Papers Papers/2020



Paper Unlock

Authors: André Pomp ; Alexander Paulus ; Sabina Jeschke and Tobias Meisen

Affiliation: RWTH Aachen University, Germany

ISBN: 978-989-758-248-6

ISSN: 2184-4992

Keyword(s): Semantic Computing, Semantic Model, Knowledge Graph, Internet of Things, Data Processing.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Cloud Computing ; Coupling and Integrating Heterogeneous Data Sources ; Data Communication Networking ; Data Engineering ; Databases and Information Systems Integration ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Internet of Things ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Semantic Web Technologies ; Sensor Networks ; Services Science ; Software Agents and Internet Computing ; Software and Architectures ; Symbolic Systems ; Telecommunications

Abstract: Over the last years, many Internet of Things (IoT) platforms have been developed to manage data from public and industrial environmental settings. To handle the upcoming amounts of structured and unstructured data in those fields, a couple of these platforms use ontologies to model the data semantics. However, generating ontologies is a complex task since it requires to collect and model all semantics of the provided data. Since the (Industrial) IoT is fast and continuously evolving, a static ontology will not be able to model each requirement. To overcome this problem, we developed the platform ESKAPE, which uses semantic models in addition to data models to handle batch and streaming data on an information focused level. Our platform enables users to process, query and subscribe to heterogeneous data sources without the need to consider the data model, facilitating the creation of information products from heterogeneous data. Instead of using a pre-defined ontology, ESKAPE uses a kn owledge graph which is expanded by semantic models defined by users upon their data sets. Utilizing the semantic annotations enables data source substitution and frees users from analyzing data models to understand their content. A first prototype of our platform was evaluated by a user study in form of a competitive hackathon, during which the participants developed mobile applications based on data published on the platform by local companies. The feedback given by the participants reveals the demand for platforms that are capable of handling data on a semantic level and allow users to easily request data that fits their application. (More)


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Pomp, A.; Paulus, A.; Jeschke, S. and Meisen, T. (2017). ESKAPE: Information Platform for Enabling Semantic Data Processing. In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-248-6 ISSN 2184-4992, pages 644-655. DOI: 10.5220/0006324906440655

author={André Pomp. and Alexander Paulus. and Sabina Jeschke. and Tobias Meisen.},
title={ESKAPE: Information Platform for Enabling Semantic Data Processing},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - ESKAPE: Information Platform for Enabling Semantic Data Processing
SN - 978-989-758-248-6
IS - 2184-4992
AU - Pomp, A.
AU - Paulus, A.
AU - Jeschke, S.
AU - Meisen, T.
PY - 2017
SP - 644
EP - 655
DO - 10.5220/0006324906440655

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.