[DL] Final CfP 1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making

[Ulrich Reimer]

Final Call for Papers:
1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making

See full version at: https://workshop-cmai.github.io/2020/

The workshop will be held in conjunction with the ER 2020 conference:
https://er2020.big.tuwien.ac.at/

Call for Papers
--------------
Artificial intelligence (AI) is front and center in the data-driven
revolution that has been taking place in the last couple of years with
the increasing availability of large amounts of data (“big data”) in
virtually every domain. The now dominant paradigm of data-driven AI,
powered by sophisticated machine learning algorithms, employs big data
to build intelligent applications and support fact-based decision
making. The focus of data-driven AI is on learning (domain) models and
keeping those models up-to-date by using statistical methods over big
data, in contrast to the manual modeling approach prevalent in
traditional, knowledge-based AI.
While data-driven AI has led to significant breakthroughs, it also comes
with a number of disadvantages. First, models generated by machine
learning algorithms often cannot be inspected and understood by a human
being, thus lacking explainability. Furthermore, integration of
preexisting domain knowledge into learned models – prior to or after
learning – is difficult. Finally, correct application of data-driven AI
depends on the domain, problem, and organizational context while
considering human aspects as well. Conceptual modeling can be the key to
applying data-driven AI in a meaningful, correct, and time-efficient way
while improving maintainability, usability, and explainability.

Topics of Interest
-----------------
The topics of interest include, but are not limited to, the following:
- Combining generated and manually engineered models
- Combining symbolic with sub-symbolic models
- Conceptual (meta-)models as background knowledge for model learning
- Explainability of learned models
- Conceptual models for enabling explainability, model validation and
plausibility checking
- Trade-off between explainability and model performance
- Trade-off between comprehensibility of an explanation and its completeness
- Reasoning in generated models
- Data-driven modeling support
- Learning of meta-models
- Automatic, incremental model adaptation
- Model-driven guidance and support for data analytics lifecycle
- Conceptual models for supporting users with conducting data analysis

Important Dates
--------------
Paper Submission: 27 July 2020
Author Notification: 17 August 2020
Camera-Ready Paper Submission: 7 September 2020

Submission Guidelines
--------------------
Submitted papers must not exceed 10 pages. Accepted papers will be
published in the LNCS series by Springer. Note that only accepted papers
presented in the workshop by at least one author will be published.

Workshop Organizers
------------------
Dominik Bork, University of Vienna, Austria
Peter Fettke, German Research Center for Artificial Intelligence, Germany
Wolfgang Maass, German Research Center for Artificial Intelligence, Germany
Ulrich Reimer, University of Applied Sciences St. Gallen, Switzerland
Christoph G. Schuetz, Johannes Kepler University Linz, Austria
Marina Tropmann-Frick, University of Applied Sciences Hamburg, Germany
Eric S. K. Yu, University of Toronto, Canada


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