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El autor Alberto Abello ha publicado 2 artículo(s):

1 - Incremental Consolidation of Data-Intensive Multi-Flows

En Transactions on Knowledge and Data Engineering, 28(5). IEEE Press, May 2016. Páginas 1203-1216. ISSN: 1041-4347. DOI: 10.1109/TKDE.2016.2515609 Ã?ndice de impacto: JCR-Science Edition 2015, 2.476 Quartil i área: Q1, COMPUTER SCIENCE, INFORMATION SYSTEMS, 17/143 Business intelligence (BI) systems depend on efficient integration of disparate and often heterogeneous data. The integration of data is governed by data-intensive flows and is driven by a set of information requirements. Designing such flows is in general a complex process, which due to the complexity of business environments is hard to be done manually. In this paper, we deal with the challenge of efficient design and maintenance of data-intensive flows and propose an incremental approach, namely CoAl , for semi-automatically consolidating data-intensive flows satisfying a given set of information requirements. CoAl works at the logical level and consolidates data flows from either high-level information requirements or platform-specific programs. As CoAl integrates a new data flow, it opts for maximal reuse of existing flows and applies a customizable cost model tuned for minimizing the overall cost of a unified solution. We demonstrate the efficiency and effectiveness of our approach through an experimental evaluation using our implemented prototype.

Autores: Petar Jovanovic / Oscar Romero / Alkis Simitsis / Alberto Abello / 
Palabras Clave: Business Intelligence - Data warehousing - Data-intensive flows - Workflow management

2 - A software reference architecture for semantic-aware Big Data systems

Information & Software Technology 90: 75-92 (2017)Impact Factor JCR 2017: 2.694 recibidas en 2017 (Google Scholar, 2-3-2018): 3———————————- 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.

Autores: Sergi Nadal / Víctor Herrero / Oscar Romero / Alberto Abello / Xavi Franch / Stijn Vansummeren / Danilo Valerio / 
Palabras Clave: big data - Data analysis - data management - Semantic-aware - Software reference architecture