=Paper=
{{Paper
|id=Vol-2191/paper42
|storemode=property
|title=Towards an Interactive Workflow Modeling Assistance by Means of Case-Based Reasoning
|pdfUrl=https://ceur-ws.org/Vol-2191/paper42.pdf
|volume=Vol-2191
|authors=Christian Zeyen,Ralph Bergmann
|dblpUrl=https://dblp.org/rec/conf/lwa/ZeyenB18
}}
==Towards an Interactive Workflow Modeling Assistance by Means of Case-Based Reasoning==
https://ceur-ws.org/Vol-2191/paper42.pdf
Towards an Interactive Workflow Modeling Assistance
by Means of Case-Based Reasoning
Christian Zeyen and Ralph Bergmann
Business Information Systems II
University of Trier
54286 Trier, Germany
[zeyen][bergmann]@uni-trier.de,
http://www.wi2.uni-trier.de
Abstract. Supporting non-experts in modeling workflows is an important yet
challenging task to make workflow technology more accessible. While scientific
workflows are broadly used in e-Science and various approaches to support mod-
eling have been proposed, Digital Humanities (DH) is a rather new application
domain that is largely unexplored, precisely in this respect. This paper presents
our current work on developing an interactive modeling assistance for scientific
workflows. We argue that interactivity is an important aspect when it comes to
supporting the user in the iterative process of workflow modeling. We consider
this process as a problem-solving activity and propose an assistance by case-
based reasoning through the experience-based reuse of previously created work-
flows. We primarily target non-expert users and focus on text and data mining
workflows, which we assume are in particular useful in the DH.
Keywords: Case-Based Reasoning · Workflow Reuse · Scientific Workflows
1 Introduction
Scientific workflows[15] can be regarded as an established means to perform experi-
ments and data analysis in e-Science. Generally speaking, workflows are beneficial for
the automation, modularization, reproducibility, reuse, documentation, and others. The
potentials of scientific workflows have also been recognized in the context of the Digital
Humanities (DH) although their broad usage is still in its infancy [12,13]. Particularly,
modularization and reuse are topics of interest since various scientific tools1 have been
emerged from DH projects but reusing them for new research questions usually requires
non-trivial and time-consuming adjustments or new combinations of tools. Scientific
workflows particularly capture expert knowledge of how to solve a concrete problem in
terms of required data, suitable processing steps and their composition, and parameter
settings to name just a few. Hence, workflows can be valuable for non-expert users.
By reusing workflows that have proven useful or repurposing them by experimenting
with different combinations users may be able to perform non-trivial data analysis tasks
1
This can be seen in the advent of tool and method collections such as TAPoR (http://tapor.ca)
and Methodica (http://methodi.ca)
2 C. Zeyen, R. Bergmann
more efficiently [8]. Scientific Workflow Management Systems (SciWFM) typically
enable the user to construct analytics processes at an more abstract level. For example,
the RapidMiner (formerly known as YALE) [14] workflow editor supports the visual
programming of workflows for data and text mining tasks. However, modeling new
workflows can be a demanding and time-consuming task for novice users, in particu-
lar for complex data analysis that involve large amounts of data and require complex
combinations of processing steps [6].
Consequently, supporting the development of scientific workflows has been consid-
ered an important topic of research and various approaches have been presented to this
effect. Regarding RapidMiner, a recent feature [11] automatically creates data mining
workflows for a user-defined data set and pre-defined analysis tasks. Another approach
by Jannach et al. [9] supports the user with context-sensitive recommendations of suit-
able processing steps and parameter settings for the current workflow under construc-
tion based on the analysis of a large workflow repository. In [10], Kietz et al. summa-
rize their findings from extending RapidMiner with semantic technologies. Building an
ontology of the available processing steps, parameters, constraints, etc. enabled them
to implement a fully automatic composition of workflows using a planning approach,
correctness-checking of workflows, and quick-fixes to support users. Another SciWFM
named WINGS [7] does also follow a semantic approach and uses planning and se-
mantic reasoning to automatically create workflows based on high-level user requests.
Case-Based Reasoning (CBR) [1] has also been utilized to support the development
of workflows as an experience-based activity. For instance, Chinthaka et al. [5] pro-
posed a generic approach that supports the keyword-based and graph-based search for
workflows based on workflows annotated by the user beforehand.
Previous approaches that automatically compose or search workflows typically re-
quire the user to specify their requirements and analysis goals in a query. This poses
the difficult dilemma of query elicitation. A query interface with a higher degree of
abstraction might be more suitable for inexperienced users but is also more restrictive
and thus less appropriate for users with more specific requests. On the other side, ex-
pressive query interfaces might be more suitable for expert users but the formulation
of such a query can be a significant burden for the inexperienced user as it requires
comprehensive domain knowledge. As a consequence, interactive search interfaces are
considered to be important since they allow an iterative query refinement [6]. Further
desirable features are the presentation of discriminative properties of similar workflows
and the consideration of user feedback. Conversational CBR (CCBR) [2] approaches
particularly target the problem of a proactive query formulation by conducting a di-
alog with the user. During a conversation, the user can answer consecutive questions
instead of deciding proactively which information to include in the query. However, to
our knowledge, CCBR has not yet been applied in the context of a modeling assistance
for scientific workflows.
In this paper, we present ongoing work on interactive approaches to scientific work-
flow reuse. We particularly focus on supporting novice users such as domain scien-
tists, students, or practitioners. More precisely, building up upon our previous works on
process-oriented CBR, we are working on new interactive CBR approaches to a work-
flow modeling assistance. We plan to implement them as an extension to RapidMiner
Towards an Interactive Workflow Modeling Assistance by Means of CBR 3
and evaluate them in the domain of text and data mining workflows with non-expert
users. We particularly aim to support digital humanists and we hope that our work can
contribute to the establishment of scientific workflows in the DH.
In the following, section 2 describes our research goals and related projects in more
detail while section 3 presents the proposed initial approach to an interactive workflow
modeling assistance. Section 4 concludes with a short summary and outlook.
2 Research Goals and Projects
Our research is embedded within two projects located at the University of Trier. In the
first project named eXplore!2 we investigate the characteristics of workflow composi-
tion in the field of Digital Humanities. From the literary-scientific perspective, the goal
is to investigate influences on the creative processes of Klaus Mann, a famous Ger-
man writer, by analyzing documents about his life and experiences written by himself.
A focus is put on his diaries from the 1930’s that comprise very detailed information
about his personal and professional life. Due to the wealth of information contained in
the texts, text and data mining workflows are created and applied to extract, combine,
and analyze the available data. Our primary goal is to accompany the creation of such
scientific workflows and to develop a workflow modeling assistance suitable to support
non-experts. The project is testing the use of RapidMiner as a SciWMS to create, apply,
and manage workflows for the data analysis. In the course of the workflow creation, we
capture the experiences made and we populate a structured workflow repository as a
basis for further research on providing modeling support. Using CBR, we aim at pro-
viding an interactive workflow modeling assistance that facilitates the creation of new
workflows by reusing and repurposing past ones.
For this purpose, we focus on developing new methods for the interactive retrieval
and adaptation of workflows by means of process-oriented CBR (POCBR) in the second
project named EVER3 . During the first funding period from 2011 to 2016, the project
has been investigating whether workflow technology and POCBR can help to analyze
and reuse procedural experiential knowledge in Internet communities such as cooking
web sites [4]. We considered recipes as cooking workflows and developed methods for
the similarity-based retrieval of workflows and the automatic adaptation of retrieved
ones. Throughout the project, we continuously integrated the developed methods into
the CBR component of the CAKE framework [3]. Currently, the project is in its second
funding period in which we focus on transferring the developed approaches to a novel
application domain, i.e., text and data mining workflows. With regard to the goal of
developing a workflow modeling assistance, interactivity will be a focus of research.
The current retrieval and adaptation methods are fully automatic for a given user query
and do not further interact with the user. To avoid the specification of user queries we
proposed a conversational approach for the interactive retrieval of cooking workflows
2
eXplore! – Computer-based Modeling, Analysis, and Exploration as a Basis for eScience in
eHumanities is a cooperation project (launched in 2016) with the Trier Center for Digital
Humanities (TCDH) at the University of Trier.
3
EVER – Extraction and Processing of Procedural Experiential Knowledge in Workflows is a
cooperation project (launched in 2011) with the Goethe University in Frankfurt.
4 C. Zeyen, R. Bergmann
[16]. However, adaptation has not yet been integrated. Our previous works revealed that
the automatic adaptation approaches are capable of increasing the overall query fulfill-
ment but potentially decrease the workflow quality. Consequently, we aim at developing
new methods for the interactive retrieval and adaptation in order to better suit the user’s
needs. In particular, we hope that our approach is able to make workflow modeling more
accessible for novice users.
3 Interactive Workflow Modeling Assistance by CBR
Figure 1 depicts the overall architecture of our case-based modeling assistance. Tra-
ditionally, the user creates, manages, and executes workflows in a graphical interface
provided by a workflow management system. In order to facilitate the workflow cre-
ation, we integrate a CBR system into the user interaction. The integration comprises
the transformation of workflows into semantic graphs and the formalization of domain
knowledge about workflows and meta data into an ontology in order to enable the sim-
ilarity assessment between workflows. Based on the transformed case base, adaptation
knowledge can be learned automatically [4]. The actual modeling assistance is realized
by implementing the CBR system following the well-known R4 -cycle [1] in order to
retrieve, reuse, revise, and retain workflows.
Workflow Management
User Workflow System
Interaction Repository
Case-based Reasoning
Transformation System
Case Base
User of Semantic Learning
Workflows
Retrieve Adaptation
Knowledge
User
Retain Reuse
Interaction
Revise Ontology
Fig. 1. Architecture of a Case-based Modeling Assistance
The interaction with the user plays an important role throughout the (iterative) work-
flow modeling process and the entire CBR cycle, respectively
Department of Business
Information Systems II
-1-
since we hope to obtain
the following valuable information:
Retrieve At first, the user starts creating a workflow. Presumably, she does not yet
know how the final workflow is constructed exactly and does only insert the input
Towards an Interactive Workflow Modeling Assistance by Means of CBR 5
and output data without specifying the entire data transformation. By accompany-
ing the user’s modeling process, information about the current modeling context can
be gathered, e.g., the last workflow element the user inserted, and the retrieval can
be invoked automatically whenever the workflow is updated. During the retrieve
phase, the interaction component displays questions to the user and thus learns the
user’s requirements and goals successively. Based on the available information, an
internal query is elaborated and best matching workflows from the repository are
retrieved and ranked by similarity. The result list is displayed to the user.
Reuse In this phase, the user can apply her own modifications to the current workflow
based on the workflows obtained from the retrieval. In addition, based on these
workflows and the available adaptation knowledge, the CBR system computes ap-
plicable adaptations to the current workflow under construction and suggests them
to the user. Such adaptations may consist of workflow fragments that can be (au-
tomatically) inserted into the current workflow. By this means, an autocompletion
feature could be realized. Each adaptation must be selected by the user.
Revise This phase is closely linked to the reuse phase since each automatic adaptation
may be undone or corrected by the user. Maintaining the quality of automatically
learned adaptation knowledge is a challenging task. In this regard, the user can
give valuable feedback to the system whether suggested or automatically performed
adaptations have met the user’s requirements.
Retain Retainment is essential for the learning of new experience. Whenever the user
finalized a workflow and the workflow is tried and tested, she can inform the sys-
tem about the new case and may also provide meta information that characterizes
the specific application situation in which the workflow has proven useful. In addi-
tion, the CBR system can try to learn from manually applied adaptations that were
performed by the user.
4 Summary and Outlook
In this paper we have presented ongoing work on the development of an interactive
workflow modeling assistance by case-based reasoning. The target is to provide an
assistance for non-experts that do not have a broad experience in creating workflows
for text and data analysis. The question under investigation is whether interactive case-
based reasoning can support inexperienced users (such as domain scientists or students)
to familiarize themselves with the workflow programming paradigm and to create ap-
propriate workflows in order to complete their desired analysis tasks. The Digital Hu-
manities are a new and so far under-explored application domain with respect to this
approach. In future work, we will focus on the interactive nature of problem-solving
and investigate new methods for interactive retrieval and adaptation of already avail-
able workflows to provide users with useful suggestions for the current workflow under
construction.
Acknowledgments. This work is partly funded by the German Federal Ministry of
Education and Research (BMBF, No. 01UG1606) and the German Research Foundation
(DFG, No. BE 1373/3-3).
6 C. Zeyen, R. Bergmann
References
1. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological varia-
tions, and system approaches. AI Communications 7(1), 39–59 (1994)
2. Aha, D.W., Breslow, L., Muñoz-Avila, H.: Conversational Case-Based Reasoning. Appl. In-
tell. 14(1), 9–32 (2001)
3. Bergmann, R., Gessinger, S., Görg, S., Müller, G.: The collaborative agile knowledge engine
CAKE. In: Goggins, S.P., Jahnke, I., McDonald, D.W., Bjørn, P. (eds.) Proceedings of the
18th International Conference on Supporting Group Work, 2014. pp. 281–284. ACM (2014)
4. Bergmann, R., Minor, M., Müller, G., Schumacher, P.: Project EVER: Extraction and Pro-
cessing of Procedural Experience Knowledge in Workflows. In: Sánchez-Ruiz, A.A., Kofod-
Petersen, A. (eds.) Proceedings of ICCBR 2017 Workshops. CEUR Workshop Proceedings,
vol. 2028, pp. 137–146. CEUR-WS.org (2017)
5. Chinthaka, E., Ekanayake, J., Leake, D.B., Plale, B.: CBR Based Workflow Composition
Assistant. In: 2009 IEEE Congress on Services, Part I, SERVICES I 2009. pp. 352–355.
IEEE Computer Society (2009)
6. Cohen-Boulakia, S., Leser, U.: Search, Adapt, and Reuse: The Future of Scientific Work-
flows. SIGMOD Record 40(2), 6–16 (2011)
7. Gil, Y., Ratnakar, V., Kim, J., González-Calero, P.A., Groth, P.T., Moody, J., Deelman, E.:
Wings: Intelligent Workflow-Based Design of Computational Experiments. IEEE Intelligent
Systems 26(1), 62–72 (2011)
8. Hauder, M., Gil, Y., Sethi, R.J., Liu, Y., Jo, H.: Making data analysis expertise broadly acces-
sible through workflows. In: Taylor, I.J., Montagnat, J. (eds.) WORKS’11, Proceedings of
the 6th Workshop on Workflows in Support of Large-Scale Science. pp. 77–86. ACM (2011)
9. Jannach, D., Jugovac, M., Lerche, L.: Supporting the Design of Machine Learning Work-
flows with a Recommendation System. TiiS 6(1), 8:1–8:35 (2016)
10. Kietz, J.U., Serban, F., Fischer, S., Bernstein, A.: “Semantics Inside!” But Let’s Not Tell
the Data Miners: Intelligent Support for Data Mining. In: Presutti, V., d’Amato, C., Gandon,
F., d’Aquin, M., Staab, S., Tordai, A. (eds.) The Semantic Web: Trends and Challenges -
11th International Conference, ESWC 2014, Proceedings. LNCS, vol. 8465, pp. 706–720.
Springer (2014)
11. Krishna Roy: RapidMiner looks to boost data scientists’ productivity with Auto Model, https:
//rapidminer.com/resource/451-research-report-auto-model/
12. Kuhn, J., Reiter, N.: A Plea for a Method-Driven Agenda in the Digital Humanities. In: Book
of Abstracts of DH 2015 (2015)
13. Kuras, C., Eckar, T.: Prozessmodellierung mittels BPMN in Forschungsinfrastrukturen der
Digital Humanities. In: Eibl, M., Gaedke, M. (eds.) INFORMATIK 2017. pp. 1101–1112.
Gesellschaft für Informatik, Bonn (2017)
14. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid prototyping
for complex data mining tasks. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D.
(eds.) Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 2006. pp. 935–940. ACM (2006)
15. Taylor, I.J., Gannon, D.B., Shields, M. (eds.): Workflows for e-Science: Scientific Workflows
for Grids. Springer London (2010)
16. Zeyen, C., Müller, G., Bergmann, R.: Conversational Process-Oriented Case-Based Reason-
ing. In: Aha, D.W., Lieber, J. (eds.) Case-Based Reasoning Research and Development -
25th International Conference, ICCBR 2017, Proceedings. LNCS, vol. 10339, pp. 403–419.
Springer (2017)