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.
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