loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Pascal Hirmer ; Peter Reimann ; Matthias Wieland and Bernhard Mitschang

Affiliation: Institute of Parallel and Distributed Systems and University of Stuttgart, Germany

Keyword(s): Data Mashups, Ad-hoc Data Integration, Patterns, Data Flow, Sensor Data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Collaboration and e-Services ; Data Engineering ; Data Management and Quality ; e-Business ; Enterprise Information Systems ; Information Integration ; Integration/Interoperability ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Management of Sensor Data ; Ontologies and the Semantic Web ; Semi-Structured and Unstructured Data ; Symbolic Systems

Abstract: Today, a multitude of highly-connected applications and information systems hold, consume and produce huge amounts of heterogeneous data. The overall amount of data is even expected to dramatically increase in the future. In order to conduct, e.g., data analysis, visualizations or other value-adding scenarios, it is necessary to integrate specific, relevant parts of data into a common source. Due to oftentimes changing environments and dynamic requests, this integration has to support ad-hoc and flexible data processing capabilities. Furthermore, an iterative and explorative trial-and-error integration based on different data sources has to be possible. To cope with these requirements, several data mashup platforms have been developed in the past. However, existing solutions are mostly non-extensible, monolithic systems or applications with many limitations regarding the mentioned requirements. In this paper, we introduce an approach that copes with these issues (i) by the introducti on of patterns to enable decoupling from implementation details, (ii) by a cloud-ready approach to enable availability and scalability, and (iii) by a high degree of flexibility and extensibility that enables the integration of heterogeneous data as well as dynamic (un-)tethering of data sources. We evaluate our approach using runtime measurements of our prototypical implementation. (More)

CC BY-NC-ND 4.0

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 3.237.16.210

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:
Hirmer, P.; Reimann, P.; Wieland, M. and Mitschang, B. (2015). Extended Techniques for Flexible Modeling and Execution of Data Mashups. In Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA, ISBN 978-989-758-103-8; ISSN 2184-285X, pages 111-122. DOI: 10.5220/0005558201110122

@conference{data15,
author={Pascal Hirmer. and Peter Reimann. and Matthias Wieland. and Bernhard Mitschang.},
title={Extended Techniques for Flexible Modeling and Execution of Data Mashups},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA,},
year={2015},
pages={111-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005558201110122},
isbn={978-989-758-103-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - DATA,
TI - Extended Techniques for Flexible Modeling and Execution of Data Mashups
SN - 978-989-758-103-8
IS - 2184-285X
AU - Hirmer, P.
AU - Reimann, P.
AU - Wieland, M.
AU - Mitschang, B.
PY - 2015
SP - 111
EP - 122
DO - 10.5220/0005558201110122

0123movie.net