=Paper=
{{Paper
|id=Vol-2454/paper_43
|storemode=property
|title=ODD-BP - an Ontology- and Data-Driven Business Process Model
|pdfUrl=https://ceur-ws.org/Vol-2454/paper_43.pdf
|volume=Vol-2454
|authors=Eric Rietzke,Ralph Bergmann,Norbert Kuhn
|dblpUrl=https://dblp.org/rec/conf/lwa/RietzkeBK19
}}
==ODD-BP - an Ontology- and Data-Driven Business Process Model==
https://ceur-ws.org/Vol-2454/paper_43.pdf
ODD-BP - an Ontology- and Data-Driven
Business Process Model
Eric Rietzke1 , Ralph Bergmann2 and Norbert Kuhn1
1
University of Applied Sciences Trier, Germany
2
University of Trier, Germany
Abstract. Many researchers have addressed the demands of knowledge
intensive processes and often propose a data-oriented workflow approach.
Others use ontologies in the periphery of workflow management systems
to achieve different kind of contributions, while just a few research utilize
the ontology for a semantic process definition.
This paper introduces a new approach which combines both perspec-
tives to define an ontology- and data-driven business process model. The
data-driven process characteristic is formed by a metamodel, placed in
the base-ontology as the core of the conceptualization. These core con-
cepts are expanded by more specific concepts to build a domain oriented
framework for the enterprise and process knowledge. Aligned with an
example, we will explain how process definitions are represented in the
knowledge store and examine the gradual transition of an executable
process instance. As a result, the ODD-BP approach takes advantage of
a declarative data-oriented process model regarding flexibility, while the
semantic process definition reduces ambiguity and builds the foundation
to drive the process execution through inference mechanisms.
1 Introduction
Compared with the established and well known BPMN approach, the new ap-
proach introduced with this paper appears to be odd, but despite this coinci-
dence, ODD-BP stands for ontology- and data-driven business process approach.
The motivation arises from the research about knowledge intensive processes
(KiPs) with its data-oriented character [5] and the general search for AI-support
within business processes. Established approaches often place data as a third
class citizen into the process definition, sometimes expressed as an afterthought
[4] within a classical control-flow oriented model. KiPs are knowledge- and data-
centric and require flexibility at design and run-time [3], especially regarding
their major resource, the knowledge workers. They should be supported by of-
fering opportunities rather than restrictions, an accompanying system should
deliver choices and recommendations and access to relevant information to ac-
complish a contribution during the process execution [3, 5].
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 E. Rietzke et al.
Artificial intelligence (AI) in all its facets and across the different kind of
technologies are discussed in a wide range of use-cases as well as it is in the focus
of research in the BPM-context. No matter if an AI contribution is delivered by
the workflow system itself or by an external agent, when it comes to a division of
labour between human- and cyber actors, the process must be described without
ambiguity and understandable for all process participants. An ontology is the
perfect tool to achieve this requirement and can be used for a semantic process
definition according to Fellmann [24].
The combination of both leads to an ontology and data driven ODD-BP
approach as it is described in this paper. The data-driven character is formed by
a metamodel defined in the base-ontology as the core of the conceptualization.
Aligned with our research project SEMANAS3 , we examine the demands of
knowledge intensive process in the domain of agricultural grant applications and
expand the base-ontology by a domain-ontology focused on this specific use-case.
Section 2 is referring the foundations of our work, while section 3 intro-
duces the metamodel for our new approach. The application of the metamodel
is described in section 4, while additional transition rules expand the conceptu-
alization to ensure a valid execution of process instances. Section 5 gives a short
insight into our current development of a process design-tool. With the conclu-
sion in section 6, we give an outlook about the possibilities and advantages of
the ODD-BP approach and our future work.
2 Foundations
Business Process Model and Notation (BPMN) is currently the de facto stan-
dard for designing and describing business processes world-wide. In the center
of this approach reigns a control-flow coordination of process steps (activities).
A less restrictive, but still activity-centric perspective is provided by constraint-
based approaches [19], which allow flexibility in a scalable manner. Alternatively,
there are several approaches with the intention to gain flexibility based on the
control-flow principles [23, 16]. Despite of the consideration of data-flow in such
processes, the data is just integrated in a kind of an afterthought [4, 11]. Op-
posed to this, knowledge-intensive processes (KiPs) are usually barely structured
and their execution is driven by user decisions and business data. Previous re-
search has shown [25, 2, 17] that an activity-centric perspective is not sufficient
to support knowledge-intensive processes.
With view to these insights, several new approaches were brought up during
the last decade, putting the data into the center. The case handling paradigm [25]
elevated the result of a process (case), reflected by its data objects; activities do
not longer drive the process but serve the outcome. For more complex scenarios
with the need of abstraction capabilities, object-awareness approaches refined
the case handling concept[26]. With business artifacts [1, 2], CorePro [17], and
PHILharmonicFlows [15] there are even more approaches to mention, which
3
SEMANAS is funded by the Federal Ministry of Education and Research (BMBF),
grant no. 13FH013IX6, duration: 2017-2021
Ontology- and Data-Driven Business Process Model 3
underlines the importance of data-centric approaches for knowledge intensive
processes.
Beside the examinations of different workflow models and principles, the pos-
sibilities of semantic information systems in the domain of BPM are matter of
current research as well. S-BPM introduced a subject-oriented modeling scheme,
where sentences with subject, predicate, object are used to describe general in-
teractions between process actors [8]. This profound methodology addresses the
communication aspects between process actors, but offers no specific strategies
regarding the demands of KiPs. The initiative of WSMO (Web Service Model-
ing Ontology) uses ontologies to formalize the interoperability of web services
[7] and thus targets a service orchestration purpose. Some work considers how
a formal semantic can be utilized for process validation [20] or optimization [9]
purposes. Opposed to this, Thomas and Fellmann [24] introduced an ontology
based representation of business processes in which process elements are assigned
to ontology classes to define a control-flow oriented metamodel. Further research
also uses semantic process modeling to define control-flow oriented approaches
like [12, 13]. In general, the research about semantic process definitions is mo-
tivated by the reduction of ambiguity [6, 24] and the opportunity of inferencing
new knowledge [14] on base of the existing process knowledge.
The research about semantic formalizations within the field of business pro-
cesses defines the foundation for our work, but to the best of our knowledge,
no research addresses a semantic process modeling principle for a data-oriented
workflow approach to support the definition and execution of knowledge intensive
processes. The general idea for this approach was already introduced through
our precedent work [22, 21] and will be carried forward with this paper.
3 Metamodel
According to a wide range of publications [10, 18], a metamodel defines ”the
frames, rules, constraints, models and theories applicable and useful for modeling
a predefined class of problems.” Knowledge intensive processes (KiPs) and their
specific demands [5] can be considered as such a predefined class of problems. We
define the ODD-BP model, a workflow metamodel aligned to the requirements
of KiPs, utilizing the underlying ontology to provide semantic and data-oriented
process definitions.
3.1 Mapping between Knowledge Store- and WfMS-Structure
Any workflow management system follows a metamodel, usually implementing
one of the workflow-approaches mentioned in section 2. From a most fundamental
perspective, all WF-approaches have in common, that they use a set of activities
to achieve a specific goal. The metamodel defines the kind of process elements
and their possible interactions and based on this model, a process definition
(PD) is specified for each kind of process goal, acting as a blueprint for process
instances (PI) which can be executed to achieve a specific goal of a certain kind.
4 E. Rietzke et al.
All established workflow approaches follow this general WfMS-structure built on
a metamodel, process definitions and process instances (Fig. 1).
Fig. 1. Modules of the knowledge store and the relations to the WfMS-structure
The knowledge store (KS) is the combination of the ontology (T-Box) as
the conceptual fundament and the triple store (A-Box) as the data storage.
The base ontology defines the metamodel 1 of the ODD-BP approach, it is
domain independent and will be introduced in detail in 3.2. The domain ontology
expands the conceptualization by defining general valid concepts and relations
of a domain. Further ontologies can expand the conceptual knowledge, like a
document ontology, and form together the enterprise ontology, the fundament
2 of all process definitions and process instances. A process definition or a
process instance is defined by a set of linked individuals according the concepts
and relations of the metamodel. Such a set represents a process graph and is
stored as triples 3 in the A-Box.
3.2 Base-Ontology
The base-ontology defines all concepts and relations to build the metamodel of
the ODD-BP approach. The most fundamental artifacts of a process are Tasks,
Dataobjects, Documents and Actors. Individuals of these concepts can be con-
nected with an individual of the class Process through a contains or involves
relation, as it is shown in Figure 2.
The mentioned artifacts are usually represented in one or another way in all
workflow approaches. The specific character of a metamodel is manifested by
the kind of relations between these artifacts and in this case the data-oriented
character of the ODD-BP approach is created by the relations between Tasks at
the one side and Documents, Dataobjects and Attributes on the other side.
According to this metamodel, a Document can be demanded by a Task as
input or a Task can produce a Document as the outcome of its execution. Analog
to this, a Dataobject or an Attribute can be required by a Task as input or a
Ontology- and Data-Driven Business Process Model 5
Fig. 2. Base-Ontology
Task can deliver such an element as output. The deeper meaning of Dataobject
and Attribute will be explained more in detail in the following, but the general
importance of data for a process execution is obvious.
Unlike these direct relations between the named concepts, the possible rela-
tions between Actors and Tasks are not defined in a direct manner, but through
specialized concepts. This allows a precise definition of different kind of Actors
and Tasks and their individual relations. Corresponding to this, a cyber actor
(labeled as Agent) can perform a System Task. A Role can just be assigned to
a User-Task, while a User can execute such a User-Task, if the User is allowed
to play the corresponding Role.
All in all the metamodel defines, how a process can be designed and executed
and these general rules are the same for process definitions PD and process
instances PI, which are defined as a specialization of the concept Process. A
process is modeled and described by individuals of the introduced concepts and
by links between these individuals according the relations of the base ontology.
3.3 Data-Orientation and Data-Driven
The general data-orientation of the metamodel can be seen by the manyfold
relations between Task and the data-carrying elements Dataobjects, Attributes
and Documents, in certain ways similar to artifact centric approaches [1, 2, 4].
However, the ODD-BP approach is not only placing data into a more central
position, the data is integrated with the intention to drive a process.
6 E. Rietzke et al.
Usually an information system organizes data about the real world with en-
tities and relations. With view to databases, entities are managed as entries into
a table, while a knowledge store is managing entities as individuals of a certain
class. Such an individual represents an object of the real world, while its object-
characteristics are stored as data-properties of the individual. This realization
lacks in expressiveness, as the knowledge store can not express dependencies be-
tween tasks and data-properties and thus, the data-properties could not be used
to drive the process.
This leads us to a conceptualization in which object-characteristics are rep-
resented through a separate concept, Attributes. They can be understood as
key-value pairs, while an individual of this type is representing a single charac-
teristic of an entity. With view to the example shown in Fig. 3, the individual
Person is a dataobject to represent a specific person in the real world. The
birthdate and the adult state are represented by 2 separate individuals of the
base-type Attribute, linked with Person through a consist of relation.
Fig. 3. Process visualization of a data-driven process segment
Assuming that a system task Is Adult can decide autonomously if a Person
can be seen as adult or not. The relation between dataobject and task would
usually just be expressed at an abstract and more general level 1 . Through the
additional conceptualization, the relation between the system task Is Adult and
the Attributes 2 can be modeled more in detail. This offers a more specific pro-
cess definition which allows to deduce the executability of tasks by an inference
engine using the linked (input-)attributes with a required by relation. Beside the
pure executability, such activities can deliver a more or less important process
contribution to achieve a process goal which can be deduced using the linked
(output-)attributes with a delivers relation.
Beside the entities itself, an information system also allows to express re-
lations between entities. The base-ontology (Fig. 2) defines two different base-
types to express such relations. The has a relation is intended to define any kind
of global valid relations between entities. Within a further conceptualization
through a domain ontology, specializations of the has a relation can be used to
form an enterprise information model, equivalent to an ER-model of a database.
Ontology- and Data-Driven Business Process Model 7
The composed by relation is the prototype of a more process dependent combi-
nation of entities, not valid from a general perspective, but valid and required
within a process context. It can be used to combine different kind of dataobjects
for a process definition and process execution.
3.4 Semantic Integration of Documents
Data can come from different sources like an information system, from a user or
cyber actor (through a process or system task) and by documents, which usually
means data-exchange with external systems or users. From this perspective, a
document can provide dataobjects (Fig. 3) within a process context. According
the introduced design to represent entities, a document can also provide at-
tributes of dataobjects. These provide relations are also introduced by the base
ontology (Fig. 2) and build the foundation for a semantic integration of docu-
ments into a process context. This topic is object of our current research and
expands the possibilities of the ODD-BP approach even further.
4 Application of the Metamodel
The introduced metamodel defines the vocabulary, which allows a process de-
signer to express a process definition for a certain process goal. The data of
process definitions and process instances as well as any data of the information
system is part of the knowledge store and is persisted as triples in the triple-
store, the A-Box. Since any process-element within a process definition is just
used to form a process template, these elements are placeholders and with view
to a process instance, such placeholder elements will be replaced by meaningful
elements during the process execution. To express this, the base-ontology (Fig.
2) also defines a placeholder concept. Any process-element of a PD is represented
by an individual assigned to one of the introduced concepts (task, dataobject,
attribute, document) and additionally assigned to the placeholder class. This
serves the inference mechanism to deduce the executability of activities as we
mentioned before and which will be addressed in detail by a separate paper.
4.1 Process instance
A process instance is initially nothing else than a copy of a process definition
with all its placeholder elements. Along the execution, the process instance per-
forms a gradual transition from a process description defined only by placeholder
elements to a final process state, where some or all process elements are mean-
ingful elements. This gradual transition must follow some rules, but since the
process descriptions of PD and PI are defined by a set of individuals in the A-
Box, the rules can not be expressed using the conceptual layer of the knowledge
store. In the following we will introduce these rules which define valid struc-
tural changes between a modeled process definition and corresponding executed
process instances.
8 E. Rietzke et al.
4.2 Multi-instantiation
A PD defines a template to achieve a process goal like an application for a
group event. In such a PD, a single individual of the classes dataobject and
placeholder is representing the applicants, not knowing how many applicants
will finally participate in a PI. To express this aspect of a multi-instanciation,
the metamodel must offer the option to define the cardinality for each process
element (PE). Since the placeholder PEs will be replaced during the process
execution, the right place to persist the possibilities of the cardinality is at the
link between the individuals of the process p and the process element pe. For
this purpose, the contains relation is expanded by an annotation, which allows
each link of this type to define the cardinality by a list with 2 numbers (n, m).
– n ∈ N0 defines the minimum occurrence of a process element,
while n = 0 defines an optional occurrence within a PI.
– m ∈ N0 defines the maximum occurrence of a process element,
while m = 0 defines an unlimited occurrence within a PI.
– In the following, the cardinality will be expressed with an additional label
contains
at the contains relation as: p −−−−−→ pe
(n,m)
The multi-instanciation only defines the possible occurrence of PEs. It tells
nothing about the way, how such PEs interact with each other. This requires a
further extension to express the intended transitions between PEs.
4.3 Transition rules
According the metamodel, Tasks, Documents and Dataobjects are connected
with a Process through the contains relation. The interplay between individ-
uals of these three concepts are defined by the five relation types provides,
demanded by, produces, required by and delivers. Analog to the ontology, two
connected process elements can be seen as a process statement with subject,
predicate and object, while subject and object are individuals of the three men-
tioned concepts and predicate is a link of the five mentioned relations. Such a
process statement defines an action (according the predicate) from a subject to
the object. Since subject and object can be defined with a different cardinality,
the predicate must be extended with an annotation how the multi-instanciation
can be processed to perform the transition from placeholder PEs to meaningful
PEs. We can differentiate in three transition rules:
– expand: Each subject can result (along the predicate) in one or many objects.
– maintain: Each subject can result (along the predicate) in exactly one object.
– join: All subjects can result (along the predicate) in exactly one object.
– In the following, the transition rules will be expressed with an additional
predicate
label at the predicate as: pe −−−−−−−−−−→ pe
(transition−rule)
Ontology- and Data-Driven Business Process Model 9
Fig. 4. Multi-instantiation and transition rules
As an example, Figure 4 shows a small process segment of a group appli-
cation with different cases of multi-instanciation and transition rules. The left
side presents the process definition, where the links of the contains relation are
annotated with the definition for a multi-instanciation, while the other links are
annotated with the transition rules. The PD defines, that exactly one (1:1) Group
Application is allowed, it allows at least 2 and a maximum of 6 Persons (2:6),
any number of Is Adult tasks and just exactly one task Group Total Age. These
four process elements are linked through provides and required by relations with
the three different transition rules (expand, maintain, join).
The right side presents a corresponding process instance, where all process
elements are transformed from placeholder PEs to meaningful PEs, according the
introduced rules. The Group Application has provided two Persons, which fulfills
the demand of the contains relations and follows the expand transition rule. It
is required, that each Person must be an adult and the maintain transition
rule is defining, that for each Person an own Is Adult task must be executed.
Additionally, the total age of the group is needed, which is why both Persons
are linked with just one Group Total Age activity according the join transition
rule.
5 Designing a Process
The metamodel defines the vocabulary to describe a process and through the
introduced rules regarding multi-instantiation and transitions, also the semantic
dependencies between process elements can be expressed. For a practical use
and to design and execute ODD-BP definitions and instances, a modeling tool
is required.
As a first step towards a POIS, we started the development of a graphical
toolset on a web-based client-server architecture. It aims to support a process
designer by utilizing the metamodel to turn the ontological restriction into an
easy to use graphical user-interface. Beside this, it also sets the outer ontological
restriction into action.
10 E. Rietzke et al.
Fig. 5. ODD-BP-Designer
Fig. 5 shows our ODD-BP Designer with the process segment from Fig 3.
The small + symbol 1 allows to add valid process elements to the current PD
according the base- and domain-ontology, actually all elements which can be
connected to the PD through a contains relation. The editor allows to connect
process elements 2 like the dataobject Person and the system task Is Adult,
also utilizing the conceptualization. The delivers relation between Is Adult and
Person can be specified more in detail by connecting the task with specific
attributes of the Person like the attribute Adult 3 .
The editor is highly generic since the possibilities to form the process is
ascertained through the ontologies. Beside the visual interpretation of the process
definition, the tool is taking care for a consistent process model by realizing the
outer ontological restrictions. As an example: As soon as the user is deleting
the linkage between Is Adult and Person, the editor will also remove all links to
attributes of the dataobject Person. The ontology defines no limitations for this
case, which is why the editor has to fill this gap to prevent inconsistency.
6 Conclusion
AI technologies in general can deliver a wide range of contributions within the
field of business processes and even semantic information systems can be used in
different ways. The introduced ODD-BP approach combines the principles of se-
mantic process definitions [24] with a metamodel which implements a declarative
and data-oriented process character. Thus, it reduces ambiguity and supports
the division of labour between human and cyber actors and takes advantage
of a data-oriented approach according the demands [5, 3] to design and execute
knowledge intensive processes. Through the renunciation of control-flow prin-
ciples and our focus to a descriptive process model, we gather the advantages
Ontology- and Data-Driven Business Process Model 11
of none imperative approaches [11, 19], regarding flexibility during the process
execution.
One central motivation is to utilize the ontology and the data-oriented meta-
model to drive the introduced approach, which leads us to the acronym ODD-BP
approach. This requires a precise definition of the interplay between data and
activities and we have shown, that the conceptualization of Attributes through
the metamodel expands the expressiveness of a process definition and defines the
base for different kinds of cyber process contributions. Within a separate paper
we will show, that a process definition on base of the ODD-BP approach can be
used to deduce the executability of activities. Further more we will show, that
even the relevance of an activity can be deduced according its contribution to
reach predefined process goals and process milestones.
There is a wide range of further possibilities to take advantage of the ODD-
BP approach. As such, the explainability of the inference while deducing exe-
cutable tasks could be utilized to adapt a process instance according an identified
problem. The semantic process definition could also be used for an adaptive pro-
cess visualization as it was already introduced [22, 21] and which is object of our
ongoing research. Finally, the practical use of our new approach within different
knowledge intensive application scenarios must be examined as well.
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