A Survey of Process Mining in ProM

1. 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

2. 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

3. 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

4. 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

5. What is Process Mining? (cont’) 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. An example of Process Mining Paradigm 3. How to get any answer about process execution: it can extract any answer through a process mining technique. An Example of Process Mining Paradigm DSL – 7 September 2009

6. 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

7. 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.215-49. van der Aalst, W.M.P. (2004a), "Process mining: a research agenda", Computers in Industry, Vol. 53 pp.231-44. DSL – 7 September 2009

8. Background of Process Mining Techniques (2) 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). An Example of Block-oriented Meta Model References: van der Aalst, W.M.P. (2003), "Workflow mining: a survey of issues and approaches", Data & Knowledge Engineering, Vol. 47 pp.237-67. Aguilar-Saven, R.S. (2004), "Business process modelling: review and framework", International Journal of Production Economics, Vol. 90 pp.129-49. DSL – 7 September 2009

9. Background of Process Mining Techniques (3) - 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

10. 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

11. Effectiveness of Process Mining - 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? Using process mining, typical manager questions that can be answered include: DSL – 7 September 2009

12. 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

13. 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

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

15. 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). Example of a bipartite graph • Petri nets were invented in August 1939 by Carl Adam Petri – at the age of 13. DSL – 7 September 2009

16. 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

17. 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

18. Petri Net as Graphs (cont’) Firing Example: 2H2 + O2 2H2O Starting graph After firing DSL – 7 September 2009

19. 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

20. 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

21. 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

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

23. 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

24. 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

25. An Example of Organizational Miner DSL – 7 September 2009

26. 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

27. …Thank you…