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Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques, such as machine learning and data mining. Process mining seeks to find a connection between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications can include: analyzing treatment processes in hospitals, improving customer service processes in multinational companies, understanding browsing behavior of customers on a booking site, analyzing failures of a baggage handling system, or improving user interface of the X-ray machine. What all of these applications have in common is the need to relate dynamic behavior to process models. Not only does process mining provide a bridge between data mining and business process management, but it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.
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Process Mining: Data Science in ActionProcess Mining as a Tool to Find Out What People and Organizations Really Do
prof.dr.ir. Wil van der Aalstwww.processmining.org
PAGE 2
How many students will take my class
next year?
What is the
real curriculum?
When, where and why do
students drop out?
When, where and why do students
deviate?
Will I graduate or drop out?
When will I graduate?
Should I take this course if I want to graduate in two
years from now?
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It's the process, stupid!
(not the data, not the system, and not a particular decision)
The data scientist …
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Data Science Center Eindhoven (DSC/e)
PAGE 5
28 research groups are
involved
Data Science Center Eindhoven (DSC/e)
PAGE 67 research programs
Process Mining
www.olifantenpaadjes.nl
Process discovery
abcdacbdabcdabcefcbdacbefcbefbcdetc
Process Discovery
10
Conformance checking
acbdabdacbefbcdacbefebcdddfeabb
ConformanceChecking
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Let's play
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Play-Out
Play Out: A possible scenario
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a b d e gCase Activity Timestamp Resource432 register travel request (a) 18-3-2014:9.15 John432 get support from local manager (b) 18-3-2014:9.25 Mary432 check budget by finance (d) 19-3-2014:8.55 John432 decide (e) 19-3-2014:9.36 Sue432 accept request (g) 19-3-2014:9.48 Mary
Play Out: Another scenario
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a cd e hf b d e
Play Out: Process model allows for many more scenarios
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abdeg
abcefbdehadceg adbeh
acbefbdehacdefcdefbdeh
adcefcdefbdefbdegabdegadcehadbeh
acbefbdegacdefcdefbdehadcefcdefbdefbdeg
abdegadceh
adbeh
acbefbdegacdefcdefbdeh
adcefcdefbdefbdeg
abdeg
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Play-In
Play In:Simple process allowing for 4 traces
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abdeg adbeg
abdehadbeh
abdegadbeg
abdehadbehabdehabdeh
abdehadbeh adbeh
Play In: Process allowing for more traces
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abdeg
abcefbdehadcegadbeh
acbefbdehacdefcdefbdehadcefcdefbdefbdeg abdeg
adcehadbehacbefbdeg
acdefcdefbdeh
adcefcdefbdefbdeg
abdegadceh
adbehacbefbdeg
acdefcdefbdeh
adcefcdefbdefbdeg
abdeg
No modeling needed!
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No modeling needed!
Example Process Discovery(Dutch housing agency, 208 cases, 5987 events)
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Example process discovery for hospital(627 gynecological oncology patients, 24331 events)
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Process discovery algorithms (small selection)
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α algorithm
α++ algorithm
α# algorithm
language-based regions
state-based regionsgenetic mining
heuristic mining
hidden Markov models
neural networks
automata-based learning
stochastic task graphs
conformal process graph
mining block structures
multi-phase miningpartial-order based mining
fuzzy mining
LTL mining
ILP mining
distributed genetic mining
ETM genetic algorithmInductive Miner (infrequent)
Language based regions
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Region R = (X,Y,c) corresponding to place pR: X = {a1,a2,c1} = transitions producing a token for pR, Y = {b1,b2,c1} = transitions consuming a token from pR, and c is the initial marking of pR.
Basic idea: enough tokens should be present when consuming
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A place is feasible if it can be added without disabling any of thetraces in the event log.
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Replay
Replay
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a c d e g
Replay
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a c
check budget (d) is missing!
e g?
Alignments: Relating reality and model
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a c » e ga c d e g
check budget (d) did not happen but should have according to the model
Replay
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a c d e gh
reject request (h) is impossible
?
Alignments: Relating reality and model
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a c h d e ga c » d e g
reject request (h) happened but could not happen according to the model
Any trace in reality can be related to a path in the model
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a c e f d d b c e h
Any trace in reality can be related to a path in the model
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a c » e f d d b c e ha c d e f d » b » e h
check is missing
one check too many
cannot both be done
optimization problem using a cost fu
nction
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process model
event log
synchronous move
move on model only
move on log only
Conformance Checking (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)
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Replay with timestamps
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a9.15 c9.20 d9.35 e10.15 g11.30
9.159.20
9.35
10.15
11.30
5 55
20 40
75
Replay with timestamps for many traces
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frequencies of activities
frequencies of paths
durations of activities
waiting times and other delays between
activities
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• conformance checking to diagnose deviations• squeezing reality into the model to do model-based
analysis
Alignments are essential!
Example: BPI Challenge 2012(Dutch financial institute, doi:10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f)
PAGE 39Work of Arya Adriansyah (Replay project)
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Auditor's toolbox
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Business analyst's toolbox
Software
600+ plug-ins available covering the whole process mining spectrum
Disco
PAGE 45
Overview: Role of process models
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Play-Out
Play-In
Replay
decomposed/distributed process mining
data Big data
http://www.multivu.com/assets/58095/photos/Data-is-the-new-oil-infographic-Nigel-Holmes-2012-from-The-Human-Face-of-Big-Data-original.jpg
What if?
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there are more than 1000 different
activities?
there are more than 1.000.000
cases?
there are more than 100.000.000
events?
Decompose event log! vertical or horizontal
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sets of cases
sets of activities
Vertical distribution: Split cases
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sets of cases
Horizontal distribution
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sets of activities
{a,b,e,f,g}
{b,c,d,e}{a,b,e,f,g}
{b,c,d,e}
Horizontal distribution: The key idea
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projected on {a,b,e,f,g}
projected on {b,c,d,e}
Two foundational ways of spitting event data: horizontal or vertical
PAGE 55
Decomposing Conformance Checking
PAGE 57See "divide and conquer" framework by Eric Verbeek.
Example of a valid decomposition
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Log can be split in the same way!
Example of an alignment for observed trace a,b,c,d,e,c,d,g,f
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Etc.
a,b,c,d,e,c,d,g,f
Conformance checking can be decomposed !!!
• General result for any valid decomposition: Any event log or trace is perfectly fitting the overall model if and only if it is also fitting all the individual fragments
PAGE 60
for a
ny Petri
net
(not j
ust WF-n
ets)
for a
ny valid
decompsiti
on
Wil van der Aalst, Decomposing Petri nets for process mining: A generic approach. Distributed and Parallel Databases, Volume 31, Issue 4, pp 471-507, 2013
also for d
ata
Petri n
ets
Example (work with Jorge Munoz-Gama and Josep Carmona)
PAGE 61
prFm6
Decomposing Process Discovery
PAGE 62See "divide and conquer" framework by Eric Verbeek.
conclusion
Don't just check the temperature …
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X-ray your processes!
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www.processmining.org
www.win.tue.nl/ieeetfpm/
Process Mining: Data Science in Actionhttps://www.coursera.org/course/procmin
First Massive Open Online Course (MOOC) on Process Mining
