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A software reference architecture for semantic-aware big data systems

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INFSOF-D-16-00343.pdf (1,800Mb)
 
10.1016/j.infsof.2017.06.001
 
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hdl:2117/108738

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Nadal Francesch, SergiMés informacióMés informacióMés informació
Herrero Otal, Víctor
Romero Moral, ÓscarMés informacióMés informacióMés informació
Abelló Gamazo, AlbertoMés informacióMés informacióMés informació
Franch Gutiérrez, JavierMés informacióMés informacióMés informació
Vansummeren, Stijn
Valerio, Danilo
Document typeArticle
Defense date2016-06-13
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectSUPERSEDE - SUpporting evolution and adaptation of PERsonalized Software by Exploiting contextual Data and End-user feedback (EC-H2020-644018)
Abstract
Context: Big Data systems are a class of software systems that ingest, store, process and serve massive amounts of heterogeneous data, from multiple sources. Despite their undisputed impact in current society, their engineering is still in its infancy and companies find it difficult to adopt them due to their inherent complexity. Existing attempts to provide architectural guidelines for their engineering fail to take into account important Big Data characteristics, such as the management, evolution and quality of the data. Objective: In this paper, we follow software engineering principles to refine the ¿-architecture, a reference model for Big Data systems, and use it as seed to create Bolster, a software reference architecture (SRA) for semantic-aware Big Data systems. Method: By including a new layer into the ¿-architecture, the Semantic Layer, Bolster is capable of handling the most representative Big Data characteristics (i.e., Volume, Velocity, Variety, Variability and Veracity). Results: We present the successful implementation of Bolster in three industrial projects, involving five organizations. The validation results show high level of agreement among practitioners from all organizations with respect to standard quality factors. Conclusion: As an SRA, Bolster allows organizations to design concrete architectures tailored to their specific needs. A distinguishing feature is that it provides semantic-awareness in Big Data Systems. These are Big Data system implementations that have components to simplify data definition and exploitation. In particular, they leverage metadata (i.e., data describing data) to enable (partial) automation of data exploitation and to aid the user in their decision making processes. This simplification supports the differentiation of responsibilities into cohesive roles enhancing data governance.
CitationNadal, S., Herrero, V., Romero, O., Abello, A., Franch, X., Vansummeren, S., Valerio, D. A software reference architecture for semantic-aware big data systems. "Information and software technology", 13 Juny 2016, vol. 90, p. 75-92. 
URIhttp://hdl.handle.net/2117/108738
DOI10.1016/j.infsof.2017.06.001
ISSN0950-5849
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0950584917304287
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  • MPI - Modelització i processament de la Informació - Articles de revista [48]
  • inSSIDE - integrated Software, Service, Information and Data Engineering - Articles de revista [113]
  • Departament d'Enginyeria de Serveis i Sistemes d'Informació - Articles de revista [210]
  • GESSI - Grup d'Enginyeria del Software i dels Serveis - Articles de revista [56]
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