=Paper=
{{Paper
|id=Vol-2567/paper23
|storemode=property
|title=Interactive Assistance for Scientic Workflow Modeling by Case-Based Reasoning
|pdfUrl=https://ceur-ws.org/Vol-2567/paper23.pdf
|volume=Vol-2567
|authors=Christian Zeyen
|dblpUrl=https://dblp.org/rec/conf/iccbr/Zeyen19
}}
==Interactive Assistance for Scientic Workflow Modeling by Case-Based Reasoning==
https://ceur-ws.org/Vol-2567/paper23.pdf
Interactive Assistance for Scientific Workflow
Modeling by Case-Based Reasoning
Christian Zeyen
Business Information Systems II
University of Trier
54286 Trier, Germany
zeyen@uni-trier.de
http://www.wi2.uni-trier.de
Abstract Developing scientific workflows is a demanding task that can
be supported in various ways. The range of approaches covers know-
ledge-intensive planning approaches to statistical approaches. Some of
the key challenges are knowledge engineering and elicitation of the target
problem to provide specific assistance. The presented research addresses
these challenges with a case-based approach. The goal is to provide an
interactive and self-improving assistant that collaborates with the devel-
oper not only to learn from newly created workflows but also to improve
the underlying domain model.
Keywords: Case-Based Reasoning · Interactive Assistance · Scientific
Workflows
1 Introduction
Scientific workflows [7] are designed for the computerized execution of data pro-
cessing and analysis tasks. Scientific workflow management systems (cf. [6,3]) are
powerful tools that provide graphical editors for composing workflows (also re-
ferred to as modeling) out of building blocks. However, developing scientific work-
flows remains a demanding task due to the large number of available components
and the wide variety of possible compositions. Various assistance approaches ex-
ist that can be roughly divided into three groups: statistical (cf. [4]), planning-
based (cf. [3]), and case-based approaches (cf. [2]). While semantic approaches
typically require extensive knowledge engineering beforehand, less knowledge-in-
tensive approaches such as statistical approaches need many available workflows
to derive best-practice recommendations. Previous case-based approaches often
come at high knowledge engineering costs and require a fully elaborated query
by the user. Conversational approaches address the latter but cause additional
effort for creating suitable dialogs. Moreover, previous work mainly focused on
attribute-value representations of workflows and did not incorporate graph rep-
resentations, which are typically used in workflow editors. Due to the complex
nature of the domain, approaches are often limited to solve certain problems
and require additional development work to keep up with continuously evolving
Copyright © 2019 for this paper by its authors. Use permitted
under Creative Commons License Attribution 4.0 International (CC BY 4.0).
workflow systems. In general, it is challenging to assist modeling for the full
range of composable workflows. For instance, it is common practice to use API
calls or to perform arbitrary script execution within workflow execution. Many
systems provide generic workflow components for this purpose, enabling to ex-
tend the functionality of built-in components. To provide an adequate assistance,
it is essential to continuously refine the domain model and to involve workflow
developers.
2 PhD Research Focus
Addressing the limitations of existing research, this PhD thesis research fol-
lows an interactive approach based on CBR to assist the modeling of scientific
workflows. In a nutshell, based on the current workflow under development, a
conversational retrieval is performed to find a suitable workflow serving as a
template. Subsequently, applicable adaptations are suggested and performed in
an interactive manner. Finally, a newly created workflow is stored as a new case
including the semantic information obtained from the user interaction.
2.1 Research Questions
The following research questions will be addressed for the domain of scientific
workflows:
1. How can a domain model be efficiently built based on available workflows
and semi-structured meta data.
2. How can workflows be efficiently retrieved with a conversational approach?
3. How can interactive workflow adaptation be realized?
4. How can results be presented and explained to users?
5. How can knowledge required for conversational retrieval and adaptation be
automatically derived from the case base and domain model?
6. How can user feedback be gathered and used to revise the knowledge model?
7. How can a conversational CBR approach be evaluated under real-world con-
ditions?
2.2 Research Plan
The research is embedded in two projects. Research question 1 is investigated in
the eXplore! 1 project, in which we build up the application domain and imple-
ment a basic case-based retrieval approach as an extension for the RapidMiner
workflow system [6].
1
eXplore! – Computer-based Modeling, Analysis, and Exploration as a Basis for
eScience in eHumanities is a cooperation project with the Trier Center for Digi-
tal Humanities (TCDH) at the University of Trier.
Further work towards interactivity is done in the scope of the EVER II 2
project (cf. [1]). In the first place, a focus is put on the conversational retrieval
of the workflow cases to investigate questions 2 and 4. For this purpose, we build
up upon previous work [10,5]. In this context, important issues are time-efficient
retrieval as well as question creation and sequence. Furthermore, previous eval-
uations [10,5] showed that an adequate presentation and explanation of results
is essential (question 4). With respect to question 7, the idea is to deploy the
research prototype for evaluating the assistance approach in public under real-
world condition. By this means, usage data can be collected that may also lead
to new insights into the process of workflow modeling.
Subsequent to conversational retrieval, the research activities focus on in-
teractive adaptation (question 3). In this step, existing adaptation approaches
will be integrated in the conversational framework. This step will also address
questions 4 and 7.
An overall focus is put on reducing the initial effort for building such an
assistance (question 1). Likewise the knowledge engineering effort at run-time
for adapting the knowledge model to changing circumstances such as an evolving
workflow system will be addressed. The research also addresses the knowledge
acquisition bottleneck by deriving knowledge from the case base and the domain
model (question 5) and by interactively acquiring knowledge during the workflow
modeling process (question 6).
3 Current Progress
Previous work already addressed some of the research questions according to the
research plan.
3.1 Application Domain
Question 1 was addressed during the implementation of the application domain.
To assist workflow development under real-world conditions, the research is ap-
plied to the RapidMiner software [6] that allows for the visual programming of
workflows for data and text mining tasks. RapidMiner is largely available un-
der an open source license while also being distributed commercially, has an
active user community, and is extensively expandable. In the eXplore! project,
RapidMiner workflows were modeled for text processing and analysis tasks. In
cooperation with digital humanists we investigated if workflow technology could
be beneficial for humanities research following the model of eScience [8]. Within
the project, we developed a prototypical modeling assistance as an extension
for RapidMiner. The case-based approach supports the retrieval of RapidMiner
workflows from a repository. A query for retrieval comprises the current work-
flow under development as well as keywords. The plugin also allows for extracting
2
EVER II – Extraction and Processing of Procedural Experiential Knowledge in Work-
flows – Quality, Interactivity, and Transferability is a cooperation project with the
Goethe University of Frankfurt
meta data about available workflow components and integrating the data into
the knowledge model. For each such component, the model comprises various
information such as textual descriptions, parameters, value ranges, default set-
tings, input and output ports, and data types.
3.2 Conversational Retrieval of Workflows
Concerning questions 2, 4, and 5, our previous work [10] investigates an inter-
active retrieval approach that incrementally elicits the relevant features of the
target problem. Thereby, we aim at reducing the effort and required expertise for
the definition of queries. In contrast to other conversational approaches, cases are
workflows that are represented as graphs. Questions are related to structural fea-
tures and are automatically constructed based on extracted workflow fragments.
Thereby, with respect to research question 5, the effort for defining suitable ques-
tions is omitted. An experimental evaluation with real users demonstrates that
those features are meaningful subjects of questions and suitable to distinguish
workflow cases from one another. The lessons learned from the evaluation are
valuable for investigating question 7 in future work.
3.3 Query Model for Workflow Retrieval
In [5], addressing research question 2, we investigate expression elements to be
used in a query language for scientific workflows. Based on a literature study,
we present a query model consisting of workflow structure and meta description
elements. The query model is evaluated with non-expert users in the RapidMiner
workflow domain. It was observed in the experiments that the workflow structure
is the most important query element followed by tags and keywords.
3.4 Automatic Adaptation of Workflows
In most recent work [9], we investigate automatic adaptation of scientific work-
flows as a first step towards answering question 3. With regard to our previous
works on the adaptation of business workflows, we discuss differences between
the workflow types and the resulting implications for transferring the adaptation
approaches. We present two adaptation approaches namely substitutional adap-
tation by generalization and specialization of single workflow steps and structural
adaptation with workflow streams that substitutes meaningful sub-components
in workflows. The approaches learn the required adaptation knowledge from
the case base, thus reducing the knowledge acquisition effort. An experimental
evaluation demonstrates that both adaptation approaches can be used to sig-
nificantly improve workflows towards a given query while mostly maintaining
the executability and semantic correctness of the workflows. The work lays the
foundation for interactive retrieval and adaptation of workflows that we consider
to be key components of an interactive assistance.
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