BD4EI 2018: Industrial Big Data Platforms Enabling Enterprise Interoperability for Smart Services Fraunhofer IPK Berlin, Germany, March 20-21, 2018 |
Conference website | http://iesa2018.ipk.fraunhofer.de/4956/ |
Submission link | https://easychair.org/conferences/?conf=bd4ei |
Abstract registration deadline | December 15, 2017 |
Submission deadline | March 6, 2018 |
Digitization of products and services is the main strategy for enterprises to stay on the cutting edge of innovation. Digitization goes hand in hand with the availability of new online data sources that provide information on the context in which these products and services are used, thus providing opportunities to customize and personalize the products and services according to the situation (and perceived needs) at hand. Such data sources include sensors, loggers and social media. There is so much data that existing hardware and software are challenged to handle the data and to extract information with economic value. This is the area of big data.
In order to avoid that big data management functions (e.g., collection, pre-processing, storage, and analysis of data) are separately developed by each vendor and for each data-driven application, big data platforms have been introduced. Big data platforms not only offer one cohesive solution with ease of use for developing, deploying, operating and managing big data to application developers, they also reduce the interoperability problems that come with multi-vendor solutions.
However, there is not a single accepted platform across or even within application domains, which hampers interoperability between platforms. Internet of Things (IoT) developments gave a further boost to big data-driven applications adopted by enterprises in various domains, but they also contributed to the abundance and diversity of solutions, standards and platforms.
Relevant initiatives to address this problem include EC’s Digitising European Industry – Digital Industrial Platforms, Fraunhofer’s Reference Architecture for Industrial Data Space, the Industrial Internet Consortium’s Layered Databus, and the European H2020 project on Interoperability of Heterogeneous IoT Platforms. The architectures and platforms developed in these initiatives require different levels of interoperability. Current approaches mostly focus on syntactic interoperability, and when semantic interoperability is addressed, usually by creating domain-specific ontologies, performance issues arise. Moreover, detection of situations of interests (on which the data-driven applications have to react) may have false positives as well as false negatives due to the quality of data and semantic translations, thus affecting the effectiveness of the smart services supported by applications. Other concerns are the security and privacy of data managed by and communicated between platforms, and the principles that govern the management of (meta-) data.
The relevance of big data and IoT, and thus of addressing interoperability and related issues, is obvious in many industrial application domains, including manufacturing, agriculture, environmental monitoring, transportation, healthcare, and city management. The objective of this workshop is to bring together researchers and practitioners working on big data platforms, smart enterprise services and enterprise interoperability, and to identify and discuss achievements, challenges and visions within and across these application domains.
Submission Guidelines
Submitted papers should be 4-6 pages in ISTE format (see http://iesa2018.ipk.fraunhofer.de/4956/ for formatting guidelines) and contain original work of the authors. The submission should be done using the submission webpage https://easychair.org/conferences/?conf=bd4ei on EasyChair. Each submission will be reviewed by at least three members of the Programme Committee. Accepted papers should be presented at the workshop by one of the authors.
List of Topics
- Big data platforms
- Internet of Things
- Semantic interoperability
- Syntactic and semantic translations
- Ontology alignment
- Data-driven smart enterprise services
- Data sources integration
- Big data sharing
- Big data as a service
- Industrial data space
- Big data standards
- Data-driven enterprise digitization
- Domain-specific applications: manufacturing etc.
- Ownership and sovereignty issues of big data
- Cloud vs. peer-to-peer approaches for industrial analytics
- Data interoperability in a business interoperability context
Committees
Program Committee
- Luiz Olavo Bonino da Silva Santos (Dutch Techcentre for Life Science, The Netherlands)
- Davide Dalle Carbonare (Engineering Ingegneria Informatica, Italy)
- Patricia Dockhorn Costa (UFES, Brasil)
- Thorsten Huelsmann (Industrial Data Space, Germany)
- Maria Iacob (University of Twente, The Netherlands)
- Ernoe Kovacs (NECLAB, Germany)
- Oscar Lazaro (Innovalia, Spain)
- Joao Moreira (University of Twente, The Netherlands)
- Stefano de Panfilis (FIWARE Foundation, Germany)
- Jorge Rodriguez (ATOS, Spain)
Organizing committee
- Marten van Sinderen, University of Twente, The Netherlands
- Sergio Gusmeroli, Politecnico di Milano, Italy
Publication
BD4EI 2018 proceedings will be published in a post-conference book by ISTE/Wiley.
Venue
The workshop will be held in conjunction with the I-ESA 2018 conference at Fraunhofer IPK in the center of Berlin in Germany.
Contact
All questions about submissions should be emailed to m.j.vansinderen@utwente.nl.