=Paper=
{{Paper
|id=Vol-1612/paper24
|storemode=property
|title=Characterizing Problem Gamblers in New Zealand: A Novel Expression of Process Cubes
|pdfUrl=https://ceur-ws.org/Vol-1612/paper24.pdf
|volume=Vol-1612
|authors=Suriadi Suriadi,Teo Susnjak,Agate M. Ponder-Sutton,Paul A. Watters,Christoph Schumacher
|dblpUrl=https://dblp.org/rec/conf/caise/SuriadiSPWS16
}}
==Characterizing Problem Gamblers in New Zealand: A Novel Expression of Process Cubes==
https://ceur-ws.org/Vol-1612/paper24.pdf
Characterizing Problem Gamblers in New
Zealand: A Novel Expression of Process Cubes
Suriadi Suriadi, Teo Susnjak, Agate M. Ponder-Sutton, Paul A. Watters,
Christoph Schumacher
Massey University, Albany, Auckland 0632, New Zealand
s.suriadi@massey.ac.nz
Abstract. This paper reports on the challenges and lessons learned from
our case study which uniquely integrates a mixture of process mining,
data mining, and confirmatory statistical techniques to explore and char-
acterize the variations in gambling behaviours exhibited by gamblers in
New Zealand. We demonstrate how we weaved techniques from these
three disciplines to understand the variety of behaviours exhibited by
gamblers, and to provide assurances of the correctness of our results.
This case study also demonstrates how such a combination of techniques
provides a rich set of tools to undertake an exploratory data analysis
project that is guided by the process cube concept.
Keywords: data mining, process mining, statistics, problem gamblers
1 Introduction
This paper reports on the challenges and lessons learned from our case study
where a mixture of process mining, data mining, and confirmatory statistical
techniques are applied to analyse a data set containing all bets recorded by a
gambling service provider in New Zealand (NZ). This study attempts to identify
and characterize various groups of gamblers (with a focus on problem gamblers)
directly from the data. The nature of this case study is exploratory: we do not
‘label’ our data with various classes of gamblers; rather, we attempt to learn
how many groups of gamblers can be discerned from the data.
The exploratory nature of this study calls for the use of unsupervised learning
techniques, such as k-means clustering [4] as the starting point of analysis. How-
ever, clustering analysis alone is not sufficient as it does not offer any insights into
the behaviour of gamblers seen in each cluster. Our case study demonstrates how
both process mining analysis, with its ability to extract detailed insights from
fine-grained and chronologically-arranged data [8, 11], and confirmatory statis-
tics, can be strategically weaved together with clustering analysis to not only
understand the variety of behaviours exhibited by gamblers, but also to evaluate
our results. Furthermore, we highlight how the combination of these techniques
can be used as an expression of the process cube concept [14].
The main contribution of this paper is on the reporting of challenges and
lessons learned in the application of these three classes of analysis techniques,
Copyright c by the paper’s authors. Copying permitted only for private and academic
purposes.
In: S. España, M. Ivanović, M. Savić (eds.): Proceedings of the CAiSE’16 Forum at
the 28th International Conference on Advanced Information Systems Engineering,
Ljubljana, Slovenia, 13-17.6.2016, published at http://ceur-ws.org
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
186 Suriadi Suriadi et al.
highlighting practical challenges that arise in analysing a relatively large size of
data with limited computational resources. Section 2 summarizes the approach
taken in this case study. Section 3 details the analyses performed at each stage
of the case study, along with the challenges and lessons learned. A discussion
about the related work is provided in Section 5, followed by the conclusion.
2 Approach
While we applied a range of techniques from multiple disciplines, our case study
employed the PM2 [16] approach because: (1) the starting point of our analysis
was an event log (the log used in our study consisted of betting events per ac-
count holder), of which process mining is designed for, and (2) the PM2 method-
ology is rather detailed in its guidance on each stage, and flexible enough to allow
the inclusion of other types of classical data mining techniques (e.g. clustering).
We also applied the concept of process cubes - a concept born from the
Online Analytical Processing (OLAP) domain. Using this approach, event logs
are organised into various cells based on multiple dimensions [14] and these cells
can then be merged or further split using typical OLAP operations, such as slice,
dice, roll-up, and drill-down. However, as suggested by van der Aalst [14], the
nature of event logs are such that special considerations are needed to perform
those operations. This paper shows some of the techniques that can be applied
to achieve those operations.
3 Case Study
This case study focuses on the data processing, data mining, clustering and
analysis, and evaluation stages of the PM2 methodology. The questions that the
stakeholder in our case study (an economist) wanted to address were: (RQ1)
how many groups of gamblers can be discerned from the data set, and (RQ2)
which users were problem gamblers from the data?
The data set was extracted from a NZ gambling provider. It contained in-
formation about all the bets placed in New Zealand from August 2006 to May
2014. The data set extracted was in event log format where each line of data
represents a betting event. Given the size of the raw data (80 GB in compressed
text format) and the limited computational resources at our disposal (three 8-
16GB/i5 Core workstations and one 32GB/i5 Core virtual machine), we focused
our analysis on gambling data from a 9-month window (Aug 2013 to May 2014).
Data Processing. The dataset consisted of 11,311,892 betting events executed
by 91,405 account-holders. The data was pre-processed because (1) our experi-
ences showed that existing implementations of many process mining techniques
do not scale well; many process mining case studies used event logs that are
significantly smaller (between a few thousand events to over 1 million events,
e.g. [2]); (2) the raw data is not in an event log format consumable by process
mining analyses.
Applying the concept of process cube [14] calls for slicing and dicing event
logs from multiple dimensions. As our case study is exploratory (without any
Characterizing Problem Gamblers in New Zealand 187
clear filtering dimensions), we applied an unsupervised k-means clustering [4]
technique to find the ‘natural separation’ of gamblers in the data set. Three
problem gambling features from the data were used as clustering criteria: bet
frequency, the ratio (in dollar) of winnnings over amount lost (win/loss dollar
ratio, and ratio of the number (count) of winning over lost bets (win/loss ra-
tio). Next, an event log was derived for each cluster using a gambler’s account
identifier as the case ID in the event log.For the activity field, the data was
preprocessed to approximate a gambler’s clawback behaviour: an activity name
was created as the concatenation of (1) the outcome of the previous bet (win
or loss) and (2) the amount of money placed in the next immediate bet as a
proportion of the amount of money placed in the previous bet: less than or equal
to the previous bet amount (<= 1), up to double the previous amount (1to2), or
more than double (>2). For example, loss <= 1 would mean that a user having
lost the previous bet, had subsequently placed another bet where the amount
bet was less than or equal to the previous bet. Through data pre-processing, we
obtained 7 manageable-size event logs for each cluster for deeper analysis. Each
cluster is colour-coded for referencing purposes.
Challenges. The large dataset was problematic given the lack of scalability of
the software tools used. Attempts to generate an XES file (i.e. the log format
expected by most process mining tools) presented challenges: we were limited
by the number of events that could be imported in Disco (www.fluxicon.com)
tool; we could have used the ProM Tool (www.prcessmining.org) to convert
the original CSV-formatted log into XES; however, the generated XES file would
have been substantially larger than the original CSV data, thus would not have
scaled well.Trace clustering algorithms, e.g. [17], could not have been applied
as they would have required the entire event log to be analysed (not feasible
with our resources). We addressed this by using k-means clustering [4] based on
aggregated features at case-level granularity. This required deciding the optimal
number of clusters (k) to be used. We combined the advice from the stakeholder
with the Within Sum of Squares (WSS) analysis which showed that the cohe-
siveness of the clusters converged optimally in the range of k=7 and k=8. We
therefore decided to use 7 clusters.
Mining and Analysis. Our analysis focused on extracting key markers for
problem gamblers across clusters using process mining techniques. Clawback be-
haviour is a key problem gambling psychological feature. We studied the claw-
back behaviours by generating process models for each cluster using the Disco
tool. Then, through visual comparisons (similar to Partington et al. [8] and
Suriadi et al. [12]), we extracted 9 distinct flow patterns in each cluster.
To assert statistical significance w.r.t the differences in the distribution (in
terms of frequency) of these 9 patterns across all clusters, Kruskal-Wallis tests
in conjunction with Dunn’s tests were performed. These tests showed that the
distribution of these patterns across the seven clusters were indeed significantly
different at the p-value of 0.05. In addition, from box-and-whisker plots, we also
observed that the distribution of these 9 patterns were quite distinct in the blue
and black clusters (see Figure 2, left diagram, for example).
188 Suriadi Suriadi et al.
Pattern 3
Pattern 4
Pattern 5
Fig. 1: A visualisation of clawback behaviours identified from the process models of
each cluster. Pattern 3 depicts a gambler who repeatedly lost bets and kept on betting
to recoup the loss; Pattern 4 captures a continuous feedback loop winning and then
losing a bet; Pattern 5 captures a clawback behaviour where the bet was double the
amount of previous bet following a lost bet.
Fig. 2: A comparison of the distribution of Pattern 3 and Bet Interval. Similar distri-
bution for other patterns is also observed (not shown above).
The time elapsed between bets (bet interval) was another marker of prob-
lem gamblers. The event interval analysis plug-in [13] was used with the ProM
Tool to extract the bet interval for each bet placed by every gambler in each
cluster. Additionally, the distribution of the median bet interval per gambler
was extracted. Next, Kruskal-Wallis and Dunn’s tests were applied to verify
if the distributions of the bet intervals across the 7 clusters were different at
p-value 0.05. The box-and-whisker graph (Fig. 2 - right) shows that the blue
and black clusters had significantly lower bet interval values compared to other
clusters. The exceptions being cyan and brown clusters, though they were dis-
counted since their actual bet numbers were very low (e.g. mostly only bet once).
We merged (‘roll-up’) the blue and black clusters as this was supported by the
pair-wise Dunn’s tests (which resulted in a p-value higher than 0.05, indicating
insignificant differences).
These analyses allowed us to group and rank the severity of gambling patterns
across the 7 clusters, resulting in 4 clusters that were statistically different (thus
Characterizing Problem Gamblers in New Zealand 189
addressing RQ1). Our ranking exercise suggested that the blue and black clusters
were more likely to contain problem gamblers, thus warranting further ‘drilling-
down’ analysis into these two clusters. We performed a second-level clustering
of these two clusters into another 7 clusters. For these second-level clusters,
our focus was to establish statistical differences in terms of gambling clawback
behaviour. We used the frequency distribution of those activities signifying loss-
related clawback behaviours: loss <=1, loss 1to2, and loss >=2.
Similar to our earlier approach, we used the Kruskal-Wallis and Dunn’s tests
results on our second-level clustering to provide a more refined severity ranking
of problem gamblers within the second-level clusters. Through this analysis,
we addressed RQ2: we narrowed down the most likely problem gamblers to two
clusters (from the second-level clusters) with a combined population size of 6,389.
Challenges Existing comparative analysis of processes [8, 9, 12] seemed to go
no further than visual analysis of process models, or through some forms of
multi-perspective visualisation techniques (e.g. [9]). However, such approaches
lack theoretical vigour. As explained above, we addressed this issue by applying
confirmatory statistical testing.Practically, we faced computational limitations
during use of the event interval analysis [13] and BPMN Miner [1] plug-ins.
We handled this by further dividing the event log of the largest cluster into
three sub-logs. However, division of large XML-formatted event log was also a
challenge as it contained more than 105 million XML elements/lines. We used
basic, powerful, Linux-based text processing utility, such as less, and sed to
cope with large XML files.
Evaluation. This case study relied heavily on clustering to identify various
classes of gamblers.The main challenge therefore was on how to be sure that
the clusters we obtained were indeed appropriate and meaningful. To do so, we
had to assert that (1) the population within each cluster shared some unique
characteristics and/or behaviours, and (2) the existence of those clusters was
within reasonable expectation of the stakeholders.
For the former, we applied classification analysis on the combined first- and
second-level clusters (12 clusters in total: 5 from the initial clusters and 7 clusters
from the second-level clusters). We used features that had not previously been
used to generate the k-means clustering, including the median bet interval values,
the frequency of the 9 gambling patterns, the frequency of clawback behaviour
activities, the total sum of money won, and the number of times a gambler won
a bet. These features when used with the random forest algorithm (with 300
trees and 5-fold cross validation) generated a classification accuracy of 84.9%
with a standard deviation of =/- 0.8%. These classification results, having used
independent features from the clustering process, provided support that the
clusters that were produced were relevant and meaningful.
The stakeholder also found the results to be reasonable. Some insights will
require further study, e.g. the dominance of Pattern 3 and Pattern 4 (which
suggested that gamblers had bet equal or less than their previous amount after
they had won or lost the previous bet) which contradicted currently-understood
190 Suriadi Suriadi et al.
risk-taking behaviour of problem gamblers where the expectation was for them
to increase the amount of money bet following a loss. While further analysis
may be required, there was an explanation for this: the dominance of Pattern
3 and Pattern 4 was evident when we took the all 12 clusters into account
(which could mean that such behaviour is discriminatory for deciding if a gambler
is a problem gambler or not). In the second level clustering, however, it was
the frequency of high-risk clawback behaviour, i.e. the loss 1to2 activity that
is statistically different across all second-level clusters (thus in line with the
expected behaviour).
4 Lessons Learned
During data processing stage, we found that it is effective to combine unsuper-
vised k-means clustering and aggregated case-level attributes to slice a large data
set was highlighted in this analysis. Doing so, we ‘deflated’ the data size from
over 11 million events to just over 94 thousand lines of data (each line represents
one gambler) that was further separated through with k-means clustering.
To estimate an optimal number of clusters that is conservative enough to
reduce the error probability of not finding enough clusters and fine-grained
enough to account for the complexity of the data, our case study shows that
it can be achieved by doubling the number of initially-suspected clusters and
cross-referencing it with WSS analysis.
We found the non-parametric assumptions of Kruskal-Wallis tests, paired
with Dunn’s tests, made them flexible and useful as, based on experience, data
used for process mining analyses is rarely normally distributed in practice.
Our case study also shows that using hierarchical clustering in combination
with confirmatory statistics is a suitable approach to apply the process cube
concept [14]. Hierarchical clustering based on aggregated case-level attributes
allowed us to ‘slice and dice’ event logs in an unsupervised manner, while con-
firmatory statistics informed us as to which groups of processes that should be
‘rolled-up’ (that is, those two groups whose p-value from the Dunn test indicates
statistical similarities) and which to ‘drill down’ into. This is an alternative ap-
proach to the currently-prevailing approach to slicing processes that is based on
control-flow perspective or simple attribute-based filtering [3, 8].
Finally, our case study demonstrates how one could apply classification anal-
ysis using features that were not used as input to the k-means clustering to
provide some meanings to the clusters that we have managed to extract from
the data. Recall that a detractor of k-means clustering is that it will produce
as many clusters as parametrised. This is an important insight as previous work
which used k-means clustering in a process mining case study [12] also applied
classification analysis to provide meaning to each cluster. However, it was done
using the same features as those used for clustering, which resulted in a very
high accuracy rate but adds nothing more to our understanding of each cluster.
From a practical perspective, the Inductive Miner [5] implementationed in
the ProM Tool is scalable to even the largest event log in our clusters. However,
the process model was less so: it produced models that exhibited ’flower model’
Characterizing Problem Gamblers in New Zealand 191
characteristics. By far, the Disco tool was still the most scalable tool in terms
of extracting process models even on an event log of over 4GB in size.
We question the need for XML-formatted data in process mining. Raw data
often come in a table-like format with fields whose meaning can be under-
stood through discussions with stakeholders, thus lessening the need for the
self-defining property of XML. XML files tend to be large and their processing
is resource intensive. Our original CSV-formatted data (< 1.5 GB) was ‘blown
up’ to over 5.8 GB after converting it to the XES format.
5 Related Work
Early process mining case studies [10,11,15] focused mainly on the application of
standard process mining techniques, such as process discovery and performance
analysis. Later process mining case studies [6–8] applied a combination of process
and data mining techniques. Our case study fits closer to the latter: we applied
a balanced amalgamation of process and data mining techniques. However, we
ventured further by also using a multi faceted approach involving process mining
and well-established confirmatory statistics.
This case study confirms some of the observations and experiences reported
in other process mining studies. For example, the use of clustering techniques
to split original event logs into smaller pieces was applied in our case study.
However, instead of using trace/sequence clustering as in [2,10], we used k-means
clustering [4] based on aggregated case-level features.
While an earlier case study [15] found that it was already feasible to conduct
process mining analysis using the ProM Tool, we found that the quality and the
robustness of the plug-ins in the ProM Tool to be highly variable with some
being robust and scalable enough to handle large data sets (e.g. the Inductive
Miner plug-in), while others tended to perform rather poorly (e.g. [1, 13]).
While our approach to process comparison was similar to other case studies,
e.g. [8, 9], ours went further by studying the distribution of observed differ-
ences, and asserted their statistical significance using well-established confirma-
tory statistics. Most importantly, this paper reported new lessons learned that
may be novel and helpful to other process mining practitioners.
6 Conclusion
We demonstrated how to apply a diverse and complementary set of techniques
from the domains of process mining, data mining, and confirmatory statistics,
as a unique expression of the process cube concept, to characterize and assert
differences in gambling behaviours exhibited by more than 94 thousand gam-
blers in New Zealand. Most importantly, this case study reported a number of
challenges and lessons learned that are novel and would likely be beneficial to
other process mining practitioners.
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