=Paper=
{{Paper
|id=Vol-2575/paper2
|storemode=property
|title=Towards a Framework for Context-Aware Resource Behaviour Analysis
|pdfUrl=https://ceur-ws.org/Vol-2575/paper2.pdf
|volume=Vol-2575
|authors=Maximilian Völker,Luise Pufahl
|dblpUrl=https://dblp.org/rec/conf/zeus/VolkerP20
}}
==Towards a Framework for Context-Aware Resource Behaviour Analysis==
https://ceur-ws.org/Vol-2575/paper2.pdf
Towards a Framework for Context-Aware
Resource Behaviour Analysis
Maximilian Völker1 and Luise Pufahl2
1
Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
maximilian.voelker@student.hpi.de
2
Software and Business Engineering, Technische Universitaet Berlin, Berlin, Germany
luise.pufahl@tu-berlin.de
Abstract. For the successful and efficient execution of business processes,
resources are essential. However, it is difficult to predict or plan executions
appropriately, as the behaviour of resources, especially human workers,
highly varies depending on the individual and the context. Although there
are several metrics to describe resource behaviour in research, the reasons
for their behaviour and the influence of the environment, like the workload,
have been less explored. Extracting resource-related metrics from event
logs and analysing them for possible relationships opens the opportunity
to understand resource behaviour and improve working conditions. In this
work, a framework for analysing correlations between resource behaviour
and environment is motivated and briefly sketched.
Keywords: Resource Behaviour, Business Processes, Process Mining
1 Introduction
Resources play a crucial role for the correct execution of business processes [2]
and their behaviour heavily affects the overall performance of the processes they
are involved in [4]. But unlike machines, human resources do not show constant
behaviour at work: their working speed varies, they might batch work or are only
available part-time [9]. In addition, humans have different preferences regarding
their work-items or co-workers, which is reflected in their behaviour [1].
From a temporal perspective, workers most likely change their behaviour
and preferences over time due to personal development or adjustments to a new
environment or circumstances. In the area of work psychology, for example, the
arousal, i.e. stress, of workers is recognised to be related to their performance,
known as the Yerkes-Dodson law [10].
In the context of business process technology, the behaviour and decisions of
resources, as well as process-related circumstances, are incidentally captured in
event logs. Metrics like workload, processing speed, waiting times and preferences
in terms of task selection can, for example, be derived from the event log [1, 6],
provided resource information is available for tasks. However, even though many
researchers state that human resources and their behaviour greatly affect overall
J. Manner, S. Haarmann, S. Kolb, O. Kopp (Eds.): 12th ZEUS Workshop, ZEUS 2020, Potsdam,
Germany, 20-21 February 2020, published at http://ceur-ws.org/Vol-2575
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
6 Maximilian Völker and Luise Pufahl
process performance, there is only little research on mining and, more importantly,
understanding the behaviour of human resources in the context of the process
execution [7].
In the remainder of the paper, related work regarding resource metrics is
presented in Sect. 2. Section 3 introduces the concept for a new framework for
resource behaviour analysis.
2 Foundations
So far, several metrics to measure the behaviour and performance of human
resources have been proposed. For example, Swennen et al. [8] introduce the
notions of Resource Frequency, Resource Involvement, and Resource Specialisation,
indicating how active resources are and in how many cases they participate. In
terms of resource behaviour, Suriadi et al. [7] describe how to extract the queuing
discipline (bounded to FIFO, LIFO or Priority) that resources show and Martin
et al. [3] propose an approach for detecting batching in resource behaviour. In
addition, Pika et al. [6] provide examples for metrics in the following categories:
Skills, Utilisation, Preference, Productivity and Collaboration.
Although several papers describe different metrics for resource behaviour,
only a few consider them in context. But former research already showed that
correlations between resource-related metrics can be found in process logs: Naka-
tumba et al. [4] confirmed the Yerkes-Dodson law by extracting the workload
and processing times from process logs and performing a regression analysis.
Another exception in the context of correlating resource metrics is the com-
prehensive framework developed by Pika et al. [6]: They present an approach for
extracting time series of Resource Behaviour Indicators (RBI) from event logs
using SQL-like queries. In later work, this framework was extended to include
the aspect of the connections between resource behaviour and different outcomes
by including a regression analysis of their RBIs [5]. However, there are some
points for improvements, e.g. regarding the scope and complexity of the metrics
available for analysis and the reuse or export of calculations.
3 A Framework for Context-Aware Resource Behaviour
Analysis
Due to the limitations of existing work, we plan to develop a framework for
context-aware resource analysis with a three-step approach as shown in Fig. 1.
Metric Selection For examining the behaviour of resources not only the analysis-
part plays an important role but also the metrics themselves must be considered
in detail beforehand. Metrics are measurements used to quantify performance
aspects and can be calculated from data for a point in time or time spans. In
the context of resources and processes, examples are the number of activities a
resource is working on, or how many activities are assigned to a resource but
have not yet been started.
Towards a Framework for Context-Aware Resource Behaviour Analysis 7
The framework will include, but not be
limited to, a collection of resource-related
metrics from the literature. To guide
the selection of metrics for analysis, we
will furthermore classify them into envi-
ronmental metrics (influencing behaviour)
and behavioural metrics (expressing be-
haviour), which should support more tar-
geted and meaningful analyses. Additionally,
the framework will not be limited to directly
Fig. 1. Framework Steps resource-related metrics, since case-related
or event-related information, such as the
case duration or the time of day, may also
have an effect on the behaviour or decisions of resources and will therefore be
available for analysis as well.
Each metric comes with its own extraction logic and imposes, often implic-
itly, certain requirements on the data set, such as certain attributes or meta-
information needed for computation. However, requirements for the process log
are often not mentioned in literature. Besides these demands, metrics can also
have different calculation techniques that differ in their requirements and quality
based on assumptions. The processing time, for example, could be extracted
by taking the timestamps of start and end events into account, or the required
time is specified directly in the log as an attribute. Some logs may even lack this
information, but an estimation of processing times could still be made, e.g. by
considering the subsequent event and assuming waiting times. The framework
for computing such metrics should therefore be aware of these variations and
prerequisites and be extendable with new metrics and calculation techniques.
This allows for a flexible and general application on a wide range of event log
variants.
Correlation Analyses After the metric-computation, correlation analysis can be
used to determine if there is a relationship between them. By automatically
executing the analyses for selected metrics, the framework is able to reveal
interesting insights for further manual investigation. For this, the separation
into environmental metrics and behavioural metrics might help to detect more
relevant results, as it indicates the direction of possible causalities.
To enable future research based on resource behaviour, the data and time
series calculated by the framework should be exportable, e.g. by enriching the
process log with new data and attributes, such as the workload or the current
work prioritisation pattern. This would facilitate further processing of the data
series, e.g. with techniques from the field of machine learning. The resulting
models could be used not only to anticipate the resources’ reactions to impending
environmental changes but also to achieve a more powerful and realistic process
simulation regarding resources.
8 Maximilian Völker and Luise Pufahl
Visualisation The visualisation component plays an important role as it is used
to communicate the outcome of the analysis. On the one hand, it should include
the resulting numbers and graphs for a comprehensive evaluation by experts; on
the other hand, the visualisation should quickly point out interesting findings
and provide assistance in interpreting the results.
The concept for a new framework for analysing resource behaviour based on event
logs as presented in this paper suggests and encourages further research on this
topic. There are several points for future work, including a comprehensive and
practical overview of resource-related metrics or new possibilities to combine and
analyse metrics, also with regard to other research areas, such as psychology.
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