=Paper=
{{Paper
|id=Vol-2703/paperDC3
|storemode=property
|title=An Interactive Framework to Facilitate Behavioural Pattern Exploration in Event Data (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2703/paperDC3.pdf
|volume=Vol-2703
|authors=Leen Jooken
|dblpUrl=https://dblp.org/rec/conf/icpm/Jooken20
}}
==An Interactive Framework to Facilitate Behavioural Pattern Exploration in Event Data (Extended Abstract)==
https://ceur-ws.org/Vol-2703/paperDC3.pdf
An Interactive Framework to Facilitate Behavioural
Pattern Exploration in Event Data (Extended
Abstract)
Leen Jooken
Faculty of Business Economics
UHasselt - Hasselt University
Hasselt, Belgium
leen.jooken@uhasselt.be
Index Terms—event data behavioural analytics, pattern min- behaviour and very hard to interpret in their raw form. The fact
ing, visual analytics, exploratory data analysis that this data is so low-level and fine-grained is what makes
analyzing it so challenging. In order to analyze the behaviour,
I. P ROBLEM S TATEMENT AND P OSITIONING WITH the low-level event data first needs to be transformed into
R EGARD TO THE S TATE OF THE A RT higher-level behavioural patterns.
A lot of data that is collected by systems can be categorized
This is where our research problem arises: because this
or treated as event data. Event data is data that describes
data is more complex than conventional transactional data,
events in which the state of the subject to which the event
traditional data analysis techniques are not always appropriate
relates, changes. This subject can be a person, an object or
to extract these insights. There is a wide variety of pattern
a process. Data must meet three important characteristics in
mining techniques available, and although some could be used
order to be categorized as event data: (1) an event happens
on (preprocessed) event data, they were not developed with
instantaneously, therefore we do not consider begin or end
event data in mind and often rely upon a set of assumptions
timestamps, nor the duration, (2) it is possible to (partially)
which are not universal for event data, for example: process
order events in time, (3) an event describes a change of state or
mining [2] expects each event to be related to an instance of the
context. The availability of this type of data has witnessed an
process. However, exploring event data is not only challenging
increase because of two characteristics of this digital age: (1)
because this data is generally interrelated in a multidimen-
the capturing has become easier, and (2) it has become easier
sional manner, but also because of the large amounts of data,
to deal with huge amounts of data as a result of cheaper data
which creates challenges for current techniques both in terms
storage opportunities, improved database technology and the
of feasibility and in terms of interpretability of the results.
availability of big data technology.
When the data consists of a lot of observations and many
Event data can provide a different type of insights than
different types of items, these techniques tend to produce a
traditional ‘rectangular’ data, because of how the information
long list of possibly interesting patterns, which is difficult to
is stored. Traditional data typically describes some type of
act on by a user who wants to explore the data. Furthermore,
business object by means of a set of attributes [1], which
insights can only be learned from patterns that are meaningful
means that it only provides a snapshot of the state of that
with respect to the practitioner’s use case. Hence it is crucial
business object. It cannot provide insights into the exact
that understanding of behavioural structures, semantics and
behaviour that led to that state. The ability to learn behavioural
dynamics of the event data is incorporated in the pattern
patterns is what makes event data so interesting. After all,
discovery and analysis stage [1].
understanding, predicting and correctly reacting to changes
in behaviour are crucial business capabilities. Furthermore, So we identified a set of needs related to exploring be-
including the behaviour perspective to complement traditional havioural patterns in event data. Firstly there is a need for
analysis can help you build a multi-dimensional viewpoint to clarity on which techniques can be used, and how they can be
better solve certain business problems [1]. used, to mine for behavioural patterns in event data. Secondly
Behaviour, however, is an abstract concept that consists of there is a need to effectively present the found patterns in
many attributes and properties, for instance: the subject that a way that is intuitively comprehensible to the end user.
carries out the action, the object on which a behaviour is Lastly there is a need to incorporate domain knowledge in an
imposed, the context in which behaviour manifests itself, the interactive way while exploring the data to ensure the quality,
goal of the behaviour and the impact on the object or context correctness and relevance of the found patterns with respect
[1]. Events, on the other hand, are the raw observation of to the practitioner’s use case.
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
II. P ROPOSED S OLUTION easy exploration of behavioural patterns in the practitioner’s
The final envisioned solution is an exploration framework event data. This artifact will be designed and implemented
to guide the practitioner in interactively exploring behavioural following a Design Science approach [5], to safeguard that it
patterns in their event data. The framework must meet the will meet the predetermined requirements and the actual needs
following requirements: (1) it should take raw event data as of the initial problem. Next, available visualization techniques
an input, (2) it should uncover insights in behaviour of people, need to be explored and put to the test, in order to present the
objects or systems, (3) it should enable interactive exploration, resulting patterns in a intuitively comprehensible way to the
and (4) it must be possible to incorporate domain knowledge. end user.
The development of this framework is an application of the C. Work Package 3
behaviour informatics approach [1]. The scientific field of Be- The focus of this last work package lies on incorporating
haviour Informatics focuses on the development of methodolo- expert knowledge into the analysis process. We plan to do
gies, techniques and practical tools for representing, modeling, this in an interactive way based on a visual analytics approach
analyzing, understanding and utilizing behaviour [1]. [6]. Visual analytics is described as the “science of analytical
The PhD Project is divided into three work packages. Each reasoning facilitated by interactive visual interfaces” [7]. At
work package meets one of the needs we identified in Sect. I, its base it is an iterative process in which automatic discovery
with the following deliverables: and visualization gets refined by feedback from the end user
1) a) a conceptual translation from the field of behav- [6]–[8]. The interactivity of visual analytics makes it extremely
ioral informatics [1] to a set of analytical chal- well suited to be used for exploratory analysis, and places it
lenges that describe different types of behavioural in the broader area of hybrid intelligence, aimed at creating
insights that can be learned synergies by letting machines and humans work together [9].
b) an overview on how different pattern mining tech- IV. R ESULT VALIDATION
niques, or underlying concepts of these techniques,
Validation of the results of the different work packages is
can address these challenges and be used to answer
a great challenge. To validate our framework’s ability to find
related research questions
meaningful patterns and its user experience, one or more case
2) a framework that incorporates the most useful concepts
studies will be set up in which a practitioner can evaluate the
from these pattern mining algorithms to facilitate the
performance of the framework on their data. However, it is
exploration of event data from a behavioural analytics
important to keep in mind that this evaluation is subjective and
perspective and visualizes the found patterns in a way
highly influenced by the end result the practitioner already has
that is intuitively comprehensible to the end user
in mind. A strategy needs to be worked out to minimize this
3) a way in which the end user can interact with the frame-
influence and to evaluate not only patterns that are expected
work and the feedback is reentered into the iterative
to be found but also patterns that are unusual or overlooked.
analysis process
A combination of a structured interview of the practitioner’s
III. R ESEARCH M ETHODOLOGY expectations beforehand, the capturing of the framework’s user
experience and an in-dept interview afterwards, seems to be
A. Work Package 1
the best approach.
Since behaviour is an abstract concept that consists of many
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