.
Process Mining – Concepts and Algorithms Review of literature on process mining techniques for event log data.
Profiling and process mining What has been done???
Visualization in Process Mining
30 januari 2018 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
Data Mining Generally, (Sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it.
Course Outcomes of Object Oriented Modeling Design (17630,C604)
Profiling based unstructured process logs
Object-Oriented Software Engineering Using UML, Patterns, and Java,
Unified Modeling Language
Software Engineering Chapter 5 (Part 3) System Modeling Dr.Doaa Sami.
Workflow Management Systems
CAP 4800/CAP 5805: Computer Simulation Concepts
CAP 4800/CAP 5805: Computer Simulation Concepts
Analysis models and design models
CAP 4800/CAP 5805: Computer Simulation Concepts
Business Alignment Using Process Mining as a Tool for Delta Analysis
19 augustus 2019 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
Petri Nets Laurie Frazier.
Presentation transcript:

A Survey of Process Mining in ProM By Jantima Polpinij Decision Systems Lab (DSL) Seminar School of Computer Science and Software Engineering Faculty of Informatics DSL – 7 September 2009

Outline - What is Process Mining? - Objectives of Process Mining - Background of Process Mining - Current Process Mining Techniques - Effectiveness of Process Mining - A Process Mining Tool: ProM DSL – 7 September 2009

What is Process Mining? -Process mining is to automatically determine and analyse actual process execution – How the processes are performing in a complete new and process oriented way. - The basic idea behind Process Mining is to extract knowledge from event logs, recorded by IT systems. - Data Mining practice has been developed and adapted to create the business process-mining techniques that are now being used to mine data logs containing process execution data. DSL – 7 September 2009

What is Process Mining? (cont’) - Note that, this concept is not limited to IT system, it can also be used to monitor other operational processes or system such as - Complex workflows in a large enterprise - Complex device working (e.g. X-ray machines, supercomputer, etc.) DSL – 7 September 2009

An example of Process Mining Paradigm 1. Information System: It contains valuable information about (the performance of) the organization. 2. The Event Logs: It contains historical data of actual process execution. Indeed, it contains the implicit answers to the famous questions: Who did, What, When and How. 3. How to get any answer about process execution: it can extract any answer through a process mining technique. What is Process Mining? (cont’) An Example of Process Mining Paradigm DSL – 7 September 2009

Objectives of Process Mining Using the knowledge that is extracted from event logs - To maintain business processes - To improve real business processes - To (re)design actual business process An Example of Process Redesign Cycle DSL – 7 September 2009

Background of Process Mining Techniques (1) - Agrawal et al. (1998) were early pioneers of process mining. Their algorithmic approach to process mining allowed the construction of process flow graphs from execution logs of a workflow application. - The discipline of process mining also has its roots in the work of Cook and Wolf (1998) who attempted to discover software process models from the data contained in event logs. - van der Aalst (2004) compares the method of extracting process models from data with that of distillation. - In terms of business process mining, van der Aalst (2004) states that almost any transactional information system can provide suitable data. References: Agrawal, R., Gunopulos, D., Leymann, F. (1998), "Mining process models from workflow logs", in Schek, H.J. (Eds),Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology, Springer Verlag, Heidelberg,. Cook, J.E., Wolf, A.L. (1998a), "Discovering models of software processes from event-based data", ACM Transactions on Software Engineering and Methodology, Vol. 7 No.3, pp van der Aalst, W.M.P. (2004a), "Process mining: a research agenda", Computers in Industry, Vol. 53 pp DSL – 7 September 2009

- van der Aalst (2003) identifies two broad types of workflow meta models: (1) Block-orientated meta model (2) Graph-orientated meta model Each model contains with their own language and graphical representation. - Aguilar-Saven (2004) adds net-based languages to this definition (with block-oriented models/languages being grouped under the term workflow languages). References: van der Aalst, W.M.P. (2003), "Workflow mining: a survey of issues and approaches", Data & Knowledge Engineering, Vol. 47 pp Aguilar-Saven, R.S. (2004), "Business process modelling: review and framework", International Journal of Production Economics, Vol. 90 pp An Example of Block-oriented Meta Model DSL – 7 September 2009 Background of Process Mining Techniques (2)

- The most common form of graph oriented meta-model is the directed graph. - Agrawal et al. (1998) was one of the first to use directed graphs in process mining. - The author describes a number of constructs involved in the actual graph. Activities, usually enclosed in boxes or circles, are referred to as vertices and the arrows between the activities, that indicate the direction of flow, are known as edges. Examples of Graph-oriented Meta Model References: Agrawal, R., Gunopulos, D., Leymann, F. (1998), "Mining process models from workflow logs", in Schek, H.J. (Eds),Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology, Springer Verlag, Heidelberg. DSL – 7 September 2009 Background of Process Mining Techniques (3)

Current Process Mining Techniques There are several techniques that may be used to perform mining of business process such as: – Genetic algorithms: Algorithms designed around the process of Darwinian natural selection (Alves de Medeiros et al. 2004) – General algorithmic approach: Custom algorithms designed for mining processes by individual authors (van der Aalst and Song, 2004) – Petri Net. – Markovian approach: An algorithm that examines past and future behaviour to define a potential current state (Cook and Wolf, 1998a). – Neural network: Models the human mind in its ability to “learn” and then identify patterns in data (Cook and Wolf, 1998a). – Cluster analysis: Divides a group of solutions into homogenous sub groups (Schimm, 2004). DSL – 7 September 2009

Effectiveness of Process Mining Using process mining, typical manager questions that can be answered include: - What is the most frequent path in a process? - To what extend do the cases comply with a process model? - What are the routing probabilities in a process? - What are the throughput times of a cases? - What are the service times for a tasks? - When will a case be completed? - How much time was spent between any two tasks in a process? - What are the business rules in a process, and are they being obeyed? - How many of people are typically involved in a case? - Which people are central in an organization? DSL – 7 September 2009

A Process Mining Tool: ProM ProM (Process Mining) is a generic open-source framework for implementing process mining tools in a standard environment. It is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java. The ProM framework receives as input logs in the Mining XML (MXML) format. DSL – 7 September 2009

Mining Plugins There are mining plugins, such as: – Plugins supporting control-flow mining techniques (such as the Alpha algorithm, Genetic mining, Multi-phase mining,...) – Plugins analysing the organizational perspective (such as the Social Network miner, the Staff Assignment miner,...) – Plugins dealing with the data perspective (such as the Decision miner,...) – Plugins for mining less-structured, flexible processes (such as the Fuzzy Miner) – Elaborate data visualization plugins (such as the Cloud Chamber Miner) Furthermore, there are analysis plugins dealing with: – The verification of process models (e.g., Woflan analysis) – Verification of Linear Temporal Logic (LTL) formulas on a log – Checking the conformance between a given process model and a log – Performance analysis (Basic statistical analysis, and Performance Analysis with a given process model) DSL – 7 September 2009

An Overview of Process Mining in ProM DSL – 7 September 2009

Petri Net It is one of several mathematical modelling languages for the description of discrete distributed systems. A Petri net is a directed bipartite graph, in which the nodes represent transitions (i.e. discrete events that may occur), places (i.e. conditions), and directed arcs (that describe which places are pre- and/or post-conditions for which transitions). Petri nets were invented in August 1939 by Carl Adam Petri – at the age of 13. Example of a bipartite graph DSL – 7 September 2009

Petri Net as Graphs In Petri nets nodes of the first subset of vertices are called places, nodes of the second is transitions. ● Places: usually model resources or partial state of the system. The symbol of a place is a circle or an ellipse ● Transitions: model state transition and synchronization. The symbol of transition is a solid bar or a rectangle ● The edges of the graph are called arcs Tokens ● The tokens are denoted by a solid dot and can be placed inside the place symbol. ● They indicate presence or absence of, for example, resource. ● Places can hold any number of tokens or only a limited number (capacitated places). DSL – 7 September 2009

Petri Net as Graphs (cont’) Transition (firing) rule: A transition t is enabled if each input place p has at least w(p, t) tokens. An enabled transition may or may not fire. A firing on an enabled transition t removes w(p, t) from each input place p, and adds w(t, p') to each output place p'. DSL – 7 September 2009

Petri Net as Graphs (cont’) Firing Example: 2H 2 + O 2  2H 2 O Starting graphAfter firing DSL – 7 September 2009

Petri Net in ProM The type of data in an event log determines which perspectives of process mining can be discovered. ProM is used for mining control-flow from event logs. If the log (i) provides the tasks that are executed in the process and (ii) it is possible to infer their order of execution and link these tasks to individual cases (or process instances), then the control flow perspective can be mined. DSL – 7 September 2009

An Example of Petri Net in ProM Petri net illustrating the control-flow perspective that can be mined from the event log DSL – 7 September 2009

Cleaning the Log - To get a better solution for mining knowledge from event logs, the log should be cleaned before mining knowledge. - In ProM, a log can be filtered by applying the provided Log Filter. - There are five log filters: Processes, Event types, Start events, End event, and Events. - The processes log filter is used to select which processes should be taken into when running a process mining algorithm. Note that a log may contain one or more processes types. - The event types log filter allows us to select the types of events (or tasks) that we want to consider while mining the log. - The Start events filters the log so that only the traces (or cases) that start with the indicated tasks are kept. - The End Events works in a similar way, but the filtering is done with respect to the final tasks in the log trace. The Event filter is used to set which events to keep in the log. DSL – 7 September 2009

- To mine the control-flow of a process from an event log. The Examples of Effectiveness of ProM (1) DSL – 7 September 2009

The Examples of Effectiveness of ProM (2) - To mine organizational-related information about a process. - It can help to answer questions regarding to social (organizational) aspect of an organization. The questions should be: 1. How many people are involved in a specific case? 2. What is the communication structure and dependencies among people? 3. How many transfers happen from one role to another role? 4. Who are important people in the communication flow? (the most frequent flow) 5. Who subcontracts work to whom? 6. Who work on the same tasks? - These and other related questions can be answered by using the mining plug-ins Social Network Miner and Organizational Miner, and the analysis plug-in Analyze Social Network. DSL – 7 September 2009

An Example of The Analyzer Social Network - A social network is a description of the social structure between actors, mostly individuals or organizations. - It indicates the ways in which they are connected through various social familiarities ranging from casual acquaintance to close familiar bonds. DSL – 7 September 2009

An Example of Organizational Miner DSL – 7 September 2009

Evaluation Mining Techniques in Prom - ProM uses the same evaluation techniques that often are used in information retrieval area: Precision and Recall. - Recall is percentage of all relevant documents that are found by a search. - Precision is Percentage of retrieved documents that are relevant. DSL – 7 September 2009

…Thank you…