Process Mining. Knut Hinkelmann. Prof. Dr. Knut Hinkelmann MSc Business Information Systems

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1 Knut Hinkelmann Prof. r. Knut Hinkelmann MSc usiness Information Systems

2 Learning Objective Topic: Learning Process knowledge from experience learning a process/decision model ase-ased Reasoning (R) reusing previous knowledge We do it because designing a process is difficult Reality of a process often differs from the defined process We cannot foresee all possible situations (in knowledge work) Process has to be adapted to change Objective You can illustrate the two approaches for learning with examples You can compare the two approaches Prof. r. Knut Hinkelmann MSc usiness Information Systems 2

3 Learning from xperience: ontinuous Improvement Strategic ecision esign Implementation xecution Performance valuation We look at two kinds of improvement Improving the Process Models (Re-esign, Implementation) Improving Process xecution by reusing previous knowledge/decisions ase-ased Reasoning (R) Prof. r. Knut Hinkelmann MSc usiness Information Systems 3

4 Learning from xperience: Input ata In order to be able to learn appropriate data must be available data (event log) Learning process knowledge is based on data about real activities that have been executed: tasks performed by a user or system data/content determining the case/state data elements recorded with the event results of decisions resources executing or used during the activity (persons or devices) context like start time, end time, or location This process data is often called event log. Prof. r. Knut Hinkelmann MSc usiness Information Systems 4

5 Fragment of an Sequential vent Log vent logs are information about the activities that have been executed. ach event refers to an activity (i.e., a welldefined step in the process) e aware that this is a sequential event log: the events are already grouped into cases and ordered sequentially by time If this is not given, there has to be pre-processing phase Prof. r. Knut Hinkelmann MSc usiness Information Systems (van der alst 2011, p.13) 5

6 ase-ased Reasoning vs. ase-ased reasoning Operational support at execution time Reuse individual cases onsolidate individual cases into typical process models Prof. r. Knut Hinkelmann MSc usiness Information Systems 6

7 ny individual case or adhoc process may be resolved in a completely ad-hoc manner ut as experience grows in resolving similar cases over time, a set of common practices can be defined for cases. It is the objective of process mining to automatically identifiy these common practices and to discover business process models and decision models tasks to be executed gateways defining possible paths decision criteria to align process models with reality Prof. r. Knut Hinkelmann MSc usiness Information Systems 7

8 Principle of : Process iscovery ase ctivity ase 1 ase 2 ase Source: Harold Loydl and Fluxicon G ( Prof. r. Knut Hinkelmann MSc usiness Information Systems 8

9 Principle of : Process iscovery ase Prof. r. Knut Hinkelmann MSc usiness Information Systems ctivity ase 1 ase 2 ase 3 Source: Harold Loydl and Fluxicon G ( 9

10 Principle of : Process iscovery ase Prof. r. Knut Hinkelmann MSc usiness Information Systems ctivity ase 1 ase 2 ase 3 Source: Harold Loydl and Fluxicon G ( 10

11 Principle of : Process iscovery ase Prof. r. Knut Hinkelmann MSc usiness Information Systems ctivity ase 1 ase 2 ase 3 Source: Harold Loydl and Fluxicon G ( 11

12 Two ways of Learning from xperience There are two ways of learning : generating a process model (design time) pply this model for any new case ase-ased Reasoning: For a new case, find the most appropriate case at run-time If no appropriate case is found, executed the case ad hoc can deliver good results, if enough cases are available for learning Prof. r. Knut Hinkelmann MSc usiness Information Systems 12

13 ata Mining and ata Mining is a special type of data mining, where data are event logs mining results are process models Process Mining Typical examples for models learned by data mining are decision trees and association rules. These can be used for learning the decision model. Recently, process mining techniques have become readily available to discover process models based on event logs. Prof. r. Knut Hinkelmann MSc usiness Information Systems 13

14 Knowledge iscovery in ata ata Mining is a step to discover knowledge in data Prof. r. Knut Hinkelmann MSc usiness Information Systems (Fayyad et al., 1996) 14

15 : ata and Models Process mining is to learn process models from real data. ata and Models are important for processes Process models play a dominant role in (re)design and implementation phases data plays a dominant role in the execution and evaluation phases (= performance evaluation) (= execution) Prof. r. Knut Hinkelmann MSc usiness Information Systems adapted from (van der alst 2011, p. 8) 15

16 ata Selection: Sources for the vent Log In principle there are two kinds of sources for the event log: From information systems like RP systems Information systems (like RP systems) log enormous amounts of data ata can often be related to cases (e.g. customer, product) There are meta-data like time (when data was added) and people (who added the data) From the execution of process/case models (play out) xecution of a predefined, preliminary process/case Workflow-Management systems store a history of tasks (audit trail) dvantage: it is easy to assign tasks to cases and to create a sequential event log Manual logging of tasks Prof. r. Knut Hinkelmann MSc usiness Information Systems 16

17 vent logs coming from process/case execution Play-out refers to the classical use of process or case models; it generates a history of process executions workflow engine can be seen as a Play-out engine that controls cases by only allowing the moves allowed according to the model concrete list of executed tasks resulting from a case execution is also an example of a play-out. process model case plan event log Prof. r. Knut Hinkelmann MSc usiness Information Systems 17

18 Preprocessing: Sequential vent Logs Most information systems store event log information in unstructured form, e.g., scattered over many tables or needs to be tapped off from subsystems exchanging messages. ata extraction and pre-processing steps are an integral part of any process mining effort to create record events sequentially each event refers to an activity (i.e., a well-defined step in the process) each event is related to a particular case (i.e., a process instance) The cases are individual requests and per case a trace of events can be recorded (van der alst 2011, p.9) Prof. r. Knut Hinkelmann MSc usiness Information Systems 18

19 Mining: Three Types of (1) The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today s systems Process mining establishes links between the actual processes and their data on the one hand and process models on the other hand. vent logs can be used to conduct three types of process mining: discovery conformance enhancement Prof. r. Knut Hinkelmann MSc usiness Information Systems (van der alst 2011, p.8f) 19

20 Three Types of (2) iscovery Prof. r. Knut Hinkelmann MSc usiness Information Systems Produces a process model without using any a-priori information. onformance heck if reality conforms to the process model: compare an existing process model with an event log of the same process. Used to detect, locate and explain deviations, and to measure the severity of these deviations nhancement extend or improve an existing process model using information about the actual process recorded in some event log repair: modifying the model to better reflect reality extension: adding a new perspective to the process model (van der alst 2011, p. 10) 20

21 Process iscovery onstruct a process model from event logs can learn both aspects of a business process Process Logic: learning control flow (process model) usiness Logic: Learning decision rules event log Prof. r. Knut Hinkelmann MSc usiness Information Systems decision model (van der alst 2011, p. 7) 21

22 Learning ontrol Flow Learning ontrol Flow Group activities by case (see table) etermine sequences for each case (using time stamp) determine a process model The -algorithm is an algortihm to learn Petri Nets from event logs Prof. r. Knut Hinkelmann MSc usiness Information Systems (van der alst 2011, p. 12ff) 22

23 Learning ecision Rules ecision rules determine the criteria for a gateway. decision at a gateway can be regarded as a classifier: each output corresponds to a class lassification learning determines the criteria for each class c5 corresponds to a gateway. Prof. r. Knut Hinkelmann MSc usiness Information Systems 23

24 10 Learning ecision Trees example Tid mployed Marital Status Taxable Income 1 No Single 125K No accept No mployed Yes 2 Yes Married 160K Yes 3 Yes Single 70K No 4 No Married 120K No 5 Yes ivorced 95K Yes 6 Yes Married 60K No 7 No ivorced 220K No 8 Yes Single 85K Yes NO MarSt Single, ivorced Married TaxInc TaxInc 80K > 80K 100K > 100K NO YS NO YS 9 Yes Married 95K No 10 Yes Single 90K Yes Training ata Model: ecision Tree There could be more than one tree that fits the same data! Prof. r. Knut Hinkelmann MSc usiness Information Systems 24

25 ecision Tree ecision Table mployed No Yes NO MarSt Single, ivorced Married TaxInc TaxInc 80K > 80K 100K > 100K NO YS NO YS decision tree can be transformed into a decision table Prof. r. Knut Hinkelmann MSc usiness Information Systems 25

26 Improving existing Process Models Replay uses an event log and a process model as input. n event log may be replayed for different purposes: onformance checking. etect discrepancies between log and model xtending the model, e.g. with frequencies and temporal information. onstructing predictive models, e.g. to show the expected time until completion Prof. r. Knut Hinkelmann MSc usiness Information Systems 26

27 Perspectives of This figure shows other aspects that can be learned by process mining control flow organisation performance decision criteria Prof. r. Knut Hinkelmann MSc usiness Information Systems 27

28 Perspectives of The control-flow perspective focuses on the control-flow, i.e., the ordering of activities. The goal of mining this perspective is to find a good characterization of all possible paths, e.g., expressed in terms of PMN. The organizational perspective focuses on information about resources hidden in the log, i.e., which actors (e.g., people, systems, roles, and departments) are involved and how are they related. The goal is to either structure the organization by classifying people in terms of roles and organizational units or to show the social network. The case perspective focuses on properties of cases. ases can be characterized by the values of the corresponding data elements. For example, if a case represents a replenishment order, it may be interesting to know the supplier or the number of products ordered. The time perspective is concerned with the timing and frequency of events. When events bear timestamps it is possible to discover bottlenecks, measure service levels, monitor the utilization of resources, and predict the remaining processing time of running cases. Prof. r. Knut Hinkelmann MSc usiness Information Systems 28

29 for Operational Support In most examples process mining is done off-line, i.e., processes are analyzed afterward to see how they can be improved or better understood. Process mining techniques can also be used in an online setting. We refer to this as operational support. xamples: etection of nonconformance at the moment the deviation actually takes place. Time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases Support the case worker by providing plans of similar cases (ase-ased Reasoning) Prof. r. Knut Hinkelmann MSc usiness Information Systems 29

30 ase-ased Reasoning for ase Management Planning at run-time is a fundamental characteristic of ase management and for ad-hoc sub-processes in PMN Planning is concerned with determination of which tasks are applicable R supports reuse of plans The plan (= event log) of each process/case execution is stored in a case base When a new situation occurs, instead of planning from scratch, the R system compares the current situation with previous ones It the retrieves from the case base the plan for the situation that is most similar to the current situation The case worker can reuse the plan or adapt it if appropriate Prof. r. Knut Hinkelmann MSc usiness Information Systems 30

31 ase-ased Reasoning ase-ased Reasoning supports the execution of cases and adhoc processes by providing plans of similar cases. Instead of planning from scratch, the system makes suggestions for plans. ssumption: Similar problems have similar solutions General approach: xperiences are stored as cases To solve a new problem... retrieve similar cases solution of the most similar case is reused in the new situation If the solution is useful, it is stored in the case base Quelle: ergmann Prof. r. Knut Hinkelmann MSc usiness Information Systems 31

32 ase-ased Reasoning R ycle ssumption: Similar problems have similar solutions General approach: experiences are stored as cases To solve a new problem... retrieve similar cases solution of the most similar case is reused in the new situation If necessary the solution is adapted If the new solution is useful, it is stored in the case base (vent Log) Source:. amodt,. Plaza (1994); I ommunications, IOS Press, Vol. 7: 1, pp Prof. r. Knut Hinkelmann MSc usiness Information Systems 32