=Paper=
{{Paper
|id=Vol-2716/paper15
|storemode=property
|title=Identifying Cohorts that Differ in their Behaviour: Tool Support
|pdfUrl=https://ceur-ws.org/Vol-2716/paper15.pdf
|volume=Vol-2716
|authors=Sander J.J. Leemans,Shiva Shabaninejad,Kanika Goel,Hassan Khosravi,Shazia Sadiq,Moe T. Wynn
|dblpUrl=https://dblp.org/rec/conf/er/LeemansS0KSW20a
}}
==Identifying Cohorts that Differ in their Behaviour: Tool Support==
https://ceur-ws.org/Vol-2716/paper15.pdf
Judith Michael, Victoria Torres (eds.): ER Forum, Demo and Posters 2020 163
Identifying Cohorts that Differ in their
Behaviour: Tool Support
Sander J. J. Leemans1 , Shiva Shabaninejad2 , Kanika Goel1 , Hassan Khosravi2 ,
Shazia Sadiq2 , and Moe T. Wynn1
1
Queensland University of Technology, Brisbane, Australia
2
University of Queensland, Brisbane, Australia
{s.leemans,k.goel,m.wynn}@qut.edu.au, {s.shabaninejad,
h.khosravi}@uq.edu.au, shazia@itee.uq.edu.au
Abstract. Process mining is a specialised form of data analytics that
aims to provide data-driven improvement recommendations, derived from
event logs. These event logs contain information about the execution of
real-world processes, which may be complex. Cohort identification rec-
ommends drill-down filters for process mining, based on differences in
process. In this paper, we describe its integration in three process min-
ing tools: as a stand-alone ProM plug-in, as part of the visual Miner and
(planned) as part of Course Insights.
Keywords: Process Mining · Feature Selection · Filter Recommenda-
tion · Stochastic Comparative Process Mining
1 Introduction
Process mining, a specialised form of data analytics, provides techniques
using which analysts can extract insights from recorded process behaviour in
event logs. The insights are used to provide data-driven recommendations to
improve business operations. Many real-life processes are complex in nature,
and studying their process models is challenging [1]. Process mining techniques
to deal with this complexity include filtering, slicing and dicing, and process
cubes.
Cohort identification aims to identify and recommend sub-sets of the traces
in the log (cohorts). These cohorts are defined by attributes of traces (e.g. “claim
amount”, “gender” or “country”), and cohort identification recommends the at-
tributes and values such that the traces that have the attribute and value (co-
hort) differ as much as possible from the traces that do not have the attribute
or have a different value (anti-cohort) in terms of the process that is being fol-
lowed, which includes the order of steps taken for traces, and how often different
sequences appeared in the (anti-)cohort. The difference between the cohort and
the anti-cohort is expressed as a distance measure [4], where 1 means that their
processes are completely different (i.e. no activity appears in both), and 0 means
that their processes are no more different than a random division of the combined
log.
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
164 Sander J. J. Leemans et al.
That is, cohort identification finds groups of cases in event logs that follow
a process that is different than the process of other groups. For instance, con-
sider the following log consisting of 1000 cases of customers purchasing online
and registering for an account, in which each trace is annotated with whether
the customer was a Silver or Gold customer, from an East or West branch:
[hregister, purchasei200 100
SE , hregister, purchaseiSW , hregister, purchaseiGE ,
50
hregister, purchasei100 GW , hpurchase, registeri 100
SE , hpurchase, registeri 50
SW ,
hpurchase, registeri200 GE , hpurchase, registeri100
GW ]. In this log, Gold customers
150
executed the trace variant hregister, purchasei 450 = 13 times, while for the other
450
customers this is 1000 = 0.45 times. Thus, the likelihood that Gold customers
first register is lower than for other customers. Cohort identification would assess
this for all potential combinations of attributes and values, and provide a ranked
list based on a quantification of such differences.
Cohort identification has similar goals as other process comparison techniques
such as trace clustering [10], concept drift detection [7], and event attribute
clustering [2], however provides better explainable results: the output is a list of
attribute-value pairs that denote sub-logs of interest. The details of our cohort
identification technique are described in [6].
In this paper, we describe the in-
tegration of cohort identification into
three existing open source data intelli-
gence tools: as a plug-in of the ProM
framework (Section 2), as an exten-
sion of the process mining tool visual
Miner (Section 3), and as an extension
of the learning analytics dashboard
Course Insights (Section 4). The first
tool demonstrates the use of cohort
identification as a stand alone tech-
nique, the second tool depicts the use
of cohort identification in conjunction
with other process mining techniques,
and the third tool illustrates the em-
bedding of cohort identification in a
learning analytics context. A screen- Fig. 1: Cohort Identification in ProM.
cast is available at3 .
2 Stand-alone Plug-in of ProM
The ProM framework [3] is a state-of-art open-source process mining frame-
work aimed at practitioners and academics. Cohort identification has been imple-
mented as a plug-in of ProM enabling practitioners and academics to access the
technique. Upon starting with as input an event log, two parameters can be set:
the attribute that determines the activity being executed, and the maximum
number of trace attributes that is exhaustively considered. Upon completion,
3
https://vimeo.com/442323972
Identifying Cohorts that Differ in their Behaviour: Tool Support 165
Fig. 2: The visual Miner (left) and the integration of cohort identification (right).
first a diversified set of cohorts is shown (with attribute, value, cohort size, and
distance between the cohort and anti-cohort). Figure 1 shows a screenshot.
Implementation. The implementation is flexible, as it provides extension points
to (1) elicit attribute value ranges, (2) measure distance between events, traces
and logs, and (3) truncate cohorts based on size or other aspects. Furthermore,
the implementation is multithreaded and for pruning stores a Map entry, an int[]
and an AtomicBoolean for each attribute value range combination.
Maturity & How to Access. Cohort identification is open source and is part
of the ProM 6.10 release; see4 . The plug-in has been successfully applied in three
case studies [6].
3 visual Miner
The visual Miner (vM) [5] is an existing process mining tool that enables end-
users to combine advanced academic process mining techniques in an industry-
capable and user-friendly package. The input of vM is an event log. First, vM
applies a process discovery technique to the log to obtain a process model. Sec-
ond, vM applies a conformance checking technique and visualises the differences
between log and the discovered model. Third, it computes detailed frequency
and performance information, and visualises this on the model, amongst others
using animation. Fourth, it allows the user to drill down and focus on parts of
the event log that are of interest, by applying one of several filters. Settings to
any of these techniques and filters can be changed at any time, and vM will
update and redo the necessary steps automatically [5]. A screenshot is shown in
Figure 2; for a complete overview of vM’s features, please refer to5 .
Cohort Identification (new). While the vM makes it easy to drill down into
parts of the log or process of particular interest, it was up to the user to manually
analyse the visualisation available to discover potentially interesting parts.
4
http://promtools.org
5
http://leemans.ch/publications/ivmProM6.10.pdf
166 Sander J. J. Leemans et al.
Selected Attributes Minimum Coverage: Recommendations
Program, Residential Status 20% 2
Coverage Distance
Program = Tourism 45 % 50.06%
Program = Engineering and Residential Status = Domestic 45 % 25.80%
CLOSE APPLY FILTER
Fig. 3: Cohort identification in Course Insights.
Cohort identification suggests filters on attributes on the traces in an event
log, such that applying the filter leads to the largest differences in process. Fig-
ure 2 shows a screenshot of the integration of cohort identification in vM: the
cohorts are computed automatically in the background, and the result is shown
to the user. The first column shows the trace attribute of the cohort, the second
column the values of the attribute that are in the cohort, the third column the
number of traces in the cohort, while the last column shows the distance between
the cohort and the anti-cohort. Using a click (for the cohort) or shift+click (for
the anti-cohort) one can quickly filter down the event log to the corresponding
traces, in order to study the differences in process in more detail. The identified
cohorts can be exported to an Excel document for further analysis. Embedding
cohort identification in vM makes it easy for end-users to conduct detailed pro-
cess mining analysis using one plug-in.
Maturity & How to Access. vM has been applied to many process mining
projects by industry partners and academics (see 6 for an overview). Cohort iden-
tification has been added in April 2020 and has been successfully applied in three
case studies [6]. Visual Miner is open source, part of the ProM framework [3]
and can be downloaded from6 .
4 Course Insights
Course Insights is an instructor-facing learning analytics dashboard, devel-
oped at the University of Queensland, that empowers course coordinators to
gain insights and act on student data to enhance student learning and experi-
ence across the course life-cycle at scale. It collates student data from a variety
of learning systems and sources and displays it to instructors all in one sim-
ple and easy to use interface. An essential element is its comparative analysis
functionality, which enables course coordinators to use filters to compare and
contrast different student groups based on their demographics, enrolment, en-
gagement and performance data. An observational study that analysed how the
filters were used by 71 staff members found that commonly only a small subset
of the features was used and filters were rarely applied on top of one another [8].
To overcome this challenge, we are implementing cohort identification in Course
Insights to recommend insightful filters to instructors [9].
6
http://visualminer.org
Identifying Cohorts that Differ in their Behaviour: Tool Support 167
Cohort Identification (planned). Figure 3 illustrates the proposed presenta-
tion of filter recommendations to instructors, including the attributes and values,
coverage (fraction of students covered by the filter), and distance (insightfullness
of the filter).
Maturity & How to Access. A case study based on data from a course with
875 students, with high demographic and educational diversity has explored the
potential benefits of applying cohort identification to Course Insights [9]. The
cohort identification is planned to be implemented in Course Insights, which
can be accessed via7 . A remaining challenge is to make Course Insights fully
process aware: with cohort identification, users can drill down into sub-groups
with differences in their process, however more support to study these differences
is necessary.
5 Conclusion
In this paper, we described how cohort identification, which recommends
trace-attribute-based filters to maximise the differences between processes, is
implemented as a stand-alone ProM plug-in, is integrated in the visual Miner,
and is being integrated in Course Insights. The technique filters sub-logs of
traces (cohorts) defined by trace attribute value ranges (features) to compare
behavioural differences in cohorts or to drill down into a particular cohort. The
technique can be used to understand the differences between two cohorts and
answer questions related to a particular cohort. Cohort identification can be
applied in reasonable time to event logs. In the future, we intend to focus imple-
menting process infrastructure in Course Insights and on automated comparison
techniques to compare the identified cohorts.
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7
https://analytics.itali.uq.edu.au/dev/insights