This paper introduces a novel approach which uni es a datacentric and a constraint-based work ow principle. This uni ed approach o ers a scalable exibility during the process execution and supports the requirements of knowledge intensive business processes. By the integration of a knowledge-based system, process de nition and execution relevant data coincide on an ontology-based semantic net. The data, mainly driving the process, can be delivered by di erent sources or can be the result of an inference step by the underlying ontology. In such a case, AI technology plays an active role during the execution of processes and results in a division of labor with human actors. Such an AI contribution in the process execution must be presented explainable to the user and for a common understanding, this paper presents a concept for a business process visualization adapted to the introduced uni ed approach. Established strategies for the adaptation of process views are under examination and new strategies will be presented to utilize the integrated knowledge-based system for a semantic oriented process visualization.
In today's business, Process-Aware Information Systems [
In many applications, a process is presented more or less in the same way as
it was designed by the process designer. This usually does not t to the speci c
needs of a user for the execution of an activity. Previous research has addressed
these aspects [
Current BPV approaches are developed for an activity-centric (usually imperative) work ow principle where the activities are directly related to each other and form a control- ow or constraints de ne some rules for their execution. However, with view to the demand of knowledge intensive processes for
exibility at design- and run-time [
Such data-centric approaches come along with characteristics, tting very well to an ontology based data management and form the preconditions for further intelligent process contributions. In this way, the process data can be used to create new information by simple inference mechanisms, exploiting the accessible knowledge. Moreover, the semantic description of the relations between data and activities can be utilized for a sophisticated process visualization as it will be shown within this paper.
We do not only consider process visualization as a possibility, but as well as a requirement. As soon as AI techniques play an active role in the execution of a process, the division of labor with human actors requires a common understanding of the process subjects. The business process visualization is the connector in this human-machine communication.
In the following we present a BPV approach based on a knowledge-based system. Therefore, section 2 introduces the elementary work ow principles. Additionally, the state of business process visualization concepts is presented. Section 3 motivates the model for a uni ed approach for business process modeling and execution. The capabilities are explained in detail by using an example. Section 4 introduces a new concept of a semantically oriented BPV for the unied work ow approach. Our motivation is expressed and the requirements, the architecture as well as view adaptation methods are presented. We conclude our paper by giving an outlook on our future research in Section 5. 2
In the following, we brie y summarize relevant previous research related to business process modeling, execution, and visualization. 2.1
Business Process Model and Notation (BPMN) is up-to-date the de facto
standard for designing and describing business processes world-wide. In the center of
this approach reigns a control- ow coordination of process-steps (activities). A
less restrictive, but still activity-centric perspective is supported by
constraintbased approaches [
With view to these insights, several new approaches were brought up during
the last decade, putting the data into the center, not only for the design but also
for the execution phase of the processes. The case handling paradigm [
The existing work ow principles can be di erentiated regarding the rationale for selecting activities for execution. Under a control- ow, the activities are chosen for the execution primarily by the connected ancestors, while a constraintbased model selects the activities by considering a set of restrictions. Both principles put the activity into the center of the view. This changes with the data-centric approaches, where activities are executable as soon as the necessary information is available and the expected outcome on a new information is still required for further process activities. This is expressed by the taxonomy of work ow principles, shown in Fig. 1.
The data-centric as well as the constraint-based concepts have in common that in both cases the relations between objects are described, while the execution order is deduced on the y. This represents a declarative work ow de nition, while the control- ow explicitly describes the execution order showing its imperative character.
The nature of both declarative principles is their inherent exibility.
Nonetheless there are major di erences. With a data-centric approach, possibilities for
the execution of activities are described, which support the requirements of
knowledge intensive processes [
The importance of visualization within all elds with a human-computer
interaction is well established [
The most established toolset to model business processes (BPMN) comes
along with a detailed graphical notation de nition. Graphs are built during the
modeling phase and are usually used directly, when it comes to a process
visualization later on. It represents the perspective of a process designer, which
in general does not t to the demands and needs of a process controller or an
actor during the process execution. Additionally, since the modeling procedure
is done to create a process template, the temporal situation of a process instance
is usually just re ected by a state presentation and has no structural impact on
the graph. An example of structural changes according to the process-state was
introduced in Proviado [
One purpose of a visualization is that the view should support the viewer in the
best possible way to ful ll his/her tasks. Sophisticated business process
visualization approaches [
The users in a business process are playing a certain role like: a process designer, a manager, a process controller, or a process actor. Depending on their role, users have di erent user perspectives on a process to ful ll their individual tasks, which makes it to an important factor for any process adaptation.
Another factor for visualizing a process instance is the process-state being the result of the state of each process element. The importance of each element for a process visualization is mainly a ected by its current in uence to the process execution.
Both factors are inherent to the situation (user perspectives and processstate) and cannot be controlled by the user directly. If a visual presentation shows a process in a modi ed way, the user might want to have in uence on this presentation by adding his personal focus to the view. This can be expressed by an interaction with the BPV and adds a third visualization factor.
These three factors claim an in uence on a process visualization with di erent reasons and describe the points of interest for an adapted BPV.
Both, the mentioned research about business process modeling and execution and also the visualization approaches build the foundation for our work and lead us to the new concepts presented in the section 3 and 4. 3
In the following we will introduce a uni ed approach which o ers a high exibility
during a process execution and serves the demands for knowledge intensive
business processes [
As described in section 2.1, the data-centric and the constraint-based approaches
follow the same declarative paradigm. In this work, we argue that both
principles can be combined to a uni ed approach. We expect that this will o er a
seamless scalability regarding exibility and strictness, from an unstructured
process task-list up to a narrow restrictive process model. The approach would
also serve the demands of knowledge intensive processes by integrating data as
a rst-class citizen into the process [
With the uni ed approach, we do not only allow data to in uence a process
instance but to directly control its execution. Like in similar approaches [
Nonetheless, data has an essential in uence on the process execution and
can be delivered by manual activities, system activities, external sources like
documents [
Figure 2 shows a segment of an order process which delivers the information, whether a customer should be served on account or whether an upfront payment is required. The most important part in this example is the required information (Upfront payment), which is represented by a tristate value (true, false, unknown). All data elements are shown as a circle while the activities are presented in rectangles. Activities can be distinguished between manual activities like decide credit ranking manually and service activities. Edges between data and activities de ne the direction of the data ow. The edges between activities represent additional constraints. The transfer of an activity result can be obligated (solid lines) or optional (dashed lines).
The process is mainly data-driven as the activities depend on the input and
the output data. This means that the activities are executable if the potential
output data (Upfront payment) is still required and as soon as the necessary
input data (Order value, Customer) is available. Since two activities can provide
the required output, an additional precedence constraint [
The example has presented the data-oriented enactment of activities of the uni ed approach. In the execution, it follows the information-state rather than a prede ned control- ow and o ers a high degree of exibility. To supervise the exibility, constraints are used to guard prede ned compliance rules.
The brie y presented uni ed approach combines the two declarative paradigms with a knowledge-based system. Besides the possibilities for an arti cial process contribution, the ontology will be utilized for the following BPV approach. 4
As presented in section 2.2, so far the research for adapting business process
views focuses on control- ow oriented approaches like BPMN. Some studies have
introduced adaptation techniques [
In general, the proposed uni ed approach requires a process visualization which allows to explore and identify the expected possibilities and allows the di erent groups of users to understand the process in its de nition and its behavior during the execution. Primarily we see this demand for three user-groups, each with its own purpose: { The process designer requires a view supportive for the process de nition. { The process controller requires a view which allows e ciently to identify delay in the process execution or potential risks. { The process actor requires a view that serves best by the execution of single activities.
Beside the fact, that in the presented approach the relation between data and activities has a dominating role and a new visualization approach is required, the integrated knowledge base o ers also new opportunities for a process view. The point of interest can be ascertained for each of the named groups and an adapted process visualization can be generated for each purpose. By utilizing the ontology, we pursue to create a view which presents the semantic relations between data and activities rather than just taking advantage of an activity arrangement by an imperative approach.
Considering the possibilities for deduced information mentioned above, humans have the need for a comprehensible presentation of the arti cial process contribution. Since such new information is deduced based on the ontology, the source can be determined and the explanation can be presented by the visualization. 4.2
The three introduced visualization factors (section 2.3) user perspective, processstate, and personal focus build the foundation of our visualization concept and de ne the individual points of interest together. These points of interest (explained in detail in the next section 4.3) and the knowledge base will be utilized to generate an adapted process view. Once the visualization is shown, three possible changes can take e ect and will result in an updated process view. The user can change the process data directly or by performing an activity. The user can express his interest for a process element, which will change the personal focus. Finally, an external event (other user, system activities) can occur and the process data is changed as well.
To a large extent, this concept is similar to existing approaches. Even the
user perspective and the view generation[
A point of interest (POI) represents a process element which has some particular importance for the process visualization. Since the uni ed approach follows the data-oriented principles, a process element can either be an activity or a dataelement. Any element which was recently changed or which is waiting for a change like executable activities has some elevated importance. With view to the user perspective, any activity which is assigned to the current user has also an elevated importance. If both factors (user perspective and process-state) coincide on the same point of interest, this can increase the importance even further. Additionally, through the personal focus the user can express his impression of importance. Thus, we get a list of points of interest (POIs) which lter the most important data and activity elements for the following process presentation.
The relevance of each further element depends on the individual meaning related to each POI. This meaning is re ected by the integrated knowledge base and the ontology can be utilized to calculate the relevance for each element. In a nutshell, the closer an element is related to a POI and the more meaningful the element is for the execution or understanding of the POI, the higher is its relevance. Such a relevance value can be used for each activity and data element for the following process adaptation. 4.4
The view adaptation is the central transformation step of the view generator.
With di erent techniques like reduction and aggregation, single elements up to
process segments can be transformed and thus can be presented with a variable
granularity. The most relevant elements can be presented in the highest level
of detail while the less important elements can be presented in a more abstract
view with a lower granularity. In the following, di erent adaptation methods
are introduced, which are partially already examined [
Reduction: One possibility to lower the granularity of a segment is the reduction step where single elements are taken out of the view. In existing BPV approaches often system activities where reduced with the expectation that they are less important than user activities. With the introduced approach, the knowledge base is used to calculate the individual relevance in semantic relation to the POIs.
Assuming that a speci c data-element (D1) has just enough relevance for an unchanged presentation, the producing activity of this information might be not important enough and thus maybe reduced from the presentation. Unless the user is not clicking on D1 and thus increasing the relevance of this element by adding his personal focus, the producing activity remains invisible in the process visualization. Alternatively, only the producing activity of the speci c information is presented and all alternative potential sources, like activities, which were not executed, are reduced.
Aggregation: Another option to reduce the granularity is to aggregate a group of activities or data elements to a single representative element. Unlike a reduction step where the elements are completely removed, the representing symbol is still visible and accessible and the user can expand it to the ne granularity any time. In existing BPV approaches, the aggregation step is used to combine activities which follow a narrow route within the control- ow and do not split to further activities. Under this new approach, elements can be grouped and aggregated by a similar meaning. For example, data-elements with the same type of relation to a common activity can be replaced by a single element, which substitutes the individual data-elements in the visualization.
Assuming that several data-elements (D1, D2, D3) were required for an already executed activity (A1), these data-elements share the same type of relation to the activity (A1). By replacing the data-elements with a single element (D1-3) the granularity of this process segment can be reduced.
Expansion: The most obvious way to adapt a view is lowering the granularity of process segments. However, with view to the underlying ontology and as described in section 3.1 with the uni ed approach, new information can also be deduced by inference steps. Referring to the example in section 3.2, the creditworthy might be stored in a global knowledge store. This source of information is not represented in the process as an explicit activity, nonetheless it has some impact to the process execution. With the method of expansion, the process view can be expanded by further elements which are de ned in the ontology and which are not an explicit part of the process de nition.
Assuming that a data-element (D1) is deduced from existing data (D2) by utilizing the relations de ned by the ontology, the process view can expand D1 with a further data-element D2 by representing the common relation through an additional edge.
The basic idea of the view adaptation is to gain attention for the important elements of a process according to the POIs. With reduction and aggregation, we presented two methods to lower the granularity of process segments to achieve a simpli ed visualization by keeping the details of the important elements. With expansion, we presented a method to add further details to the important elements to get transparency for an arti cial process contribution. For these three adaptation methods as well as for the calculation of the relevance of each process element we have utilized the integrated knowledge base. 5
In this paper, we have shown that the data-centric as well as the constraintbased approaches follow the same declarative paradigm and can be combined to a uni ed approach. For this, we expect a scalable exibility, see the demands of knowledge intensive processes ful lled, and can use constraints to de ne explicit restrictions to satisfy compliance rules. Through the integration of a knowledge based system, arti cial intelligence methods should be able to participate directly in a process execution. This allows a division of labor between humans and an AI system. For a common understanding and a comprehensive presentation of the uni ed approach a new concept for business process visualization was presented.
Referring to existing research we have considered three visualization factors (process-state, user-perspective, personal focus) to determine the points of interest in a process instance. These POIs are used as a guideline and by utilizing the integrated knowledge base, the speci c relevance for each process element can be calculated. With the help of three methods (reduction, aggregation, expansion) the granularity of process segments can be adjusted according to the calculated relevance. The adaptation of the process view is taking advantage of the integrated knowledge base and a semantically-oriented process visualization is conceivable.
In our ongoing work, we will implement the described uni ed approach in a prototype and will de ne the data-driven process based on an ontology. The described architecture will be realized to transfer a process instance to a visual representation. The methods for a process adaption will be realized to prove the concept of a semantically-oriented process visualization.