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Proceedings ArticleDOI

A Tool for Modelling Business Behaviour Using Decision Tables

TL;DR: A tool supporting end users to capture and model business behaviour and integrates decision tables for capturing actionable knowledge using ontology technology is proposed.
Abstract: Decision tables have been recently recognised as an effective technique to model business behaviour. The increasing adoption of decision tables in this context requires appropriate end-user support. Although several tools have been developed using decision tables, few have been targeted to business end users. This paper fulfils this gap by proposing a tool supporting end users to capture and model business behaviour. The proposed tool integrates decision tables for capturing actionable knowledge using ontology technology. The tool is validated in two case studies.

Summary (2 min read)

INTRODUCTION

  • For decades, decision tables have been used to represent decisions in the context of programming logic [1] .
  • The fundamental goal is to improve business systems such as order management, service delivery and client relationship management, just to mention few that could benefit from improved business modelling.
  • This problem is challenging because of the distance between the technical and the organisational/end user levels.
  • Therefore, the authors research decision tables from an end user perspective;.

II. LITERATURE REVIEW

  • A decision table is a tabular structure for describing and analysing decisional logic, which defines what actions can be performed depending on given conditions [20] . Rules: Rows define rules linking specific conditions to actions.
  • The decision table in Table 1 has three rules.
  • In a similar vein, Batoulis and Weske [7, 9] developed a tool that checks overlapping rules and simplifies decision tables.
  • While ontologies provide definitional knowledge about a business [14, 15, 26, 27] , decision tables provide actionable knowledge about the business behaviour.

III. METHOD

  • The authors research adopts the DSR approach to develop a proofof-concept tool.
  • The approach comprises two activities: build and evaluate [16] .
  • A plugin for modelling business behavior by integrating ontologies and decision tables -Ontologies managed by Protégé [19].
  • The first case -order discount and delivery -is adapted from DMN [3] and illustrates that the tool is suitable for making business operational decisions.
  • The second case -decision to crowdsource -is more related to business strategy [19] , as it supports end users making decisions about crowdsourcing depending on several contingency factors.

A. Order Discount and Delivery (adapted from [3])

  • When company X processes customers' orders, it has to make two relevant decisions.
  • The first is to decide how much discount a particular order gets, which depends on two factors: type of customers (business vs. private) and order size (>=10 vs. <10).
  • The second is to decide how to deliver the order, which depends on the order's weight.
  • The decision to make a discount includes three business rules.

Fig. 3. Decision table: Discount

  • The delivery decision includes two business rules.
  • The plugin is then used to model business behaviour, which includes definitional and actionable knowledge.
  • Order is defined as the parent concept, and the others are sub-concepts.
  • The authors then relate these concepts to the two decision tables previously discussed: the discount decision table is added to the discount concept; and the delivery decision table is added to the delivery concept (see Fig. 2 ).
  • When the end user changes the configuration, the visualiser combines the new inputs with the actionable knowledge and generates the corresponding decisions.

B. Decision to Crowdsource

  • The decision to crowdsource is an important decision for organizations to strategize how to use such workforce.
  • The end user can navigate the ontological concepts related to crowdsourcing, including definitions, references, relationships, and can link the concepts to actionable knowledge.
  • In the example illustrated in Fig. 6 , the visualiser shows four decision tables regarding the decision to crowdsource, which enables users to execute various related business rules.
  • The visualiser enables end users to change the input data and observe the outputs.
  • The second case shows the tool can be used to explore complex decisions.

VI. DISCUSSION AND CONCLUSION

  • This study assesses the adoption of decision tables to model business behaviour [4, 8, 13] .
  • The authors integrate definitional and actionable knowledge using ontologies and decision tables.
  • Furthermore, the authors suggest that end users can model and explore business behaviour using what-if scenarios.
  • Secondly, the tool was developed for research purposes, not actual use by organisations.

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A Tool for Modelling Business Behaviour Using
Decision Tables
Tomas Antunes
Victoria University of
Wellington
Wellington, New Zealand
tomas.antunes@icloud.com
Pedro Antunes
Victoria University of
Wellington
Wellington, New Zealand
pedro.antunes@vuw.ac.nz
David Johnstone
Victoria University of
Wellington
Wellington, New Zealand
david.johnstone@vuw.ac.nz
Vo Trong Nghia
Can Tho University of
Technology
Can Tho, Vietnam
vtnghia.ktpm0115@student.
ctuet.edu.vn
Nguyen Hoang Thuan
Can Tho University of
Technology
Can Tho, Vietnam
nhthuan@ctuet.edu.vn
Abstract— Decision tables have been recently recognised as
an effective technique to model business behaviour. The
increasing adoption of decision tables in this context requires
appropriate end-user support. Although several tools have been
developed using decision tables, few have been targeted to
business end users. This paper fulfils this gap by proposing a tool
supporting end users to capture and model business behaviour.
The proposed tool integrates decision tables for capturing
actionable knowledge using ontology technology. The tool is
validated in two case studies.
Keywords— Decision Tables, Decision Tools, Ontologies,
Camunda, Protégé, Case study.
I. INTRODUCTION
For decades, decision tables have been used to represent
decisions in the context of programming logic [1]. Recently,
the use of decision tables has gained momentum in the
business context with the purpose to better model business
behaviour. The fundamental goal is to improve business
systems such as order management, service delivery and client
relationship management, just to mention few that could
benefit from improved business modelling. A contributing
factor to the wider adoption of decision tables in the business
context is the decision by the Object Management Group
(OMG) to integrate decision tables into the Decision Model
and Notation (DMN) standard, which is currently used by
many business systems [2, 3].
Integrated with DMN, the approach enables system
developers to create business systems that can be injected with
business models defined by the end users themselves [3, 4].
Such approach has been named either end-used design or meta-
design [5]: while developers define the meta functionality, end
users finish the design with their contextualised business
models.
Furthermore, decision tables are viewed as a more flexible
way to model business behaviour, especially when compared
to similar technology such as decision trees and oblique rules
[6]. Decision tables are also considered easier to integrate with
business systems, in particular business process management
[7]. These important capabilities have motivated an increased
research interest, as suggested by an increasing number of
publications on the topic [2-4, 8].
By and large, the body of literature on decision tables
embraces two very distinct problems, which can be related to
computer science and information systems (IS). In the
computer science domain, researchers are mainly interested in
the formalisation, standardisation, automation, optimisation,
and integration of decision tables at the technical level.
Examples in this category include semantic analysis [7, 9-12],
detection of inconsistencies and missing rules [7, 10, 11], rule
formalisation, and optimisation [2].
On the other hand, the IS field is more concerned with the
use, adoption and implementation of the technology at the
business and organisational levels. Examples in this category
include business modelling, decision making, knowledge
integration, knowledge management, business support, and
usability [4, 8, 13].
This paper concerns business modelling from an IS
perspective. In particular, we are interested in understanding
how to support end users to meta design business
behaviour through decision tables. This problem is
challenging because of the distance between the technical and
the organisational/end user levels. Such distance makes the
approach more amenable to technology experts (e.g.,
developers, requirements engineers and technical analysts)
than to end users (e.g. middle managers, business analysts and
decision makers). This problem is relevant because a solution
creates opportunities for developing more flexible, resilient
and accessible business systems.
In particular, this research addresses the following goals:
1. Enable end users to model business behaviour using
decision tables. Therefore, we research decision tables
from an end user perspective;
2. Integrate decision tables with ontology technology.
While decision tables provide actionable knowledge
about business behaviour, ontological models provide
the underlying definitional knowledge [14, 15];
3. Enable end users to explore what-if business scenarios
by interacting with decision tables. Here, we note that
business models often involve multiple factors and
conflicting values, which require users to explore
different possibilities before making an informed
decision about the best course of action.
This study adopts a Design Science Research (DSR)
paradigm, dividing the research in two main activities, build
and evaluate [16]. Regarding the build activity, we use open
source technology to integrate decision tables with ontology
technology. In particular, we use Camunda [17] and Protégé
[18] to model business behaviour. Camunda is used to manage
decision tables, while Protégé is used to manage definitional
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knowledge. The solution allows end users to model business
behaviour and perform what-if analysis.
Regarding the evaluate activity, we adopt the case study
approach to illustrate the usefulness of the developed solution,
considering in particular the three abovementioned research
goals. The selected cases consider order discount and delivery
[3], and business process crowdsourcing [15, 19].
This research provides the following contributions. Firstly,
we develop a solution that supports end users in independently
modelling business behaviour using ontologies and decision
tables. Secondly, we illustrate the usefulness of the approach.
II. L
ITERATURE REVIEW
A decision table is a tabular structure for describing and
analysing decisional logic, which defines what actions can be
performed depending on given conditions [20]. While there are
different ways to represent decision tables, they generally
consist of three basic elements:
Conditions: An input column defines alternative
conditions, which are set or not depending on an
independent variable. Table 1 presents an example of a
decision table with two input columns considering
customer and order size as variables.
Actions: Output columns define which actions are
executed after applying the corresponding rules. Table 1
shows a decision table with one output column named
discount. The 5% action is executed if rule 3 is applied.
Rules: Rows define rules linking specific conditions to
actions. Rules are applied when the corresponding
conditions are true. The decision table in Table 1 has
three rules. Rule 3 is applied if a customer is private.
The symbol (-) means the condition is true by default.
TABLE
I. A
N
E
XAMPLE OF A
D
ECISION
T
ABLE
Rules
Input Output
Customer Order size Discount
1
Business >=10 15%
2
Business <10 10%
3
Private - 5%
We now review the literature on decision tables, focusing
on the recent interest in using decision tables for business
modelling [2, 4, 8-10, 21, 22]. Since the introduction of the
DMN standard, several research lines have explored how to
use this technology.
A research line has been centred on how to specify and
validate business rules using decision tables, so they are
complete, consistent and non-redundant [2, 7, 8, 10, 11, 13].
Algorithms have been developed to detect overlaps, conflicts
and missing rules [7, 10, 11], and to simplify these tables [2,
23]. These studies extend prior research in computer science
[20, 24], but applied to the more complex business context.
Several proof-of-concept tools have been developed to
evaluate the proposed algorithms. Laurson and Maggi [10]
developed a tool for verifying missing and overlapping rules in
decision tables. In a similar vein, Batoulis and Weske [7, 9]
developed a tool that checks overlapping rules and simplifies
decision tables.
Another research line is centred on how to model business
behaviour using decision tables. The overarching goal is to
integrate decision tables with business processes. This goal can
be further decomposed into two related goals [4, 21, 22]:
integrating business processes with the domain knowledge and
business logic (conditions, actions and rules) expressed in
decision tables; and automating the execution of business rules
within the execution of business processes [10]. In particular,
several proof-of-concept tools have been developed integrating
business process modelling (e.g., BPMN) and decision
modelling [21, 22, 25].
In our research, we adopt an alternative viewpoint. Instead
of representing business behaviour using a combination of
decision and process modelling, we consider a combination
of decision and ontology modelling [4, 26, 27]. While
ontologies provide definitional knowledge about a business
[14, 15, 26, 27], decision tables provide actionable knowledge
about the business behaviour.
However, little attention has been given in the related
literature to the integration of definitional and actionable
knowledge, considering in particular the integration of
ontologies and decision tables as a way to represent business
behaviour. This paper aims to address this research gap.
III. M
ETHOD
Our research adopts the DSR approach to develop a proof-
of-concept tool. The approach comprises two activities: build
and evaluate [16]. Fig. 1 and the text below describe the two
activities in more detail.
A plugin for modelling business behavior by
integrating ontologies and decision tables
- Ontologies managed by Protégé [19]
- Decision tables managed by Camunda [14]
A prototype for executing decision tables
and performing what-if analysis
Build activity
(two artefacts)
Case studies for illustrating the tool utility
- Order discount and delivery [3]
- Decision to crowdsource
Evaluate
activity
Fig. 1. Research activities
The build activity aims to develop a proof-of-concept tool
modelling business behaviour using decision tables. The tool
consists of two artefacts. The first artefact is a plugin allowing
end users to model business behaviour using an ontology and
several decision tables. The second artefact is a visualiser
allowing end users to explore the model in what-if scenarios.
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We evaluate the proof-of-concept tool in two case studies.
We purposefully chose two cases with different characteristics.
The first case – order discount and delivery – is adapted from
DMN [3] and illustrates that the tool is suitable for making
business operational decisions. The second case – decision to
crowdsourceis more related to business strategy [19], as it
supports end users making decisions about crowdsourcing
depending on several contingency factors.
IV. T
OOL DEVELOPMENT
A. The Plugin
The main purpose of the plugin is to model business
behaviour. We used existing open-source software to develop
the plugin. More specifically, we rely on Camunda to manage
decision tables and Protégé to manage ontologies. The plugin
publicly is available at:
https://github.com/tom277/EBM_ruleManagement
The plugin consists of ontology and decision modules. The
ontology module is shown on the left-hand side of Fig. 2.
Empowered by Protégé, it enables end users to define classes,
instances, attributes, and relations, which together provide
definitional knowledge about a business. The case shown in
Fig. 2 shows that the business behaviour considers orders,
deliveries and discounts.
The decision module is shown on the right-hand side of
Fig. 2. It enables end users to create and link decision tables to
particular concepts defined in the ontology. For instance, Fig. 2
shows that discount rules have been defined and linked to the
discount concept. Decision tables are defined using Camunda.
B. Visualiser
The visualiser enables end users to explore what-if
scenarios of business behaviour using the models defined with
the plugin (see Fig. 5 and Fig. 6). The visualiser was developed
in Java and is available at:
https://github.com/tom277/EBM_tool
The visualiser consists of two main modules: ontology
visualisation and decision visualisation. The ontology is
visualised as a tree structure, reflecting the typical hierarchical
structure of ontological representations. It provides an
overview of the concepts defined in the business domain. Users
can select the elements to see definitions and relationships to
other elements.
When the end user selects a decision element, the
visualiser shows the related decision tables. The decision
visualisation module highlights a set of decision conditions
that the user may change by configuring input variables. When
the user configures an input, the module executes the decision
rules specified in the decision tables and visualizes the
outputs. This allows end users to perform what-if analysis by
changing the inputs and analysing the outputs.
Fig. 2. Plugin integrating ontologies and decision tables
V. CASE STUDY
We describe two case studies validating the proof-of-
concept tool. For each case, we provide background
information and discuss how the tool models business
behaviour.
A. Order Discount and Delivery (adapted from [3])
When company X processes customers’ orders, it has to
make two relevant decisions. The first is to decide how much
discount a particular order gets, which depends on two factors:
type of customers (business vs. private) and order size (>=10
vs. <10). The second is to decide how to deliver the order,
which depends on the order’s weight.
The decision to make a discount includes three business
rules. Camunda is used to create the decision table shown in
Fig. 3.
Fig. 3. Decision table: Discount
The delivery decision includes two business rules. Fig. 4
shows the decision table capturing these rules.
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Fig. 4. Decision table: Delivery
The plugin is then used to model business behaviour, which
includes definitional and actionable knowledge. Regarding
definitional knowledge, we model three basic concepts: order,
discount decision, and delivery decision. Order is defined as
the parent concept, and the others are sub-concepts. We then
relate these concepts to the two decision tables previously
discussed: the discount decision table is added to the discount
concept; and the delivery decision table is added to the delivery
concept (see Fig. 2).
The visualiser can then used to display the definitional and
actionable knowledge, and to analyse what-if scenarios (Fig.
5). The visualiser shows the three business concepts (order,
discount decision), including parent-child relationships, and
associated rules. When the end user selects a concept, the
associated decision rules are visualised. The end user can then
configure different options and analyse the outputs. When the
end user changes the configuration, the visualiser combines the
new inputs with the actionable knowledge and generates the
corresponding decisions. The visualiser generates the business
behaviour automatically, so that the end user can analyse the
results immediately. For instance, if the user changes the order
size from “>=10” to “<10”, the visualiser will immediately
suggest 0.10 as the new discount.
Fig. 5. Visualiser for the order discount and delivery case
B. Decision to Crowdsource
Crowdsourcing is an emerging way to use an online
workforce to outsource business tasks. The decision to
crowdsource is an important decision for organizations to
strategize how to use such workforce. Yet, it is a complex
decision for organisations, since several contingency factors
should be examined [19, 28]. Seventeen relevant decision
factors influencing the decision to crowdsource have been
identified [19, 28]. Based on these factors, several decision
tables can be defined, regarding different aspects of
crowdsourcing such as task properties, people, management,
and environment [19].
Furthermore, the decision factors have to be considered
along with other defining aspects of the organization, which
concern definitional knowledge. The decision to crowdsource
is therefore complementary to definitional knowledge, which
has been specified using an BPC ontology [15].
Using our proof-of-concept tool, we can support businesses
making the decision to crowdsource. The plugin is used to
model the BPC concepts, their hierarchical relationships, as
well as the decision rules. The visualiser is then used to analyse
different scenarios and decide what to do. Fig. 6 shows the
visualiser when making the decision to crowdsource.
By using this tool, end users can model the crowdsourcing
behaviour and perform what-if analysis of the available
alternatives. The left-hand side of Fig. 6 shows the definitional
knowledge required to make the decision to crowdsource,
while the right-hand side shows the action possibilities. The
visualiser shows the definitional knowledge as a graph, which
can be navigated. The end user can navigate the ontological
concepts related to crowdsourcing, including definitions,
references, relationships, and can link the concepts to
actionable knowledge.
When the end user accesses a concept referring to business
decisions, the visualiser shows the relevant decision tables (the
2019 19th International Symposium on Communications and Information Technologies
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right-hand side of Fig. 6). In the example illustrated in Fig. 6,
the visualiser shows four decision tables regarding the decision
to crowdsource, which enables users to execute various related
business rules.
We note that making the decision to crowdsource is not
straightforward but needs to consider multiple factors. The
visualiser enables end users to change the input data and
observe the outputs. As a result, the user gains a better
understanding of the decision to crowdsource. The ‘Summary
of Rules’ tab provided by the visualizer allows end users to
review decisions arising from crowdsourcing rules. This tab
provides knowledge required to make an informed decision.
Fig. 6. Visualiser for the decision to crowdsource case
Overall, the two cases show how the tool supports end
users modelling business behaviour and making decisions. The
first case, which has been adapted from DMN, shows the tool
is suitable for making business decisions. The second case
shows the tool can be used to explore complex decisions. Both
cases highlight the tool can manage ontological and actionable
knowledge. They also highlight how the tool can support what-
if analysis to make more informed decisions.
VI. D
ISCUSSION AND CONCLUSION
This study assesses the adoption of decision tables to model
business behaviour [4, 8, 13]. We developed a proof-of-
concept tool that supports end users defining business
behaviour and making what-if decisions about which decisions
to make in specific business contexts. The tool inherits the
strengths of decision tables and ontology technologies. While
ontologies capture definitional knowledge [26, 27, 29],
decision tables capture actionable knowledge [4]. We
integrated the two technologies in a tool that is targeted to end
users. The tool was used in two business cases. The cases
illustrate how the tool can be used by end users to create,
model, visualise, and make decisions depending on the
business context.
This study contributes to business modelling and
knowledge management. We integrate definitional and
actionable knowledge using ontologies and decision tables.
The approach contrasts with prior research where business
behaviour is modelled using a combination of business
processes and decision tables [21, 22, 25]. Our work suggests
that definitional knowledge is an important element in business
modelling. Furthermore, we suggest that end users can model
and explore business behaviour using what-if scenarios.
Ontologies have been applied to formalise and share
knowledge [14, 15]. This study extends the use of ontologies to
behaviour modelling in business contexts. We suggest the
integration of decision tables and ontologies in business
systems, so they can inform decision making. We regard the
integration of decision tables and ontologies as exaptation [30]:
using well-known solutions to address new problems.
2019 19th International Symposium on Communications and Information Technologies
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  • ...In particular, we use Camunda [17] and Protégé [18] to model business behaviour....

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  • ...2019) [16] Hevner, A., March, S.T., Park, J., and Ram, S.: ‘Design science in information systems research’, MIS Quarterly, 2004, 28, (1), pp. 75-105 [17] Camunda: ‘Decision Automation’, 2019, https://docs.camunda.org/getstarted/quick-start/decision-automation/ [18] Gennari, J.H., Musen, M.A., Fergerson, R.W., Grosso, W.E., Crubézy, M., Eriksson, H., Noy, N.F., and Tu, S.W.: ‘The evolution of Protégé: an environment for knowledge-based systems development’, International Journal of Human-computer studies, 2003, 58, (1), pp. 89- 123 [19] Thuan, N.H., Antunes, P., and Johnstone, D.: ‘Factors Influencing the Decision to Crowdsource: A Systematic Literature Review’, Information Systems Frontiers, 2016, 18, (1), pp. 47-68 [20] Vanthienen, J., and Wets, G.: ‘From decision tables to expert system shells’, Data & Knowledge Engineering, 1994, 13, (3), pp. 265-282 [21] Biard, T., Le Mauff, A., Bigand, M., and Bourey, J.-P.: ‘Separation of decision modeling from business process modeling using new “Decision Model and Notation”(DMN) for automating operational decisionmaking’, ‘Book Separation of decision modeling from business process modeling using new “Decision Model and Notation”(DMN) for automating operational decision-making’ (Springer, 2015, edn.)...

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  • ...More specifically, we rely on Camunda to manage decision tables and Protégé to manage ontologies....

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  • ...Empowered by Protégé, it enables end users to define classes, instances, attributes, and relations, which together provide definitional knowledge about a business....

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  • ...Camunda is used to manage decision tables, while Protégé is used to manage definitional 978-1-7281-5009-3/19/$31.00 ©2019 IEEE 132 knowledge....

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Journal ArticleDOI
TL;DR: A critical examination of the substrate of crowdsourcing research is presented by surveying the landscape of existing studies, including theoretical foundations, research methods, and research foci, and identifies several important research directions for IS scholars from three perspectives—the participant, organization, and system—and which warrant further study.
Abstract: Crowdsourcing is one of the emerging Web 2.0 based phenomenon and has attracted great attention from both practitioners and scholars over the years. It can facilitate the connectivity and collaboration of people, organizations, and societies. We believe that Information Systems scholars are in a unique position to make significant contributions to this emerging research area and consider it as a new research frontier. However, so far, few studies have elaborated what have been achieved and what should be done. This paper seeks to present a critical examination of the substrate of crowdsourcing research by surveying the landscape of existing studies, including theoretical foundations, research methods, and research foci, and identifies several important research directions for IS scholars from three perspectives--the participant, organization, and system--and which warrant further study. This research contributes to the IS literature and provides insights for researchers, designers, policy-makers, and managers to better understand various issues in crowdsourcing systems and projects.

535 citations


"A Tool for Modelling Business Behav..." refers background in this paper

  • ...Yet, it is a complex decision for organisations, since several contingency factors should be examined [19, 28]....

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  • ...Seventeen relevant decision factors influencing the decision to crowdsource have been identified [19, 28]....

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Frequently Asked Questions (1)
Q1. What have the authors contributed in "A tool for modelling business behaviour using decision tables" ?

This paper fulfils this gap by proposing a tool supporting end users to capture and model business behaviour.