=Paper=
{{Paper
|id=Vol-3758/paper-03
|storemode=property
|title=Discovering Organizational Models from Event Logs for Workforce Analytics
|pdfUrl=https://ceur-ws.org/Vol-3758/paper-03.pdf
|volume=Vol-3758
|authors=Jing Yang
|dblpUrl=https://dblp.org/rec/conf/bpm/Yang24
}}
==Discovering Organizational Models from Event Logs for Workforce Analytics==
https://ceur-ws.org/Vol-3758/paper-03.pdf
Discovering Organizational Models from Event Logs
for Workforce Analytics (Extended Abstract)
Jing Yang1,†
1
Queensland University of Technology, Brisbane, Australia
Abstract
This PhD research aims to develop process mining approaches to support workforce analytics and
improve business process management. It presents a set of novel data-driven methods to systematically
construct and analyze organizational models, which can be utilized for guiding organizational structure
design and staff deployment toward process improvement.
Keywords
Process mining, event log, organizational model, workforce analytics, organizational model discovery,
conformance checking, business process management
1. Introduction
Organizations today operate in a rapidly changing world. Leaders of organizations need to
have a deep insight into their employees in order to streamline business processes and increase
competitiveness. Many are exploring opportunities to apply workforce analytics and understand
how human resources act in groups [1] to deliver organizational outcomes.
Process mining holds high promise for achieving successful workforce analytics. It enables
analysts to exploit event logs — readily available data [2] that records how human resources
participated in actual process execution — and extract accurate and timely insights into employee
performance and collaboration [3]. A relatively underexplored subfield of process mining,
organizational model mining [4, 5, 6], is concerned with the study of groups of human resources,
specifically how models can be derived from event logs to reflect resource groupings in process
execution. However, existing methods for organizational model mining are not fully up to the
task of supporting analyses of human resource groups.
Our research reveals three major gaps in the literature, as illustrated in Figure 1. First, event
logs recording process execution typically encompass multiple dimensions including case,
activity, and time. Existing organizational model mining methods mainly focus on exploiting
the activity dimension, but rarely consider the case and time dimensions. This inattention to
multidimensional information is limiting, when resource groupings need to be considered across
different cases (e.g., specialist groups dedicated to particular customers) or across different
time periods (e.g., employees playing the same role but working different shifts). Second,
Proceedings of the Best BPM Dissertation Award, Doctoral Consortium, and Demonstrations & Resources Forum co-located
with 22nd International Conference on Business Process Management (BPM 2024), Krakow, Poland, September 1st to 6th,
2024.
†
Jing Yang is also known by the name Roy Yang.
$ roy.j.yang@qut.edu.au (J. Yang)
0000-0001-9218-6954 (J. Yang)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
organizational models discovered by existing methods often do not transcend the mere clustering
of resources — they do not describe how the discovered resource groups were involved in process
execution. Therefore, they are not very helpful in analyzing and understanding the behavior
of resource groups. Last but not least, existing methods rely on either domain knowledge or
technique-specific, intrinsic measures to evaluate discovered models. A generic evaluation
approach, which can assess discovered models against the input event logs, remains absent.
event
2
cases
1 time
activities
discover
3
resource groups resources
event log organizational model
in state-of-the-art
Figure 1: An illustration of the three research gaps identified in state-of-the-art organizational model
mining research: (1) lack of consideration for the multidimensional information recorded in event
logs; (2) missing interpretation of discovered resource groups in terms of their involvement in process
execution; and (3) absence of evaluation between input event logs and discovered models.
2. Research Approach and Outcomes
In light of the identified research gaps, this PhD thesis sets out to develop novel organizational
model mining methods. It extends state-of-the-art process mining by addressing several research
questions (RQ). Figure 2 provides an overview of the research in this thesis.
RQ1. How can organizational models be discovered from event log data?
First, we propose a new, rich notion of organizational model as the foundation of a novel
framework for organizational model mining from event logs. Compared to the literature,
the new organizational models consider multiple dimensions of process execution, captured
by the notion of execution contexts, where events (instances of human resource performing
activities) are categorized by their respective case types, activity types, and time types. The new
organizational models can link relevant process execution information with resource groupings.
As such, they can be used to represent more comprehensive knowledge about resource groups
and their involvement in processes.
Building on the new notion of organizational model, we introduce a conceptual framework
for organizational model mining, namely OrdinoR 1 [8]. The OrdinoR framework formulates
1
Ordino means “to arrange” in Latin; the trailing letter R stands for “resources”.
events mapped onto OrdinoR organizational model
multidimensional
execution contexts
case
Model
types
discovery
time
activity types types
execution contexts resource groups resources
event log
Model organizational model fitness
evaluation model quality model precision
Model resource group Applying
analysis work profiles visual analytics
workforce employee segmentation
analytics performance review
findings compliance issues
Data processing Evaluation
improvement
event data refined/new
ideas
analytical questions
Figure 2: An overview of this PhD research, including the proposed notions and methods, and their
usage as process mining techniques in context of the Process Mining Project Methodology [7]. We
propose a new, rich notion of organizational model and introduce a novel framework (namely OrdinoR)
for the discovery, evaluation, and analysis of organizational models using event logs (addressing RQ1).
OrdinoR organizational models may be applied to derive resource group work profiles and be used to
analyze resource group performance in process execution (addressing RQ2).
three types of organizational model mining task, i.e., discovery, evaluation, and analysis. Model
discovery aims to construct organizational models to characterize the grouping of resources
and their involvement in actual process execution recorded by event logs. Model evaluation
aims to assess organizational model quality with respect to an event log. Model analysis aims
to examine the performance of resource groups captured in organizational models and provide
information to support workforce analytics.
For model discovery, we propose a systematic approach to discovering organizational models
from event logs [9, 8]. It is capable of incorporating user domain knowledge and constructing
organizational models from event logs with a minimum set of standard attributes. Specifically,
this approach addresses three concrete model discovery tasks, presenting alternative methods
for each task: (i) learning execution contexts to characterize the specialization of resources from
multiple dimensions, applying decision-tree- and simulated-annealing-based rule induction;
(ii) discovering resource grouping to identify clusters of resources with shared characteristics,
applying hierarchical clustering and model-based clustering; and (iii) profiling discovered resource
groups to determine resource group capabilities in process execution, by ranking execution
contexts where groups had the highest participation and contribution. We then conduct a series
of experiments on five real-life event logs to evaluate the proposed model discovery approach
under various configurations.
For model evaluation, we introduce two measures, fitness and precision [8]. These measures
are the first of its kind in process mining, providing a generic basis for assessing the quality
of discovered organizational models with respect to event logs. They can be used to evaluate
organizational models without relying on domain knowledge of existing organizational models
or technique-specific measures.
For model analysis, we formulate a set of quantitative measures for analyzing the behavior of
resource groups [8] based on an organizational model and an event log, considering resource
group workload distribution and the contribution by group members.
Knowing how organizational models may be extracted from event logs and how they represent
knowledge about resource groups, the next step is to investigate the application of such models
to support workforce analytics. Therefore, this thesis also investigates the following question.
RQ2. How can organizational models be exploited to analyze resource group performance in
process execution?
We extend the measures proposed for organizational model analysis and formulate the notion
of resource group work profile [10]. This notion is developed based on reviewing the management
literature on human resource performance measurement. Resource group work profiles encom-
pass an array of indicators as informed by the management literature. These indicators can be
extracted from event logs and organizational models and be applied to measure various aspects
relevant to how resource groups and their members work in process execution. Furthermore,
we also introduce an approach to applying visual analytics to work profiles extracted from data.
It enables tracking, comparing, and correlating resource groups’ performance — across group
and individual levels, over different time periods, and related to various process dimensions.
We built open-source software tools 2 3 to implement the proposed research artifacts. The
organizational model discovery methods, as well as the measures for model evaluation and
analysis, are implemented in the tools.
3. Conclusion and Future Work
This research contributes to the field of process mining from the organizational perspective [2].
Specifically, it addressed three research gaps concerned with organizational model mining by
(i) utilizing multidimensional event log information for model discovery, (ii) improving the
interpretability of organizational models by capturing resource group involvement in process
execution, and (iii) enabling a generic method for assessing the quality of discovered models
with model evaluation and model analysis measures. Furthermore, our approach to extracting
and analyzing resource group work profiles presents a novel means to exploit discovered
organizational models for analyzing resources and their groupings. This enhances the practical
use of organizational model mining.
Outcomes from this research pave a number of avenues for future process mining research.
Notably, the model evaluation and model analysis measures form the basis for conformance
checking of organizational models with respect to event logs, in terms of both global conformance
2
The OrdinoR library for organizational model mining: https://royjy.me/to/ordinor
3
Prototype performing visual analytics of resource group work profiles: https://royjy.me/to/gwp-demo
and local diagnostics. To do so, it will be necessary to investigate questions around encoding ex-
isting resource and organizational models in conventional forms (e.g., role-based access control
models, rosters, organizational charts) into OrdinoR models. Organizational model conformance
checking will subsequently inform novel process mining approaches to support using process
execution data to guide the design of process-oriented organizational structures (e.g., role
designation and employee team composition) and staff deployment alongside evolving business
processes. In the meantime, our approach contributes to the precise modeling of resources and
resource groups in process execution and therefore will benefit process simulation from the or-
ganizational perspective. The integration of process simulation of organizational model mining
offers improved solutions to scenario-based workforce planning concerned with processes. Last
but not least, the generic definition of OrdinoR organizational models allows for extensions to
capture more complex relations among resources and their groups, for example, hierarchy in
organizations and reporting relations. This opens opportunities to connect organizational model
mining with other process mining tasks from the organizational perspective, e.g., discovering
social network among resources to capture handover and cooperation [4].
This research also contributes to bridging process mining with the field of human resource
management on the topic of workforce analytics. We introduced event logs as a useful data
source capturing the execution of end-to-end processes, and how event logs may be exploited for
group-oriented workforce analytics using our approaches. This contributes toward addressing
some key challenges in workforce analytics, with regard to inconsistent recording of human
resource performance and its disconnection from analysis of organizational performance [1].
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