=Paper=
{{Paper
|id=Vol-3113/paper5
|storemode=property
|title=Decision Support for Knowledge-Intensive Processes
|pdfUrl=https://ceur-ws.org/Vol-3113/paper5.pdf
|volume=Vol-3113
|authors=Anjo Seidel,Stephan Haarmann
|dblpUrl=https://dblp.org/rec/conf/zeus/SeidelH22
}}
==Decision Support for Knowledge-Intensive Processes==
https://ceur-ws.org/Vol-3113/paper5.pdf
Decision Support for Knowledge-Intensive
Processes
Anjo Seidel and Stephan Haarmann
Hasso Plattner Institute, University of Potsdam,
Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
anjo.seidel@student.hpi.de, stephan.haarmann@hpi.de
Abstract. In knowledge-intensive processes, knowledge workers have to
choose from many actions those that align best with their objectives. This
is challenging since such a decision involves explicit and tacit knowledge
and may affect the future of the process in intricate ways. In other words,
they cause a high cognitive load. Using flexible case models, we present
an automated recommender system that determines the best possible
action for given key performance indicators. This supports knowledge
workers to accomplish their goals efficiently.
Keywords: Case Management · Decision Support · Recommendations
1 Introduction
Knowledge-intensive business processes (KiPs) are characterized as multi-variant
and unpredictable [2], calling for flexibility at design- and run-time [2]. Hence,
new modeling approaches have emerged, which are more declarative [11, 16] and
data-centric [3, 12, 13, 19] than traditional, imperative ones (e.g., such as BPMN).
With the help of an execution engine, modeled processes can be enacted [24].
At run-time, knowledge workers drive a case by deciding which of the possible
next actions to execute. These decisions are interconnected and knowledge-
intensive [22] and drive the process gradually towards its goal.
Due to the flexibility, knowledge workers may choose from numerous activities,
and the effect of a particular activity on the process outcome is not necessarily
apparent. This makes it difficult to plan the execution of KiPs, i.e., arranging
actions in a sequence leading to a certain goal. Planning, however, is characteristic
for knowledge work [17]. In KiPs, goals are typically defined by the knowledge
workers at run-time. This is called late goal modeling [2].
Different approaches of providing recommendations to support planning exist,
including predictive process monitoring techniques [4, 21] and decision support
via process simulation [18, 25]. However, both approaches cannot be applied to
KiPs, as these processes are unrepeatable and unpredictable [2].
Therefore, we propose a model-based approach for providing recommendations.
In [6], we already presented a solution to allow knowledge workers to define
objectives during run-time. Objectives describe desired case states. We aim to
J. Manner, D. Lübke, S. Haarmann, S. Kolb, N. Herzberg, O. Kopp (Eds.): 14th ZEUS
Workshop, ZEUS 2022, Bamberg, held virtually due to Covid-19 pandemic, Germany, 24–25
February 2022, published at http://ceur-ws.org
Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
Decision Support for Knowledge-Intensive Processes 21
analyze the model and the execution context to recommend how to reach such a
state. Two research questions emerge:
RQ1 What are the requirements for recommendations in KiPs?
RQ2 How can such recommendations be derived?
Our approach is based on fragment-based Case Management [10]. We analyze
the nature of KiPs and the requirements for late goal modeling. To provide
recommendations, we query the state space of a case model and search for
activities that most likely lead to desired states.
In Sect. 2, we present related work. The groundwork regarding fragment-based
Case Management and modeling objectives is elaborated in Sect. 3, while our
approach is elaborated in Sect. 4. We discuss the current state of work and future
research and conclude the paper in Sect. 5.
2 Related Work
KiPs are highly flexible and driven by the decisions of knowledge workers [2, 20].
Various approaches for modeling knowledge-intensive processes have been pro-
posed: some are declarative, like DECLARE [16] and Dynamic Condition Re-
sponse Graphs [11]. Others are data-centric, such as Guard-Stage-Milestone [12],
PHILharmonicFlows [13], and BAUML [3]. The survey papers by Di Ciccio et
al. [2] and Steinau et al. [19] provide an overview of knowledge-intensive and
data-centric approaches, respectively.
The limited support for data in declarative approaches and for activities
in data-centric approaches, calls for hybrid ones [1], one of which is fragment-
based Case Management [10]. This approach focuses on highly structured process
fragments that can be combined dynamically during run-time. It allows combining
imperative control flow and declarative data flow. Recent extensions define the
modeling of data associations [7], multiplicity constraints [9], and colored Petri
net semantics [5]. However, the models use implicit data flow to buy flexibility at
the cost of comprehensibility, challenging knowledge workers in planning actions.
Planning is an important task in knowledge work [17]. Marella et al. proposed
an approach for automating planning in business processes [14,15], which does not
apply to the knowledge worker-centric nature of KiPs. Wynn et al. and Rozinat
et al. provide decision support based on simulating business processes [18, 25].
As KiPs are unrepeatable and unpredictable [2], a non-repeatable simulation
provides only limited support. Furthermore, predictive business process mon-
itoring approaches aim at predicting the next actions to be executed [4, 21].
Those predictions are based on past executions, which, again, contradicts the
unrepeatable and unpredictable nature of KiPs.
The challenge of assisting planning KiPs remains open. First steps have been
made by providing a framework for knowledge workers to define objectives [6].
In this paper, We show how objectives can be used to derive recommendations.
22 Anjo Seidel and Stephan Haarmann
3 Backgound
Our approach is based on the fragment-based case management (fCM) approach.
Furthermore, this paper continues our work of allowing knowledge workers to
define objectives during run-time [6]. In the following, we provide an overview of
the fCM approach and our previous work regarding modeling objectives.
3.1 Fragment-Based Case Management
Fragment-based case management (fCM) combines imperative control flow and
declarative data flow [10]. In fCM, the process is composed of multiple fragments,
which are control flow graphs similar to BPMN models. Additionally, data flow
defines data requirements and operations of activities. It constrains how fragments
can be combined during run-time. An fCM case model furthermore includes a
data model, object behaviors, and a termination condition. The data model
consists of data classes, associations, and multiplicity constraints [5, 7, 9]. Each
data class has a state transition system defining the behavior of corresponding
objects. The termination condition specifies the goal of the process.
In the following, we introduce the exemplary case model for assessing and
deciding on insurance claims. A more detailed explanation of the example can be
found online1 .
Claim
F1 Risk
F2 F3 Assess-
F4
Claim Claim
ment
Claim
[received] [low] [received] [rejected] Assess-
[in [rejected]
question] ment
Risk Claim [created]
assess Risk decide Claim request create
risk on claim [in assessment
[in expert Assess-
claim [medium] [low] assessment question]
question] ment
received
Assess- Assess-
ment [improved]
Risk Risk Risk Claim ment
[re- [re-
[high] [medium] [high] [approved] quested] quested]
F5 F6 F7
Assess- Assess-
Risk Risk ment Advice Claim
ment Advice
[low] [approved] [approve] [approved]
Assess- [approve] [low]
[created] Claim
ment
[in
Claim [rejected] question] Risk Claim
review reassess Risk revise
[in assessment claim decision [in
Assess- Assess- [medium]
question] [medium] question]
ment ment
Assess- [approved]
[approved]
ment Advice Risk Risk Advice Claim
[improved] [reject] [high] [high] [reject] [rejected]
Fig. 1. Extract of fragments of the insurance claim handling process.
1
The detailed example is available at https://github.com/AnjoSs/DS4KiPs
Decision Support for Knowledge-Intensive Processes 23
The process starts with receiving a claim. The first fragment F1 is executed,
and a risk is assessed. Given the risk, the knowledge worker can decide on the
claim in F2. It can be accepted, rejected, or remain in question. A case in the
state in question must be reassessed. During the reassessment, multiple expert
assessments can be requested (F3 ), created (F4), and reviewed (F5). With the
resulting assessments, the claim can be reassessed (F6 ), and the decision on the
claim can be revised (F7 ).
Fig. 2. The data model and object behaviors of the insurance claim handling process.
The data objects are instances of the classes Claim, Risk, Assessment, and
Advice (see Fig. 2). Each claim can have one risk, and multiple expert assessments.
From a number of assessments, an advice object can be retrieved. A claim can
be in the states received, approved, in question, and rejected. A risk can be low,
medium, or high. However, it cannot be changed from low to high or vice versa.
An assessment can be rejected, created, then approved or rejected and improved.
An advice can be either to approve or reject the claim
3.2 Modeling Objectives
In [6], we present a framework for specifying objectives based on an fCM model.
Objectives are constraints on the state of a case. They can refer to data objects,
their relationships, and to activities.
A case includes data, described by a set of data object O and a set of links
L. Each object o ∈ O belongs to a class o.class and has an ID o.id and a state
o.state. A link l ∈ L is an unordered pair of data objects.
Furthermore, each case has a set A of activity instances, henceforth called
actions. An action a ∈ A is an instance of an activity a.activity. It has a state
a.state, which is either initial, control flow enabled, data flow enabled, enabled,
running, or terminated [10]. Furthermore, an action reads a set of data objects
a.reads and writes a set of data objects a.writes. By executing an action, the
state of the case (i.e., the sets O, L, and A) change. Using first-order logic, we
can express knowledge workers’ objectives using O, L, and A.
24 Anjo Seidel and Stephan Haarmann
The objective g1 , for example, requires an enabled instance of activity revise
decision reading an advice in state approve:
g1 ≡ ∃a ∈ A, ∃o ∈ a.reads :a.activity = (revise decision) ∧ a.state = enabled
o.class = Advice ∧ o.state = approve
Multiple objectives can furthermore be composed by defining a partial order
among them. It specifies the order in which the objectives need to be accomplished.
4 Recommendations for Knowledge Workers
With the opportunity to specify objectives at hand, the question is how to
derive recommendations for the knowledge worker. Our approach focuses on
analyzing the state space of the model itself. As the objectives are subject to the
characteristics of late goal modeling, knowledge workers have special requirements
for their recommendations. In the following, we elaborate on these requirements
and explain how to derive suitable recommendations from a case.
4.1 Recommendation Requirements
KiPs are emergent [2]. Thus, it is impossible to plan far ahead. Instead, recom-
mendations should focus on the immediate decision of choosing the next action.
Yet, decisions still need to be made by knowledge workers, as they may have
knowledge that is not part of the case state. To support workers, we calculate a
score for all possible next actions. Purely based on the model, the action with the
highest score aligns best with the objectives of the worker, i.e., it is recommended.
Objectives arise during run-time [9]. As the execution context may change,
new objectives arise, and existing objectives change or become obsolete [2].
A knowledge worker must be able to update their objectives during run-time.
Subsequently, recommendations can be calculated and actions can be (re)planned.
Weinzierl et al. [23] state that recommendations should be made w.r.t. to
key performance indicators, which can be derived from data objects or past
executions (i.e., event logs). In our approach, the key performance indicators are
combined into a path cost function. Constant costs for all paths are equal to
no cost function. Another simple implementation costs a path according to its
length (number of activities). In summary, we require two user inputs:
1. A set of objectives that need to be fulfilled in the future.
2. A path cost function representing meaningful key performance indicators.
The expected results of recommendations and the described user inputs
define the requirements of knowledge workers towards recommendations. RQ1 is
answered.
Consider our example from Sect. 3. The knowledge worker has specified the
objective g1 requiring revise decision reading an advice object in the state approve
to be enabled. Assuming the case is in a state in which the claim has state in
Decision Support for Knowledge-Intensive Processes 25
question, the risk is medium, two assessments are already approved, and no advice
exists yet. The tasks reassess claim and request expert assessment are enabled.
Now, a new objective g2 emerges. It requires revise decision to be enabled for an
advice object linked to at least three approved assignments.
Starting in the current state, the knowledge worker is interested in reaching
the objectives g1 and g2 . As a path cost function, the objectives should be reached
with as few activities as possible. Therefore, we calculate a corresponding score
for the next activities reassess claim and request expert assessment.
4.2 Deriving Recommendations
A business process model can be encoded into a planning domain [15], which can
be used to derive recommendations. For this purpose, we reuse fCM’s colored
Petri net formalization [5, 8]. It enables us to calculate and explore the model’s
state space, i.e., a directed graph consisting of all states and state transitions.
We calculate the scores for actions as follows (cf. Alg. 1): For each action, we
start a breath-first search in the target state. We search for paths that result
in a state satisfying the knowledge worker’s objectives. For each such path, we
calculate its costs. The inverse of the cost is added to the action’s score. The
rationale behind this scoring function is “if more cheap paths satisfying the
objectives exist, the score of an action is higher.” In other words, an action scores
higher if it is likely to lead efficiently to a state, where all objectives are satisfied.
Algorithm 1 The score evaluation for next activities
function retrieve recommendations(current state, objectives, path cost function)
action scores ← [ ]
Q ← queue(next(current state))
while Q is not empty do
current path ← Q.pop()
if objectives hold in current path[last] then
action scores at current path[0] += 1 ÷ path cost function(current path)
else
for next action in next(current path) do
Q.push(current path.append(next action))
end for
end if
end while
return action scores
end function
The presented algorithm provides a solution for deriving recommendations
according to their requirements. It addresses and answers RQ2.
Considering the example, in the current state, reassess claim and request
expert assessment are enabled. For both, a score is computed how likely they
efficiently lead to a state, where g1 and g2 hold. All paths that start by executing
reassess claim create an advice with only two assessments. This does not suffice to
satisfy g2 . A new advice would need to be created with three or more assessments.
On the other side, by executing request expert assessment, it is possible to create
26 Anjo Seidel and Stephan Haarmann
and review a new assessment, and to create the advice based on three assessments
directly. There are shorter paths starting in request expert assessment than those
starting in reassess claim. Therefore, Alg. 1 will rank request expert assessment
higher than reassess claim.
5 Discussion and Conclusion
In our approach, we propose the use of a breadth-first search algorithm. The state
space of a case grows exponentially and is possibly infinite. Search algorithms
might not terminate. In combination with useful termination conditions, a breadth-
first search can terminate early and lead to approximate results without querying
the whole state space. The algorithm aims to find all reachable states where the
objective holds, it derives optimal results for the specified path cost function.
What especially suitable path cost functions look like, still needs to be evaluated.
For evaluation, we implemented a first prototype2 , which makes simple recom-
mendations. It uses fCM’s colored Petri net formalization and CPN-Tools3 [5, 8]:
By analyzing the model’s state space, our prototype can verify for each possi-
ble next action whether the objectives can be satisfied eventually. This allows
knowledge workers to assess whether an action complies with their objectives.
In future work, we plan to extend the prototype. First, knowledge workers
need to be allowed to input the objectives and the cost function. Second, the
prototype needs to calculate and return the scores of actions. Also, some technical
challenges need to be addressed. Due to the flexibility of fCM, the state space
is expected to grow exponentially. The algorithm for the state space search
profits from optimization. The definition of fCM allows the state space even
to be infinite, so the algorithm might not terminate at all. In practice, useful
termination conditions for the search need to be found. Furthermore, a qualitative
evaluation in the form of a user study can help to gain insights for the presented
approach and prove it to work.
In this paper, we propose a framework allowing knowledge workers to state their
requirements toward recommendations. These requirements consist of objectives
and a path cost function, which encodes meaningful key performance indicators.
The case model’s state space is then analyzed in the search for paths towards
states that satisfy the objectives. The more likely an action is to be part of such
paths, and the cheaper the paths are, the higher the action is recommended.
With our work, we aim to support knowledge workers in making decisions. This
support is a great asset for utilizing knowledge-intensive processes in practice.
2
https://github.com/bptlab/fCM-query-generator/tree/ZEUS_2022
3
http://cpntools.org
Decision Support for Knowledge-Intensive Processes 27
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