=Paper=
{{Paper
|id=Vol-2454/paper_6
|storemode=property
|title=Towards Case-Based Deviation Management for Flexible Workflows
|pdfUrl=https://ceur-ws.org/Vol-2454/paper_6.pdf
|volume=Vol-2454
|authors=Lisa Grumbach,Ralph Bergmann
|dblpUrl=https://dblp.org/rec/conf/lwa/GrumbachB19
}}
==Towards Case-Based Deviation Management for Flexible Workflows==
https://ceur-ws.org/Vol-2454/paper_6.pdf
Towards Case-Based Deviation Management
for Flexible Workflows
Lisa Grumbach1,2 and Ralph Bergmann2
1
Trier University of Applied Sciences, Location Birkenfeld, Campusallee,
55761 Birkenfeld, Germany
l.grumbach@umwelt-campus.de
2
University of Trier, Department of Business Information Systems II,
54286 Trier, Germany
bergmann@uni-trier.de
Abstract This work introduces potentials for a case-based approach to
deviation management in the context of flexible workflows. A constraint-
based workflow engine is shortly introduced with some strategies for devi-
ation handling. Three different research works about case-based methods
and sequence similarity are described that may be adapted adequately
to fit our use case of deviation management. Finally, not only challenges
that need to be addressed in future work are sketched, but also potentials
of the pursued approach are discussed.
Keywords: Case-Based Reasoning · Flexible Workflow Management
· Flexibility by Deviation · Sequence Similarity.
1 Introduction
Digitalization is advancing in todays business. Small and medium-sized en-
terprises (SMEs) appear to be lagging behind compared to large companies.
Process-aware information systems (PAIS, [1]) are well established for standard-
ized processes, but they lack support for flexibility, which is often required in
SMEs and may lead to competitive advantages. Additionally, in SMEs there are
often few experts which are responsible for certain processes. The knowledge
about how things are done is implicit, but not stored digitally and information
is simply shared orally. As stated by da Silva et al. [23], these experts usually
deviate and perform processes due to their expertise and experiences without los-
ing control or missing the objective, but rather optimizing the process. Tracing
these processes automatically and using them for process control may simplify
the transfer and preservation of “best practices”. This in turn may lead to an
increase of efficiency and enhanced assistance possibilities especially for inexpe-
rienced users. Furthermore, bypassing the system and thus a loss of knowledge
and transparency is prevented.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
L. Grumbach and R. Bergmann
A main characteristic of an adequate approach for SMEs is to allow unex-
pected deviations and support unforeseen changing circumstances. Thereby, the
key challenge is to determine how to continue with the workflow, while still
achieving a successful completion. For an automated handling of such deviations
without the demand for expert knowledge that needs to be integrated manually,
an option is to use past experiences, more specifically completed processes. Ap-
plying case-based reasoning (CBR) to solve the described problem seems to be
promising, as previous made experiences are exploited for adapting to unknown
situations. Our objective is to develop an approach based on case-based reaso-
ning that detects and handles deviations which occur in a flexible workflow, but
still guides the user by recommending adequate work items.
The remainder of the paper is organized as follows. The notions of workflow
flexibility and deviation management including related work will be introduced
in section 2. Our approach of flexible workflow management allowing deviations
is presented in section 3, while the proposed case-based deviation management
including three promising methods, that could be exploited, are outlined in chap-
ter 4. We conclude with a summary and an insight into our future work.
2 Foundations and Related Work
In this section our previous work on workflow flexibility and a specification of the
underlying terminology will be presented. Furthermore the notion of deviation
will be introduced and related work will be sketched.
2.1 Workflow Flexibility
Flexible Workflows have been focussed in research for more than a decade [22].
Schonenberg et al. [22] distinguish four different flexibility principles. Flexibility
by Design, Change and Underspecification either require an entire awareness
about all possible upcoming situations at design-time in order to manually model
all possible alternatives, or a remodeling of the workflow is necessary at run-time.
Flexibility by Deviation in contrast “is the ability for a process instance to deviate
at run-time from the execution path prescribed by the original process without
altering its process definition” [22]. Thus, single instances may not fit to the
process model. Therefore we explicitly distinguish between modeled workflow,
denoted as de jure, and executed workflow, called de facto [1]. Based on this
definition we developed a workflow engine that facilitates flexibility by deviation.
2.2 Constraint-Based Workflow Engine
During the SEMAFLEX3 [12] and the SEMANAS4 [13] project we developed a
flexible workflow engine based on constraints. We presented an approach [10,11],
3
SEMAFLEX is funded by Stiftung Rheinland-Pfalz für Innovation, grant no. 1158
4
SEMANAS is funded by the Federal Ministry of Education and Research (BMBF),
grant no. 13FH013IX6
Towards Case-Based Deviation Management for Flexible Workflows
that allows flexible deviations from prescribed workflows, but still maintains
control and recommends valid work items to a limited extent.
The proposed method is applied to imperatively modeled workflows, re-
stricted to block-oriented ones. The paradigm of regular block structuring in-
troduced by [20] is applied to guarantee model correctness by construction [16]
and achieve a simplified handling of workflow models. Block-oriented workflows
are constructed through a single root block element, which in turn consists of a
single task node, a sequence of two block elements or a control-flow block. Start
and end of control-flow blocks are clearly defined through control-flow nodes. An
example of a block-oriented workflow is shown in Fig. 1.
d1 d2
t2
t1 + + t4
t3
Figure 1. Example Block-Oriented Workflow
The exemplary workflow consists of four task nodes (rectangles), two data
nodes (ovals) and two control-flow nodes (rhombuses), which represent a parallel
control-flow block (“+”). Additionally, the edges denote either control-flow (solid
lines) or data-flow (dashed lines, input or output relation).
In our approach we transform these imperative workflow models into declara-
tive constraints, which indicate sequential dependencies, to be able to determine
task activations and thus possible executions in a specific unterminated state
of the workflow. A constraint satisfaction problem (CSP) is constructed on the
basis of these generated constraints and logged task enactments. A solution of
the CSP is searched for, which tries to calculate a valid sequential enactment
of all already executed and possible future executions of tasks. Thus, with a
solution we are able to recommend valid task enactments. Consider the exam-
ple of Fig. 1, the constraint set as logical formula is constructed as follows:
t1 < t2 ∧ t1 < t3 ∧ t2 < t4 ∧ t3 < t4 . If task t1 is executed, a value is assigned,
in this case t1 = 1, and added to the constraint set. Task recommendations are
calculated by regarding possible task assignments of the next sequential value,
in this case 2. Considering the constraint set either with t2 = 2 or with t3 = 2 a
solution is found. Thus, t2 and t3 are added as work items to the work list.
An additional advantage of using a CSP is that it is relatively easy and fast
to retract violated constraints at run-time in order to restore consistency in case
of a deviation. Still, by regarding the remaining constraints valid solutions can
be computed. In our work, we therefore described a method, which detects devi-
ations and retracts violated constraints to restore consistency and re-enable the
L. Grumbach and R. Bergmann
workflow engine to recommend work items. But, a categorization of deviations
and a determination of the cause is not considered until now. Thus, in addition
to the restoration of consistency and overcoming a possible deadlock through
system failure, no adequate reaction is possible. Different strategies for resolving
inconsistencies, that are implemented until now, and their limitations are pre-
sented in section 3.1. Due to these limitations, deviation management needs to
be elaborated in more detail and we propose a case-based approach.
2.3 Deviation Management
The notion of deviation is not clearly defined and often included in the term
exception, as a special type. To clarify our used classification of exceptions and
deviations several specifications will be quoted. A process deviation is defined by
da Silva et al. [23] as mismatch between executed process and process model. In
contrast to exceptional behaviour deviations cannot be anticipated. According
to Marrella et al. [17] exception handling is implemented manually at design-
time, whereas deviation handling requires ad-hoc changes at run-time. Eder et
al. [8] describe unexpected exceptions, what we call deviations, as an important
aspect which should be handled to gain possible benefits.
Deviation Management in general can be split up into 3 phases: deviation
detection, deviation handling and deviation analysis. Most of the related work
focus on the detection and handling of deviations by either adapting running
workflow instances or proposing corrective measures. Zhu et al. [27] developed a
process behaviour space expression based on algebra to identify deviations and
provide simple methods like tolerating or adjusting deviations that handle such
exceptions to a limited extent.
Several existing approaches utilise ECA rules [4,7,19] to determine a reaction
to detected deviations. Da Silva et al. [23] use logical formulae as rule basis for the
detection and recommend manually created correction plans. Grambower et al.
[9] present a flexible variant of ECA rules, which is enhanced through contextual
semantic information and reasoning. Actions to perform are additionally adapted
on the basis of semantic knowledge. A similar approach is developed by Adams et
al. [3]. The manual handling of deviation is circumvented by so-called worklets,
which represent exception handling patterns that are inserted automatically in
running workflow instances on the basis of contextual information.
Depaire et al. [6] focus on deviation diagnosis or as they refer to the “manage-
rial view” and investigate and analyse characteristics of deviations on purpose
of searching for control weaknesses. Workflow instances are mined and business
rules representing the deviations are derived subsequently, reducing the amount
of data drastically, which in turn may be analysed.
CBRFlow [25] is a system that exploits conversational case-based reasoning
to react to changing circumstances. Business rules are used to model necessary
workflow run-time changes and may then be recommmended in similar future
situations. But still the user has to intervene manually to trigger a deviation and
to resolve the issues.
Towards Case-Based Deviation Management for Flexible Workflows
3 Analysis of the Deviation Management Problem
All of the presented related work considers manually created knowledge or even
requires manual intervention in a running workflow for the management of de-
viations. As we address SMEs our aim is to automatically adapt to changing
circumstances without the need of modeling process fragments or adaptation
knowledge in any form. Our objective is to create an approach that relies on
experience and thus previously executed workflows and exploits available data
to provide further workflow control and recommendations of work items even in
unexpected situations.
3.1 Typical Scenarios
Two different and very simple constraint violation scenarios will be presented
in combination with strategies, which we already implemented, for restoring
consistency in the CSP.
Sequential Constraint Violation For handling detected deviations in a sim-
ple sequential workflow or in sequential parts of a workflow, we developed two
different strategies for restoring consistency. Consider the example in Fig. 2,
where the first row shows the initial situation and the second row represents one
step further with an additional executed task. Circles indicate tasks and edges
represent the execution order. Blue filled nodes were already executed, pink ones
Figure 2. Example Sequential Workflow with Deviation
are currently recommended and grey nodes are not activated yet. In the lower
row there is a task executed, which is not in a valid order, thus a deviation
occurred. The cause of the violation is not explicit without semantic knowledge
and needs to be specified by the user. Without necessary user intervention we
are able to derive two possible reasons, which are handled in different strategies.
1. Skipped Tasks. Tasks may have become obsolete and are therefore skipped
or have been executed but without notice of the system (cf. Fig. 3a). All
constraints concerning skipped tasks are irrelevant for continuing with the
workflow and simply may be omitted.
2. Changed Order. Task order may have changed due to unknown reasons.
Remaining tasks need to be connected sequentially, as if the current executed
task (second blue one) would have been inserted after the last executed one
(first blue one, cf. new edges in Fig. 3b).
L. Grumbach and R. Bergmann
(a) Skipped Tasks (b) Changed Order
Figure 3. Possible Strategies for Handling Sequential Deviations
Which strategy is applied needs to be determined either prior to the start of
a workflow or at run-time. If the applied strategies differ for single deviations
of the same type, further user interaction is necessary for each occurrence at
run-time, which reduces the advantages of allowed flexibility and an additional
obstacle for an easy-to-use system emerges.
Exclusive Constraint Violation Another example scenario of a simple viola-
tion that might occur is depicted in Fig. 4. In this simplified sketch at first both
exclusive paths might be chosen, followed by the execution of the upper task,
which implies the exclusion of the lower tasks. The deviation occurs in the last
step, as nevertheless a task of the lower path has been executed.
Figure 4. Example Workflow with Exclusive Pattern and Deviation
Which path should be pursued for workflow continuation without further
knowledge can only be guessed by the system. Possible reasons are that either
some task was completed by mistake or eventually certain circumstances changed
that demand an adjustment and caused the execution of an excluded task or
rather whole path. Constraints need to be adapted in case of such a violation
according to one of the strategies pointed out in Fig. 5a and 5b.
The described scenarios have shown two types of deviations and thus con-
straint violations with a simple structure and a limited number of resolving
possibilities. These strategies are straight-forward and easy to implement, but
require user interaction. Besides, deviations might lead to additional deviations
or even might be more complex from ground up, which makes it impossible to
determine similar strategies to handle all possible deviation scenarios. Further-
more, it is not our aim to construct strategies, which need to be picked by the
user, but rather some methods, which can be applied in an automated manner
and support the user invisibly without the need of manual intervention.
Towards Case-Based Deviation Management for Flexible Workflows
(a) Continuing with the Upper (b) Continuing with the Lower Path
Path
Figure 5. Possible Strategies for Handling Deviations in Exclusive Patterns
In our ongoing work we focus on case-based reasoning as technique to over-
come the previously mentioned disadvantages.
4 Case-Based Deviation Management
Case-Based Reasoning [2] is a method that exploits previously made experiences
to adapt to currently upcoming and partly unforeseen situations. As we pur-
sue an approach where users are allowed to deviate flexibly, but still guidance
through recommending work items is to be achieved on the basis of formerly
traced workflows, CBR seems promising. De facto workflows may be used as
case base, which is searched for similar cases compared to a running workflow in-
stance. Furthermore, retrieved cases may be used to adapt the running workflow
instance, either considering the work items or the underlying workflow model.
Our aim is to create a learning workflow engine, which automatically handles
unforeseen deviations and nevertheless guides the user to a successful completion
of the workflow by recommending work items by reuse of previous cases.
4.1 Basic Approach for a Case-Based Deviation Management
In the following, we sketch some characteristics of a case-based approach to
deviation management for flexible workflows.
Case As cases we regard all de facto workflows and their corresponding de jure
workflow. The de jure workflow is block-oriented and the de facto workflow is a
simple sequence of tasks, which were traced at run-time.
Query A running workflow instance, which is not completed yet, will be used
as query. The associated de jure workflow is also available. This instance is a
subsequence of a future de facto workflow and contains at least one deviation
concerning its de jure workflow, that occurred at the time of the request. With
case-based reasoning we aim at recommending work items to continue with the
running workflow instance of the query, by searching for similar de facto work-
flows whose subsequences match the current instance. Successive tasks to those
subsequences in the case may be reused as possible recommendations.
L. Grumbach and R. Bergmann
Additional Knowledge Semantic descriptions and contextual information of
the task and data nodes are accessible as additional knowledge. In certain cases
there might be stored some kind of evaluation, i.e. if the workflow completed suc-
cessful or failed. Furthermore, a categorization of the deviation might be derived,
e.g. skipping of a task. Besides the de jure workflow, the declarative constraints,
which are created and processed by the workflow engine, might be considered.
Therefore, violated constraints are identifiable as well, if a deviation occurred.
All of this additional information may be consulted for similarity calculation and
enhance the retrieval process.
Similarity The similarity measure has to deal with a subset relation between
query and case, as the de facto workflow of the query has not terminated yet,
whereas the de facto workflows of the cases are complete. For the comparison of
the de facto workflows we propose to use a sequence similarity measure, but local
semantic similarity of tasks should be considered as well in a global similarity
measure for the whole workflows.
Furthermore, differences in subsequences that are not related directly to the
deviation, when comparing query and case, might not be relevant. This rel-
evance might be expressed by the distance to the currently focussed task or
the position concerning its level. For example, consider the workflow in Fig. 1,
where a parallel control-flow block is included. Valid de facto workflows could be
f1 = ⟨t1 , t2 , t3 , t4 ⟩ as well as f2 = ⟨t1 , t3 , t2 , t4 ⟩, where the order of the tasks of
the control-flow block may differ. If a deviation occurs in the part after t4 , which
is omitted in the figure, differences in the preceding part of the de facto workflow
of case and query, like f1 and f2 , might not be relevant for the deviation and
should nevertheless be regarded as highly similar.
Additionally, as the technique will be applied during workflow execution, the
retrieval and adaptation needs to fulfill real-time constraints, otherwise users
will be tempted to bypass the system.
In the following, we will investigate three methods for the retrieval of the
most similar cases and adaptations of the query concerning their applicability
to the described case-based deviation management.
4.2 Trace-Based Reasoning
Trace-based Reasoning (TBR), which was first introduced by Mille [18], is a spe-
cial form of case-based reasoning, where traces serve as case base. An important
aspect as extension to standard CBR is the introduction of a temporal aspect. In
TBR similarity measures are used that base upon temporal sequences. Cordier et
al. [5] and Zarka [26] presented an approach with the objective of finding contex-
tual recommendations for users that interact with web applications. Therefore
an algorithm was developed that is based on the Smith-Waterman-Algorithm
(SWA) and was adapted for retrieving similar traces. The SWA is used for local
sequence alignment and compares traces in all possible lengths. This character-
istic perfectly matches the requirements of trace-based reasoning as terminated
Towards Case-Based Deviation Management for Flexible Workflows
instances have to be compared to ongoing and not completed processes which
are only subsequences and will not match exactly considering the length. Several
local similarity measures are used for each attribute of the observed elements,
that are part of the traces. For each observed element a global similarity value
is computed on the basis of local values with use of a weighted average function.
These similarity values of single elements are furthermore used in the SWA to
calculate the overall similarity of traces. This method can be transferred to our
pursued deviation management, if de facto workflows are considered as traces,
for retrieving the best matching cases.
As adaptation method, Zarka [26] proposes a simple extraction method, that
takes a subsequence of actions from the most similar retrieved case, which follows
the part of the matched trace. Some values of this subsequence are adapted
by more adequate ones on the basis of contextual information. As last step in
the adaptation process, actions are filtered, e.g. invalid actions or unnecessary
repetitions.
In our case of deviation management this subsequence can be extracted from
the retrieved de facto workflow and first tasks of this subsequence can then
be recommended as work items. Furthermore, if case and query do not match
exactly, edit steps, which are necessary for aligning query and case, are part of the
solution of the SWA. These additional differences relate to previously happened
deviations, but might be required for a successful completion of the workflow as
well. Therefore, these edit steps possibly might be transformed adequately into
additional work items to ultimately provide further workflow control.
4.3 Alternative Similarity Measures
Dynamic Time Warping Dynamic Time Warping (DTW, [21]) may be used
as alternative similarity measure for sequence comparison. DTW is already ap-
plied in various CBR approaches, for example for patient case matching in the
medical domain [24]. It is a method which is used for aligning two time series,
in our case de facto workflows can be regarded as such, by finding the best
possible matching. For local comparison of different elements a cost function is
defined. An optimal warping path is then searched for in the constructed cost
matrix. The algorithm follows the same principle compared to the SWA with
the main difference of values that are used in the matrix. A main advantage of
both methods is the resistance to noise.
Vector Similarity Measure for Sequences A completely different approach
is adopted by Gundersen [14]. In his research work, he presents a similarity
measure for sequences on the basis of vectors. The main objective is to create
a similarity measure which is suitable for real-time analysis relying on the as-
sumption that events that happened near-time are more important than events
from longer time ago. This is realized by a weighting function, that considers
the position of events, related to the start and end of a sequence. The nearer
the occurrence of an event is to the end of a sequence, the higher the weight
L. Grumbach and R. Bergmann
value. For each event type this weight is computed as sum of single weights of
single event occurrences. These weights of event types are then combined into a
vector, that in turn may be compared to other vectors with common methods,
like cosine similarity.
The approach is lacking semantic similarity between single tasks, as only
whole sequences are compared considering their properties of task occurrences.
Only self-referential characteristics of tasks can be included in the single weights
of tasks, like e.g. importance or severity.
The vector similarity measure is developed on the basis of requirements for
recognizing failure causes and recommending adequate solutions in the oil-well
drilling domain. As specific characteristics are addressed, cf. [15], the approach
is not generally applicable, but some properties might be transferable, especially
for the relevance of task similarity dependent on the distance to the currently
focused task.
4.4 Challenges and Potentials
To fit the needs of our proposed case-based deviation management the presented
similarity measures and adaptation methods need to be investigated in detail and
evaluated against our requirements. But as described previously some character-
istics of each of the approaches might be adopted and combined for an adequate
handling of deviations. Until now, we consider TBR as the technique with the
most potential for an application to our proposed deviation management, as
the similarity measure includes semantics of tasks and is also resistant to noise.
Furthermore, adaptation opportunities are already available. We will consider
to slightly adjust the similarity function with weights, indicating the distance to
the deviation, analogously to the vector similarity measure of Gundersen [14].
Additionally to these challenging requirements some opportunities arise. An
important aspect that should not be neglected is the performance of the applied
algorithm as our approach will be used in real-time. To improve retrieval time,
an option may be to filter the case base during execution, as the beginning of
the trace is already available and enhanced step by step.
Another potential which will be investigated is, if the work list can be opti-
mized through using CBR methods even if no deviation occurs, e.g. through a
priorization and ranking of work items one the basis of previous traces. Perhaps
even deviations could be explicitly triggered. Consider a best practice, and thus
optimized, workflow, which is executed through experienced employees, will be
traced and enhance the case base. This workflow might not be an exact mapping
of the designed workflow model, but may still be used for workflow control with
the use of CBR methods.
5 Conclusion
In this paper we presented our vision of a case-based approach to deviation man-
agement in the context of flexible workflows. We described our ongoing research
Towards Case-Based Deviation Management for Flexible Workflows
work about constraint-based workflow flexibility and the need for an integration
of a thorough deviation management. We investigated the potential of applying
methods of case-based reasoning and sequence similarity and therefore intro-
duced three existing techniques, that may be used after an adequate adoption.
Finally we sketched some challenges and potentials of the approach, that we will
face in our future work.
References
1. van der Aalst, W.M.P.: Business Process Management - A Comprehensive Survey.
ISRN Software Engineering 2013, 1–37 (2013)
2. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological
variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
3. Adams, M., ter Hofstede, A.H.M., van der Aalst, W.M.P., Edmond, D.: Dynamic,
extensible and context-aware exception handling for workflows. In: On the Move
to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS,
OTM Confederated International Conferences, Vilamoura, Portugal, November 25-
30, 2007, Proceedings, Part I. pp. 95–112 (2007)
4. Casati, F., Ceri, S., Paraboschi, S., Pozzi, G.: Specification and implementation of
exceptions in workflow management systems. ACM Trans. Database Syst. 24(3),
405–451 (1999)
5. Cordier, A., Lefèvre, M., Champin, P., Georgeon, O.L., Mille, A.: Trace-based rea-
soning - modeling interaction traces for reasoning on experiences. In: Proceedings
of the Twenty-Sixth International Florida Artificial Intelligence Research Society
Conference, FLAIRS 2013, St. Pete Beach, Florida, USA, May 22-24, 2013. (2013)
6. Depaire, B., Swinnen, J., Jans, M., Vanhoof, K.: A process deviation analysis
framework. In: Business Process Management Workshops - BPM 2012 Interna-
tional Workshops, Tallinn, Estonia, September 3, 2012. pp. 701–706 (2012)
7. Döhring, M., Zimmermann, B., Godehardt, E.: Extended workflow flexibility us-
ing rule-based adaptation patterns with eventing semantics. In: Informatik 2010:
Service Science - Neue Perspektiven für die Informatik, Beiträge der 40. Jahresta-
gung der Gesellschaft für Informatik e.V. (GI), Band 1, 27.09. - 1.10.2010, Leipzig,
Deutschland. pp. 195–200 (2010)
8. Eder, J., Liebhart, W.: The workflow activity model WAMO. In: CoopIS. pp. 87–98
(1995)
9. Grambow, G., Oberhauser, R., Reichert, M.: Event-driven exception handling for
software engineering processes. In: Business Process Management Workshops -
BPM 2011 International Workshops, Clermont-Ferrand, France, August 29, 2011,
Revised Selected Papers, Part I. pp. 414–426 (2011)
10. Grumbach, L., Bergmann, R.: Using constraint satisfaction problem solving to
enable workflow flexibility by deviation (best technical paper). In: Artificial Intel-
ligence XXXIV - 37th SGAI International Conference on Artificial Intelligence, AI
2017, Cambridge, UK, December 12-14, 2017, Proceedings. pp. 3–17 (2017)
11. Grumbach, L., Bergmann, R.: SEMAFLEX: A novel approach for implementing
workflow flexibility by deviation based on constraint satisfaction problem solving.
Expert Systems (03 2019)
12. Grumbach, L., Rietzke, E., Schwinn, M., Bergmann, R., Kuhn, N.: SEMAFLEX -
Semantic Integration of Flexible Workflow and Document management. In: Pro-
ceedings of the Conference “Lernen, Wissen, Daten, Analysen”, Potsdam, Ger-
many, September 12-14, 2016. pp. 43–50 (2016)
L. Grumbach and R. Bergmann
13. Grumbach, L., Rietzke, E., Schwinn, M., Bergmann, R., Kuhn, N.: SEMANAS -
semantic support for grant application processes. In: Proceedings of the Conference
“Lernen, Wissen, Daten, Analysen”, LWDA 2018, Mannheim, Germany, August
22-24, 2018. pp. 126–131 (2018)
14. Gundersen, O.E.: Toward measuring the similarity of complex event sequences in
real-time. In: Case-Based Reasoning Research and Development - 20th Interna-
tional Conference, ICCBR 2012, Lyon, France, September 3-6, 2012. Proceedings.
pp. 107–121 (2012)
15. Gundersen, O.E.: Enhancing the Situation Awareness of Decision Makers by Apply-
ing Case-Based Reasoning on Streaming Data. Ph.D. thesis, Norwegian University
of Science and Technology, Trondheim, Norway (2014)
16. Kiepuszewski, B., ter Hofstede, A.H.M., Bussler, C.: On structured workflow mod-
elling. In: Advanced Information Systems Engineering, 12th International Confer-
ence CAiSE, Stockholm, Sweden, June 5-9, 2000, Proceedings. pp. 431–445 (2000)
17. Marrella, A., Mecella, M., Sardiña, S.: Intelligent process adaptation in the
smartpm system. ACM TIST 8(2), 25:1–25:43 (2017)
18. Mille, A.: From case-based reasoning to traces-based reasoning. Annual Reviews
in Control 30(2), 223–232 (2006)
19. Müller, R., Greiner, U., Rahm, E.: Agent work : a workflow system supporting rule-
based workflow adaptation. Data Knowl. Eng. 51(2), 223–256 (2004)
20. Reichert, M.: Dynamische Ablaufänderungen in Workflow-Management-Systemen.
Ph.D. thesis, University of Ulm, Germany (2000), http://d-nb.info/960862684
21. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken
word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing
26, 43–49 (02 1978)
22. Schonenberg, H., Mans, R., Russell, N., Mulyar, N., van der Aalst, W.M.P.: To-
wards a taxonomy of process flexibility. In: Proceedings of the Forum at the
CAiSE’08 Conference, Montpellier, France, June 18-20, 2008. pp. 81–84 (2008)
23. da Silva, M.A.A., Bendraou, R., Robin, J., Blanc, X.: Flexible deviation handling
during software process enactment. In: Workshops Proceedings of the 15th IEEE
International Enterprise Distributed Object Computing Conference, EDOCW
2011, Helsinki, Finland, August 29 - September 2, 2011. pp. 34–41 (2011)
24. Tsevas, S., Iakovidis, D.K.: Dynamic time warping fusion for the retrieval of similar
patient cases represented by multimodal time-series medical data. Proceedings of
the 10th IEEE International Conference on Information Technology and Applica-
tions in Biomedicine pp. 1–4 (2010)
25. Weber, B., Wild, W., Breu, R.: Cbrflow: Enabling adaptive workflow manage-
ment through conversational case-based reasoning. In: Advances in Case-Based
Reasoning, 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 -
September 2, 2004, Proceedings. pp. 434–448 (2004)
26. Zarka, R.: Trace-based reasoning for user assistance and recommendations.
(Raisonnement à partir de l’expérience tracée pour l’assistance à l’utilisateur et les
recommandations). Ph.D. thesis, INSA Lyon, Lyon - Villeurbanne, France (2013)
27. Zhu, R., Dai, F., Mo, Q., Yu, Y., Lin, L., Li, T.: An approach to handling software
process deviations. Lecture Notes on Software Engineering 3, 238–244 (01 2015)