=Paper=
{{Paper
|id=Vol-2973/paper_194
|storemode=property
|title=Interactive Resolution and Prevention of Inconsistencies in Business Rule Management
|pdfUrl=https://ceur-ws.org/Vol-2973/paper_194.pdf
|volume=Vol-2973
|authors=Sabine Nagel
|dblpUrl=https://dblp.org/rec/conf/bpm/Nagel21
}}
==Interactive Resolution and Prevention of Inconsistencies in Business Rule Management==
https://ceur-ws.org/Vol-2973/paper_194.pdf
Interactive Resolution and Prevention of Inconsistencies in
Business Rule Management
Sabine Nagel 1
1
University of Koblenz-Landau, UniversitΓ€tsstr. 1, 56070 Koblenz, Germany
Abstract
To define allowed company behavior, regulations are often represented in the form of business
rules. As rule modeling is an incremental and often collaborative process, there is a high risk
for contradicting rules being modeled. To date, several approaches for the detection,
measurement and automated resolution of such inconsistencies have been introduced. While
these approaches are an important first step towards successful compliance management in
companies, they are currently incapable of providing resolution strategies that are applicable
in a real-life company scenario. This calls for novel resolution approaches with possibilities
for human intervention. By providing human experts with inconsistency metrics and
visualizations, we hope to improve the understanding of inconsistencies, which is a prerequisite
for a successful human-in-the-loop integration. Additionally, we will identify sources of
inconsistencies to develop a general framework for the prevention of inconsistencies already
during modeling.
Keywords 1
Business Rules, Business Rule Management, Inconsistencies, Inconsistency Resolution,
Inconsistency Prevention, Explainability, Human-in-the-loop
1. Introduction and Related Work
Company operations are limited by an increasing amount of internal and external regulations.
Especially as violating such policies might come with major legal consequences, managing compliance
is a current challenge for organizations [1]. In this context, regulations are often represented as
behavioral business rules, which can be defined as conditions in the form of a declarative statement that
define allowed behavior of company processes [2]. Business rules are expressions of the general form
π! , β¦ , π" β π with π! , β¦ , π" representing the condition and π representing the conclusion of a rule.
Business rules originate from different sources and can be modeled either manually or automatically.
Manual authoring of business rules is often a collaborative and incremental process, which increases
the likelihood of erroneous rules being modeled [3]. Furthermore, rule mining can be applied to
automatically extract business rules from already existing sources such as event logs [4,5], process
models [6,7] or natural language [8,9]. As current approaches only focus on the extraction of rules in
general, without taking potential interrelations between rules into account, the resulting rule sets might
contain contradicting statements, which are referred to as inconsistencies.
Generally, we distinguish between different types of inconsistencies. In addition to inconsistencies
in the classic-logical sense, which can be assessed already at design-time, recent works [10,11] have
introduced so-called potential inconsistencies that comprise both actual issues and potential issues and
can only be assessed at run-time. Actual issues describe rules that lead to contradictory conclusions as
soon as their shared condition is met (e.g., {π β π, π β Β¬π}). While there already exist means to assess
the inconsistency types mentioned above, little research has been directed towards potential issues. In
contrast to actual issues, potential issues do not have a shared condition (e.g., {π β π, π β Β¬π}). Thus,
Proceedings of the Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium at BPM 2021 co-located with
the 19th International Conference on Business Process Management, BPM 2021, September 6-10, 2021, Rome, Italy
EMAIL: snagel@uni-koblenz.de
ORCID: 0000-0003-4838-8246
Β© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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they only lead to contradictory conclusions if multiple conditions are met at the same time. As the
individual rules might still make sense on their own, potential issues are particularly hard to assess and
resolve. Lastly, hidden dependencies [12] between rules might also lead to problems. For simplicity,
we will use the term inconsistencies for all types mentioned above in the remainder of the paper.
Currently, inconsistency handling is mainly located after the authoring phase of the business rules
management (BRM) lifecycle (see Figure 1) [13], i.e., inconsistencies are usually identified, measured,
and resolved after business rules have been modeled. Recent works include the measurement of both
classic inconsistencies [14,15] and actual issues [10,11] in general, implementations of inconsistency
measurement in DMN decision tables [16] as well as first inconsistency resolution approaches [17,18].
Align Creation & Maintenance Implementation
Plan Capture Organize Author Verify Apply
Figure 1: Business Rules Management Lifecyle (adapted from [13])
Regarding the latter, there currently only exist automated approaches that delete elements from a
rule base to achieve consistency. While this is an important first step, it might not always be plausible
or applicable in a real-world scenario, as this might lead to erroneous rules being kept, while potentially
business critical rules are deleted instead. To solve this problem, it is not only important to include
human experts but also to consider additional change patterns [19] (e.g., editing existing rules or adding
context to rules) in novel inconsistency resolution approaches. While the deletion of rules can only
lower the overall inconsistency of a rule base, changing rules might result in the creation of additional
inconsistencies, which must be prevented as part of a resolution strategy. In addition to resolving
existing inconsistencies, it is also important to focus on the prevention of inconsistencies already
during rule modeling, i.e., in the capture, organize or author phase of the BRM lifecycle (see Figure 1).
Thus, we will address these two research gaps by developing interactive inconsistency resolution
and prevention strategies, as further described in the following section.
2. Research Aim and Methodology
The aim of this thesis is to provide a continuous and interactive management of inconsistencies in
business rule bases across the entire BRM lifecycle. This not only includes interactive approaches for
measuring, visualizing, and resolving already existing inconsistencies, but also preventing
inconsistencies from being modeled in the first place. Here, the focus is on a strong human-in-the-loop
integration, as this aspect is crucial for the applicability of the results in companies.
2.1. Research Objectives and Questions
This work consists of four main research objectives (RO) and their corresponding research questions
(RQ), which are described in more detail below. All objectives are closely interrelated and build upon
each other, which is visualized in Figure 2.
Identification of Inconsistency Sources (RO1)
Improvement of Inconsistency Understanding (RO2) Prevention of Inconsistencies (RO4)
Interactive Inconsistency Resolution (RO3)
Figure 2: Research Objectives and their Interrelations
As a foundation for the prevention of inconsistencies (RO4), it is important to first identify their
exact origin. Here we distinguish between two types of rule modeling, namely manual and automatic
modeling. As manual modeling is often an incremental and collaborative process, our first goal is to
better understand the manual modeling process and identify the causes for potentially inconsistent
business rules. Additionally, inconsistent business rules can originate from rule mining, i.e.,
automatically extracting rules from event logs, process models, or natural language descriptions. Here,
the aim is to understand which parts of these rule mining algorithms enable inconsistent business rules
to be modeled as this is an important prerequisite for future resolution and prevention strategies. Thus,
this objective leads to the first research question.
RQ1: How do manual and automatic rule modeling approaches enable inconsistent business rules to
be modeled?
To develop interactive resolution and prevention approaches that involve human experts, it is
important for humans to understand inconsistencies in business rules. Existing research on declarative
process model understanding [12,20,21] has shown that especially combinations of constraints and
hidden dependencies pose challenges to human modelers. This also leads to a lack of inconsistency
understanding within such a model, which impairs their resolution and prevention. To solve this
problem and to increase understandability of inconsistencies, we will make use of decision support
technologies from the area of Business Intelligence (BI) [22,23]. The field of inconsistency
measurement [14,24] already provides a number of quantitative measures for both entire rule bases and
formulas within a rule base. In this work, we will focus on the explainability and extension of existing
rule-based measures, as well as developing novel metrics, such as semantic and economic measures. In
addition to providing the user with quantitative metrics, we will also develop and test novel
inconsistency visualization techniques. Thus, the following research question will provide a strong
foundation for the interactive resolution (RO3) and prevention (RO4) of inconsistencies.
RQ2: How can quantitative metrics and visualization techniques improve the understanding of
inconsistencies in business rules?
The aim of the third objective (RO3) is to develop inconsistency resolution approaches with a strong
human-in-the-loop integration. As current state-of-the-art approaches [17,18] solely apply (semi-
)automated inconsistency resolution, we will first examine the applicability of these approaches to an
interactive inconsistency resolution. Here, the focus will be on explainability, as the measures used in
existing approaches mainly focus on guaranteeing minimal information loss as opposed to supporting
humans in understanding inconsistencies and helping them to decide which rules are erroneous. The
output of this objective will be an approach for a guided and stepwise inconsistency resolution, by
integrating the inconsistency metrics and visualizations with the resolution approach itself. In this
context, we will also investigate inconsistency resolution based on different change patterns [19], as
changing a rule or adding context to rules might be more plausible in a real-life scenario compared to
simply deleting rules from a rule base. Therefore, this objective leads to the third research question.
RQ3: How can inconsistencies in business rules be successfully resolved in an interactive manner?
To prevent inconsistencies already during rule authoring, we will integrate the results from RO1-
RO3 and develop a general framework for inconsistency prevention, which is covered by the last
research question (RQ4). Here, we also distinguish between manual and automatic rule modeling. To
prevent inconsistencies during manual modeling, we will develop a procedure model containing best
practices, based on the findings from RO1. Also, we will focus on the continuous support of consistent
rule modeling by preventing inconsistencies during modeling, e.g., after only parts of the rule have been
entered. To measure and visualize the impact of a current rule input, we will make use of the results
from RO2, as an understanding of the current rule base and potentially arising inconsistencies is crucial
for this step. In addition to identifying, measuring, and visualizing classic inconsistencies and actual
issues directly, we will also develop means to predict and prioritize potential issues. Furthermore, we
will develop approaches for consistent rule mining. Based on the findings from RO1, we will extend
existing rule mining algorithms with novel parameters and develop entirely new approaches that take
interrelations between rules into account to prevent the resulting rule base from being inconsistent.
RQ4: How can inconsistencies already be prevented during rule modeling?
2.2. Research Design
To develop and evaluate interactive inconsistency resolution and prevention approaches, we will
apply a Design Science Research (DSR) methodology [25]. Table 1 provides an overview of the DSR
phases, the respective RQs, and a description of each phase, including the applied research methods.
Table 1: DSR Phases and Research Methods
DSR Phase RQs Description
Awareness of - To get an overview of the state of the art in the areas of BRM and especially
Problem inconsistency handling within business rule bases, an extensive literature review [26β
28] has been conducted. The result of this phase is a research proposal containing the
problem statement and identified research gaps that lead to the research objectives and
questions guiding this work.
Suggestion RQ1 Immediately following the previous phase, the suggestion phase aims at developing a
RQ2 tentative design for the envisioned artifact. To this aim, we will not only conduct
further literature reviews in the areas of manual and automatic rule modeling and
process model understanding, but also apply qualitative research methods to better
understand the manual modeling process and inconsistency understanding in general.
This includes conducting interviews, case studies and eye-tracking experiments to gain
both subjective and objective insights.
Development RQ3 Next, the tentative design is further developed and implemented. This includes the
RQ4 development of novel metrics and visualization techniques to improve inconsistency
understanding, which then provide a strong foundation for the development of
interactive inconsistency resolution and prevention approaches, which are the focus of
this phase and this work in general.
Evaluation RQ2 In this phase, the developed artifact will be iteratively evaluated and tested to analyze
RQ3 its behavior and identify required changes to the tentative design. At this stage, we will
RQ4 again focus on a close human-in-the-loop integration. To this aim, we will conduct
experiments to evaluate the (cognitive) effects of the developed metrics and
visualizations, as well as the entire resolution and prevention approaches.
Conclusion RQ3 This last phase will focus on the presentation and communication of results, as well as
RQ4 positioning them in terms of their contribution to knowledge.
3. Contribution, Current State and Outlook
The main contribution of this work is twofold. First, we will develop and evaluate explainable and
interactive resolution approaches, which enables the applicability of inconsistency resolution directly
in companies. Second, we will provide means for inconsistency prevention in the form of a general
framework. Thus, this work will contribute to BRM by integrating inconsistency handling throughout
the entire BRM lifecycle and preventing companies from having to detect, measure and resolve
inconsistencies in the future. Additionally, the results of this work will contribute to research by
identifying weaknesses of current state-of-the-art rule mining and modeling approaches and providing
additional insights into process model understanding in general.
Regarding the current state of this work, the Awareness of Problem phase has been successfully
completed and the focus is now on the Suggestion phase. In order to examine the potential of applying
BI in the form of metrics and visualization techniques to improve inconsistency understanding, we have
already conducted initial studies with human participants to not only test the effects of quantitative
measures in general [29] but also the effects of visualization techniques on understanding
inconsistencies in business rules [30,31]. Based on these results, we will now focus on further
developing inconsistency metrics and visualizations that can serve as a basis for interactive resolution
and prevention approaches. Furthermore, we have recently published a paper on an interactive and
minimal repair of declarative process models [32] to show, how a human-in-the-loop integration could
be combined with a semi-automated approach focusing on minimal information loss w.r.t. the number
of deleted rules. As such a combination of automation and human-in-the-loop integration can provide
many benefits, we will further extend this approach towards a stepwise and interactive inconsistency
resolution as a next step.
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