Introduction. Process Mining post-execution analysis Process Simulation what-if analysis

Transcription

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2 Process mining Process mining is the missing link between model-based process analysis and dataoriented analysis techniques. Through concrete data sets and easy to use software the process mining provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

3 Problem

4 Introduction Correctness, effectiveness and efficiency of business processes are vital to an organization Significant gap between what is prescribed and what actually happens Process owners have limited info about what is actually happening Model-based (static) analysis Validation Verification (correctness of a model) Performance analysis Process Mining post-execution analysis Process Simulation what-if analysis 4

5 Preliminaries: Data Logging Keeping track of execution data Activities that have been carried out Timestamps (Start and end times of activities) esources involved Data Purposes Audit trails Disaster recovery Monitoring Data Mining Process Mining Process Simulation 5

6 Preliminaries: Process Mining Event logs (recorded actual behaviors) Covers a wide-range of techniques Provide insights into control flow dependencies data usage resource involvement performance related statistics etc. Identify problems that cannot be identified by inspecting a static model alone 6

7 Preliminaries: Process Simulation Develop a simulation model at design time Carry out experiments under different assumptions Used for process reengineering decisions Data input is time-consuming and error-prone equires careful interpretation Abstraction of the actual behavior Different assumptions made Inaccurate or Incomplete data input Starts from an empty initial state 7

8 Process Mining Process discovery: "What is really happening?" Conformance checking: "Do we do what was agreed upon?" Performance analysis: "Where are the bottlenecks?" Process prediction: "Will this case be late?" Process improvement: "How to redesign this process?" Etc. 8

9 Example: mining student data Process discovery: "What is the real curriculum?" Conformance checking: "Do students meet the prerequisites?" Performance analysis: "Where are the bottlenecks?" Process prediction: "Will a student complete his studies (in time)?" Process improvement: "How to redesign the curriculum?" 9

10 Process mining: Linking events to models world business processes people machines components organizations models analyzes supports/ controls specifies configures implements analyzes software system records events, e.g., messages, transactions, etc. process/ system model discovery conformance event logs 10

11 Where to start? process control diagnosis process mining process enactment process design implementation/ configuration 11

12 esource Analysis 12

13 Performance analysis showing bottlenecks flow time from A to B bottlenecks throughput time 13

14 Dotted chart analysis short cases time (relative) events case s long cases 14

15 Log examples <Process id="payment_subprocess.ywl"> <ProcessInstance id="3f9dfc e7-b9f7-329b5c6f0ded"> <AuditTrailEntry> <WorkflowModelElement>Check_PrePaid_Shipments_10</WorkflowModelElement> <EventType>start</EventType> <Timestamp> T10:11: :00</Timestamp> <Originator>JohnsI</Originator> </AuditTrailEntry> <AuditTrailEntry> <Data><Attribute name="prepaidshipment">true</attribute></data> <WorkflowModelElement>Check_PrePaid_Shipments_10</WorkflowModelElement> <EventType>complete</EventType> <Timestamp> T10:11: :00</Timestamp> <Originator>JohnsI</Originator> </AuditTrailEntry> </ProcessInstance> </Process> 15

16 Starting point: event logs YAWL logs or other event logs, audit trails, databases, message logs, etc. unified event log (MXML) 16

17 Linking process mining to simulation Gather process statistics using process mining techniques Calibrate simulation experiments with this data Analyze simulation logs in the same way as execution logs 17

18 Data sources for process characteristics Design (Workflow and Organizational Models) Control and data flow Organizational model Initial data values ole assignments Historical (Event logs) Data value range distributions Execution time distributions Case arrival rate esource availability patterns State (Workflow system) Progress state Data values for running cases Busy resources un time for cases 18

19 Alpha algorithm α

20 Process log Minimal information in log: case id s and task id s. Additional information: event type, time, resources, and data. In this log there are three possible sequences: ABCD ACBD EF case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D

21 >,,,# relations Direct succession: x>y iff for some case x is directly followed by y Causality: x y iff x>y and not y>x Parallel: x y iff x>y and y>x Choice: x#y iff not x>y and not y>x case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D A>B A>C B>C B>D C>B C> D E>F B C C B A B A C B D C D E F

22 Basic idea (1) x y x y

23 Basic idea (2) y x z x y, x z, and y z

24 Basic idea (3) y x z x y, x z, and y#z

25 Basic idea (4) x z y x z, y z, and x y

26 Basic idea (5) x z y x z, y z, and x#y

27 It is not that simple: Basic alpha algorithm Let W be a workflow log over T. a(w) is defined as follows. 1. T W = { t T $ s W t s}, 2. T I = { t T $ s W t = first(s) }, 3. T O = { t T $ s W t = last(s) }, 4. X W = { (A,B) A T W B T W " a A " b B a W b " a1,a2 A a 1 # W a 2 " b1,b2 B b 1 # W b 2 }, 5. Y W = { (A,B) X " (A,B ) X A A B B (A,B) = (A,B ) }, 6. P W = { p (A,B) (A,B) Y W } {i W,o W }, 7. F W = { (a,p (A,B) ) (A,B) Y W a A } { (p (A,B),b) (A,B) Y W b B } { (i W,t) t T I } { (t,o W ) t T O }, and 8. a(w) = (P W,T W,F W ). The alpha algorithm has been proven to be correct for a large class of free-choice nets.

28 W Example case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D A E B C F D a(w)

29 DEMO Alpha algorithm B L get review 1 get review X C M time-out 1 D time-out X K invite additional reviewer A get review 2 G H I invite reviewers E time-out 2 collect reviews decide accept J F reject get review 3 G time-out 3 48 cases 16 performers

30 Logging system Nlog NLog can process diagnostic messages emitted from any.net language (such as C# or Visual Basic), augment them with contextual information (such as date/time, severity, thread, process, environment enviroment), format them according to your preference and send them to one or more targets such as file or database.

31 Supported targets Files - single file or multiple, with automatic file naming and archival Event Log - local or remote Database - store your logs in databases supported by.net Network - using TCP, UDP, SOAP, MSMQ protocols Command-line console - including color coding of messages - you can receive s whenever application errors occur ASP.NET trace... and many more

32 Conclusions Introduction Concise assessment of reality needed for processes Preliminaries Data logging, Process Mining, Process Simulation Process mining with ProM Understanding process characteristics Process simulation Operational decision support Utilizing log info for simulation experiments Tools: YAWL, ProM & CPN Tools Payment example Conclusion 32

33 Log 33

34 Process model mined from log 34

35 What we can do? Inspecting and Cleaning an Event Log Mining the Control-Flow Perspective of a Process - Alpha algorithm Social networks 35

36 Questions? 36 36