=Paper=
{{Paper
|id=Vol-1520/paper34
|storemode=property
|title=Workflow Adaptation in Process-oriented Case-based Reasoning
|pdfUrl=https://ceur-ws.org/Vol-1520/paper34.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/Muller15
}}
==Workflow Adaptation in Process-oriented Case-based Reasoning==
https://ceur-ws.org/Vol-1520/paper34.pdf
277
Workflow Adaptation in Process-oriented
Case-based Reasoning
Gilbert Müller
Business Information Systems II
University of Trier
54286 Trier, Germany
muellerg@uni-trier.de,
http://www.wi2.uni-trier.de
Workflows are an important research domain, as they are used in many ap-
plication areas, e.g., there are business workflows, scientific workflows, workflows
representing information gathering processes, or cooking instructions. Workflows
are “the automation of a business process, in whole or part, during which doc-
uments, information or tasks are passed from one participant to another for
action, according to a set of procedural rules” [4]. Thus, workflows consists
of a structured set of tasks and data objects shared between those tasks. In
this regard, Process-oriented Case-based Reasoning (POCBR) [7] addresses the
creation and adaptation of processes that are, e.g., represented as workflows.
Although, POCBR is of high relevance little research exist so far.
The presented research focuses on the development of new workflow adapta-
tion approaches and related topics, for instance the retrieval of workflows. Meth-
ods are investigated, which automatically learn adaptation knowledge from the
case base. This prevents limited adaptation capabilities due to the acquisition
bottleneck for adaptation knowledge.
1 Research Questions
This section presents the research questions addressed by my doctoral thesis in
note form.
1. How can workflows be efficiently retrieved?
2. How can workflows be adapted regarding defined preferences or restrictions?
3. How can interactive workflow adaptation be realized?
4. How can the adaptability of workflows be reflected during retrieval?
5. How can adaptation knowledge be revised to address the retainment of adap-
tation knowledge?
The approaches to address the first two research questions are described
in the next section and section 3 describes how the remaining open research
questions are going to be investigated.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
278
2 Current state of research
The presented research is implemented and evaluated using the CAKE (Collabo-
rative Agent-based Knowledge Engine) framework1 developed at the University
of Trier. It deals with semantic workflows and is able to compute the similarity
between two workflows according to the semantic similarity [2]. The approaches
will are illustrated and investigated in the cooking domain, i.e., the workflows
represent cooking recipes.
Currently, approaches addressing the first two research questions have been
investigated:
1. Based on research about clustering of workflows [3], the problem of improv-
ing retrieval performance by developing a cluster-based retrieval method for
workflows [8] was addressed. To achieve this, a new clustering algorithm,
which constructs a binary tree of clusters was developed. The binary tree is
used as index structure during a heuristic search to identify the most sim-
ilar clusters containing the most similar workflows in a top-down fashion.
Further, POQL [12] was developed serving as query language to guide the
retrieval and the adaptation of workflows regarding defined preferences or
restrictions.
2. Several adaptation approaches had been investigated to address the second
research question. A compositional adaptation approach for workflows was
investigated [9] where workflows are decomposed into meaningful subcompo-
nents, called workflow streams. In order to support adaptation, streams of the
retrieved workflow are replaced by appropriate streams of other workflows.
Based on this work, operator-based adaptation [11] has been developed. The
adaptation operators are learned automatically based on the workflows in the
case base enabling to remove, insert or replace workflow fragments. Further,
workflow generalization and specialization has been addressed [10], which
increases the coverage of the workflow cases and thus being able to support
adaptation as well.
3 Future Work
In future work, an additional adaptation approach will be investigated for se-
mantic workflows, similar to the adaptation approach presented by Minor et. al.
[6], which is based on adaptation cases describing how to transform a partic-
ular workflow to a target workflow. The future work addressing the remaining
research questions 3.-5. is summarized below.
A drawback of applying traditional adaptation methods is that the adap-
tation goal must mostly be known previously. Consequently, this can lead to a
non-optimal or not desired solution. Hence, interactive adaption [1] will be in-
vestigated, as it is a promising approach to overcome this drawback. It supports
1
cakeflow.wi2.uni-trier.de
279
the search of a suitable query and hence the desired solutions by involving user
interaction during adaptation.
Further, separating similarity-based retrieval and adaptation may provide
workflows that can not be at best adapted according to the query. Hence, meth-
ods will be developed that also reflect the adaptability of the workflows during
the retrieval stage [13].
Moreover, feedback of workflow adaptation will be captured in order to ad-
dress the retaining of adaptation knowledge [5]. This is essential, as the quality
of automatically learned adaptation knowledge can not always be ensured. Thus,
the quality of workflow adaptation is improved. Further, the growth of adapta-
tion knowledge can be controlled and hence the performance of adaptation can
be maintained.
References
1. Aha, D.W., Muñoz-Avila, H.: Introduction: Interactive case-based reasoning. Ap-
plied Intelligence 14(1), 7–8 (2001)
2. Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic
workflows. Inf. Syst. 40, 115–127 (Mar 2014)
3. Bergmann, R., Müller, G., Wittkowsky, D.: Workflow clustering using semantic
similarity measures. In: Timm, Thimm (eds.) KI 2013: Advances in Artificial In-
telligence. LNCS, vol. 8077, pp. 13–24. Springer (2013)
4. Hollingsworth, D.: Workflow management coalition glossary & terminol-
ogy. http://www.wfmc.org/docs/TC-1011_term_glossary_v3.pdf (1999),
last access on 04-04-2014
5. Jalali, V., Leake, D.: On retention of adaptation rules. In: Case-Based Reasoning
Research and Development, pp. 200–214. Springer (2014)
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Information Systems 40(0), 103 – 105 (2014)
8. Müller, G., Bergmann, R.: A Cluster-Based Approach to Improve Similarity-Based
Retrieval for Process-Oriented Case-Based Reasoning. In: Proceedings of ECAI
2014. Prague, Czech Republic (2014)
9. Müller, G., Bergmann, R.: Workflow Streams: A Means for Compositional Adap-
tation in Process-Oriented CBR. In: Proceedings of ICCBR 2014. Cork, Ireland
(2014)
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Based Reasoning. In: 28th International FLAIRS Conference. AAAI, Hollywood
(Florida), USA (2015)
11. Müller, G., Bergmann, R.: Learning and Applying Adaptation Operators in
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Case-Based Reasoning (2015), to be submitted to LWA 2015 Trier, Germany
13. Smyth, B., Keane, M.: Retrieving adaptable cases. In: Wess, S., Altho↵, K.D.,
Richter, M. (eds.) Topics in Case-Based Reasoning, Lecture Notes in Computer
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