Business Intelligence & Process Modelling
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1 Business Intelligence & Process Modelling Frank Takes Universiteit Leiden Lecture 9 Process Modelling & BPMN & Tooling BIPM Lecture 9 Process Modelling & BPMN & Tooling 1 / 47
2 Recap Business Intelligence: anything that aims at providing actionable information that can be used to support business decision making Business Intelligence Visual Analytics Descriptive Analytics Predictive Analytics Process Modelling (April and May) BIPM Lecture 9 Process Modelling & BPMN & Tooling 2 / 47
3 Process Mining (recap) BIPM Lecture 9 Process Modelling & BPMN & Tooling 3 / 47
4 Petri Nets (recap) BIPM Lecture 9 Process Modelling & BPMN & Tooling 4 / 47
5 Simplified event log (recap) Case ID Trace 1 a, b, d, e, h 2 a, d, c, e, g 3 a, c, d, e, f, b, d, e, g 4 a, d, b, e, h 5 a, c, d, e, f, d, c, e, f, c, d, e, h 6 a, c, d, e, g Table : Simplified event log of a support desk handling customer compensations (a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, h = reject request) BIPM Lecture 9 Process Modelling & BPMN & Tooling 5 / 47
6 Simplified event log (recap) Case ID Trace 1 a, b, d, e, h 2 a, d, c, e, g 3 a, c, d, e, f, b, d, e, g 4 a, d, b, e, h 5 a, c, d, e, f, d, c, e, f, c, d, e, h 6 a, c, d, e, g Table : Simplified event log of a support desk handling customer compensations (a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, h = reject request) In short: { a, b, d, e, h, a, d, c, e, g, a, c, d, e, f, b, d, e, g, a, d, b, e, h, a, c, d, e, f, d, c, e, f, c, d, e, h, a, c, d, e, g } BIPM Lecture 9 Process Modelling & BPMN & Tooling 5 / 47
7 Labeled Petri Nets (recap) Petri net N = (P, T, F, A, l) P is a finite set of places T is a finite set of transitions F (P T ) (T P) is a finite set of directed arcs called the flow relation A is a set of activity labels l : T A is a labeling function BIPM Lecture 9 Process Modelling & BPMN & Tooling 6 / 47
8 BPMN BIPM Lecture 9 Process Modelling & BPMN & Tooling 7 / 47
9 BPMN Business Process Modelling Notation (BPMN) S. White, Business Process Modelling Notation v1.0, Business Process Management Initiative, Maintained by BPMI and later the OMG consortium Now at version (2013) 532 pages of specification: BIPM Lecture 9 Process Modelling & BPMN & Tooling 8 / 47
10 BPMN Example BIPM Lecture 9 Process Modelling & BPMN & Tooling 9 / 47
11 Corresponding Petri net BIPM Lecture 9 Process Modelling & BPMN & Tooling 10 / 47
12 Simple BPMN gates BIPM Lecture 9 Process Modelling & BPMN & Tooling 11 / 47
13 BPMN Example Figure : Regular vs. dotted arcs BIPM Lecture 9 Process Modelling & BPMN & Tooling 12 / 47
14 Feature: subprocesses Complex processes can be hard to understand In large schemas, errors are easily overlooked Solution: decomposition (encapsulation) Sub-processes in an activity Sub-process can be re-used later on BIPM Lecture 9 Process Modelling & BPMN & Tooling 13 / 47
15 Feature: subprocesses BIPM Lecture 9 Process Modelling & BPMN & Tooling 14 / 47
16 Feature: multiple instances BIPM Lecture 9 Process Modelling & BPMN & Tooling 15 / 47
17 Feature: multiple instances BIPM Lecture 9 Process Modelling & BPMN & Tooling 16 / 47
18 Feature: loops BIPM Lecture 9 Process Modelling & BPMN & Tooling 17 / 47
19 Featured BPMN gates Figure : Loops and multiple instances, plain and as a subprocess BIPM Lecture 9 Process Modelling & BPMN & Tooling 18 / 47
20 Example exam questions (so far) Given an informal process description, draw a Petri net or BPMN diagram Given a Petri net, draw the reachability graph Given a Petri net, draw the BPMN diagram (or vice versa) Play an event log on a Petri net or an BPMN diagram Given a Petri net, determine if it adheres to certain properties (e.g., deadlock-free, safe, etc.) BIPM Lecture 9 Process Modelling & BPMN & Tooling 19 / 47
21 Event logs & Visualization BIPM Lecture 9 Process Modelling & BPMN & Tooling 20 / 47
22 Model-based analysis Limitations of model-based analysis: Verification and performance analysis heavily rely on the availability of high quality models When the models and reality have little in common, model-based analysis does not make much sense There is often a lack of alignment between hand-made models and reality Process mining aims to address these problems by establishing a direct connection between the models and actual low-level event data about the process. Process discovery techniques allow for viewing the same reality from different angles and at different levels of abstraction. BIPM Lecture 9 Process Modelling & BPMN & Tooling 21 / 47
23 Event logs Case ID Event ID dd-mm-yyyy:hh.mm Activity Resource Costs :11.02 register request Pete :10.06 examine thoroughly Sue :15.12 check ticket Mike :11.18 decide Sara :14.24 reject request Pete :11.32 register request Mike :12.12 check ticket Mike :14.16 examine casually Sean :11.22 decide Sara :12.05 pay compensation Ellen :14.32 register request Pete :15.06 examine casually Mike :16.34 check ticket Ellen :09.18 decide Sara :12.18 reinitiate request Sara :13.06 examine thoroughly Sean :11.43 check ticket Pete :09.55 decide Sara :10.45 pay compensation Ellen :15.02 register request Pete :12.06 check ticket Mike :14.43 examine thoroughly Sean :12.02 decide Sara :15.44 reject request Ellen Table : Event logs of a helpdesk handling customer compensations BIPM Lecture 9 Process Modelling & BPMN & Tooling 22 / 47
24 Event logs A process consists of cases A case consists of events such that each event relates to precisely one case Events within a case are ordered. Events can have attributes, e.g.: activity, time, costs and resource BIPM Lecture 9 Process Modelling & BPMN & Tooling 23 / 47
25 Event log attributes BIPM Lecture 9 Process Modelling & BPMN & Tooling 24 / 47
26 Resource-based Social Network BIPM Lecture 9 Process Modelling & BPMN & Tooling 25 / 47
27 Extracting event logs From data to event log (ETL-like process) Not just a syntactical issue Different views are possible Important: Selecting the right instance notion Ordering of events Selection of events Tool support: e.g., XESame BIPM Lecture 9 Process Modelling & BPMN & Tooling 26 / 47
28 Challenges when extracting event logs Correlation: events in an event log are grouped per case. Challenging, as it requires event correlation, i.e., events need to be related to each other Timestamps: events need to be ordered per case. Typical problems: only dates, different clocks, delayed logging Snapshots: cases may have a lifetime extending beyond the recorded period, e.g., a case was started before the beginning of the event log Scoping: how to decide which tables to incorporate? BIPM Lecture 9 Process Modelling & BPMN & Tooling 27 / 47
29 XES XES (extensible Event Stream) Adopted by the IEEE Task Force on Process Mining Predecessors: MXML and SA-MXML. The format is supported by tools such as ProM BIPM Lecture 9 Process Modelling & BPMN & Tooling 28 / 47
30 XES specification BIPM Lecture 9 Process Modelling & BPMN & Tooling 29 / 47
31 XES header BIPM Lecture 9 Process Modelling & BPMN & Tooling 30 / 47
32 XES events BIPM Lecture 9 Process Modelling & BPMN & Tooling 31 / 47
33 Dotted Chart Analysis Visualization of cases Colors indicate the same event within a case Horizontal axis indicates time Cases along the vertical axis Absolute or relative time Attributes can be added as labels BIPM Lecture 9 Process Modelling & BPMN & Tooling 32 / 47
34 Dotted charts BIPM Lecture 9 Process Modelling & BPMN & Tooling 33 / 47
35 Dotted charts (absolute) BIPM Lecture 9 Process Modelling & BPMN & Tooling 34 / 47
36 Dotted charts (absolute) BIPM Lecture 9 Process Modelling & BPMN & Tooling 35 / 47
37 Dotted charts (relative) BIPM Lecture 9 Process Modelling & BPMN & Tooling 36 / 47
38 Tool support BIPM Lecture 9 Process Modelling & BPMN & Tooling 37 / 47
39 Tool support BIPM Lecture 9 Process Modelling & BPMN & Tooling 38 / 47
40 ProM BIPM Lecture 9 Process Modelling & BPMN & Tooling 39 / 47
41 ProM visualization BIPM Lecture 9 Process Modelling & BPMN & Tooling 40 / 47
42 ProM exploration BIPM Lecture 9 Process Modelling & BPMN & Tooling 41 / 47
43 ProM summarization BIPM Lecture 9 Process Modelling & BPMN & Tooling 42 / 47
44 ProM mining BIPM Lecture 9 Process Modelling & BPMN & Tooling 43 / 47
45 ProM plugins BIPM Lecture 9 Process Modelling & BPMN & Tooling 44 / 47
46 Assignment 3 Goal: use a tool (ProM) to perform process mining Real-world event data from a financial institution Visualize the data Extract a sensible model Compare algorithms for doing so Answer some business-related questions Focus on high quality report BIPM Lecture 9 Process Modelling & BPMN & Tooling 45 / 47
47 Lab session April 20 Practice with Petri nets on paper Snellius room 174 BIPM Lecture 9 Process Modelling & BPMN & Tooling 46 / 47
48 Credits Lecture partially based on (slides of the (previous edition of the)) course book: W. van der Aalst, Process Mining: Data Science in Action, 2nd edition, Springer, BIPM Lecture 9 Process Modelling & BPMN & Tooling 47 / 47
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