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An integration-oriented ontology to govern evolution in big data ecosystems

Author
Nadal, S.; Romero, O.; Abello, A.; Vassiliadis , P.; Vansummeren, S.
Type of activity
Presentation of work at congresses
Name of edition
19th International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data
Date of publication
2017
Presentation's date
2017-03-21
Book of congress proceedings
Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017
First page
1
Last page
10
Publisher
CEUR-WS.org
Project funding
(GENESIS) Generation and Evolution of Smart APIs
SUpporting evolution and adaptation of PERsonalized Software by Exploiting contextual Data and End-user feedback
Repository
http://hdl.handle.net/2117/113387 Open in new window
URL
http://ceur-ws.org/Vol-1810/DOLAP_paper_09.pdf Open in new window
Abstract
Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in its original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving, forcing data analysts using it need to adapt their analytical processes after each release. This gets more challenging when aiming to perform an integrated or historical analysis of multiple sources. To cope with such complexity, in this paper we present the Big Data Integratio...
Citation
Nadal, S., Romero, O., Abelló, A., Vassiliadis , P., Vansummeren, S. An integration-oriented ontology to govern evolution in big data ecosystems. A: International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data. "Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017". Venice: CEUR-WS.org, 2017, p. 1-10.
Keywords
Evolution, Modeling, Semi-structured data, Stream data
Group of research
DTIM - Database Technologies and lnformation Management Group
IMP - Information Modelling and Processing
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

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