Goals of the Workshop
=====================
Industrial enterprises currently address the challenge of transforming
the ideas of the Internet of Things, Industry 4.0, Cyber-Physical
Systems, and similar concepts into reality. A direct application of the
IoT approach to the production chains in manufacturing companies is
presently not feasible, as there are many more parameters, but much less
available data compared to other big data application domains. Modern
production is characterized by vast amounts of data. However, this data
is neither easily accessible, interpretable, nor connected to gain
knowledge. Digital twins are supposed to provide a digital
representation of a production landscape, but the challenges in
building, maintaining, optimizing, and evolving digital twins in
inter-organizational production chains that cross several boundaries
have not been addressed yet in a systematic manner.

In this context, also various data management challenges have to be
addressed. Huge amounts of heterogeneous sensor data (numerical, audio,
video, etc.) have to be processed in real-time in order to control the
production machines. In addition, unstructured data from production
reports or external sources have also to be integrated to analyze and
optimize the production process. Well established mathematical models
for production engineering have to be integrated with data-driven
machine learning for cross-domain knowledge generation.

On the other hand, Industry 4.0 or the Industrial Internet of Things are
the basis for new applications and business opportunities. By connecting
physical objects, systems, machines, and applications, the data produced
by these objects may become a valuable resource, i.e., a product in its
own right. Thus, data management and analysis operations have to be
linked questions about value creation within and across enterprises.
These ideas raise new requirements in terms of trust, data security, and
data sovereignty, which also have to be considered in data-oriented
industrial applications.

The workshop aims at bringing together researchers from different
domains and to discuss the challenges for data science in industrial
settings. The workshop will provide a forum for the presentation of
recent research results, work-in-progress reports, vision papers, a
panel discussion, and an attractive keynote speaker.

Topics
======
The list of topics includes, but is not limited to:

* Data Stream Processing for Industrial Data
** Adaptive systems
** Data Stream Mining
** Concept Drift Adaption
** Machine Learning in Industrial Applications
** IoT Analytics

* Query Processing and Data Integration for Industrial Data
** Integration of Sensor Data
** Query Processing in Distributed Streaming Systems
** Data Integration and Change Propagation

* Distributed Architectures for Efficient Management of IoT Data
** Edge \& Fog Computing
** Blockchain for IoT \& Industry 4.0
** New Hardware Architectures for Industrial Data Management
** In-Network Data Processing and Analysis
** Distributed Communication Networks and Data Analysis

* Applications for Industry 4.0 and IoT
** Data Management for Manucfacturing Engineering
** Smart Homes, Smart Cities, Smart Facilities
** Data Analytics in Industrial Internet of Things

* Other Emerging Topics for Industrial Applications
** Modeling \& Reasoning for Industry 4.0, IoT, Digital Twins
** Data Security and Data Sovereignty
** Human-centered Interfaces
** Semantic Web and IoT, Web of Things
** Standardization in Industrial IoT Applications

Proceedings
===========

The proceedings of the workshop will be published online as a volume of the \href{http://www.ceur-ws.org}{CEUR Workshop
Proceedings}, ISSN 1613-0073, in joint volume with other EDBT workshops. A special issue in an international journal is also
planned, best papers from the workshop will be invited to the special issue.

Submission Guidelines
=====================

DSI4 welcomes the full paper submission of original and previously unpublished research.
All submissions will be peer-reviewed and, once accepted, they will be included in the workshop proceedings.

* Full papers can be up to 8 pages in length including all figures, tables, and references and should be
submitted as a PDF according to the EDBT format. The template can be found at
http://edbticdt2019.inesc-id.pt/?EDBT_CFP

* We also encourage the submission of short papers (up to 4 pages) reporting work in progress.

* Full-length papers are accepted through the online submission system of the workshop.

* Submissions in PDF have to be uploaded to the workshop's EasyChair submission site:
https://easychair.org/conferences/?conf=dsi4


Organization
============
Christoph Quix, Fraunhofer FIT, St. Augustin, Germany
Matthias Jarke, RWTH Aachen University, Germany
Albert Bifet, Télécom ParisTech, France
Miguel Correia, Instituto Superior Técnico (IST) of Universidade de Lisboa, Portugal
Charith Perera, Cardiff University, United Kingdom

Contact
=======
dsi4@dbis.rwth-aachen.de