Aplicació per fer consultes de cubs de dades usant MapReduce

Finalista del Premi Cercle Fiber al millor Projecte Final de Carrera (curs 2010-2011)Català: L'objectiu d'aquest projecte és implementar un sistema ETL per treballar amb cubs de dades. És a dir, un sistema per a fer consultes sobre un cub de dades a través dels valors de les seves dimensions. La seva implementació es farà usant MapReduce i HBase de Hadoop

Self-healing Multi-Cloud Application Modelling

Cloud computing market forecasts and technology trends confirm that Cloud is an IT disrupting phenomena and that the number of companies with multi-cloud strategy is continuously growing. Cost optimization and increased competitiveness of companies that exploit multi-cloud will only be possible when they are able to leverage multiple cloud offerings, while mastering both the complexity of multiple cloud provider management and the protection against the higher exposure to attacks that multi-cloud brings. This paper presents the MUSA Security modelling language for multi-cloud applications which is based on the Cloud Application Modelling and Execution Language (CAMEL) to overcome the lack of expressiveness of state-of-the-art modelling languages towards easing: a) the automation of distributed deployment, b) the computation of composite Service Level Agreements (SLAs) that include security and privacy aspects, and c) the risk analysis and service match-making taking into account not only functionality and business aspects of the cloud services, but also security aspects. The paper includes the description of the MUSA Modeller as the Web tool supporting the modelling with the MUSA modelling language. The paper introduces also the MUSA SecDevOps framework in which the MUSA Modeller is integrated and with which the MUSA Modeller will be validated.The MUSA project leading to this paper has received funding from the European Union’s Horizon 2020 research and innovation pro- gramme under grant agreement No 644429

Aplicació per fer consultes de cubs de dades usant MapReduce

Finalista del Premi Cercle Fiber al millor Projecte Final de Carrera (curs 2010-2011)Català: L'objectiu d'aquest projecte és implementar un sistema ETL per treballar amb cubs de dades. És a dir, un sistema per a fer consultes sobre un cub de dades a través dels valors de les seves dimensions. La seva implementació es farà usant MapReduce i HBase de Hadoop

Aplicació per fer consultes de cubs de dades usant MapReduce

Finalista del Premi Cercle Fiber al millor Projecte Final de Carrera (curs 2010-2011)Català: L'objectiu d'aquest projecte és implementar un sistema ETL per treballar amb cubs de dades. És a dir, un sistema per a fer consultes sobre un cub de dades a través dels valors de les seves dimensions. La seva implementació es farà usant MapReduce i HBase de Hadoop

Tuning small analytics on Big Data: Data partitioning and secondary indexes in the Hadoop ecosystem

In the recent years the problems of using generic storage (i.e., relational) techniques for very specific applications have been detected and outlined and, as a consequence, some alternatives to Relational DBMSs (e.g., HBase) have bloomed. Most of these alternatives sit on the cloud and benefit from cloud computing, which is nowadays a reality that helps us to save money by eliminating the hardware as well as software fixed costs and just pay per use. On top of this, specific querying frameworks to exploit the brute force in the cloud (e.g., MapReduce) have also been devised. The question arising next tries to clear out if this (rather naive) exploitation of the cloud is an alternative to tuning DBMSs or it still makes sense to consider other options when retrieving data from these settings.; In this paper, we study the feasibility of solving OLAP queries with Hadoop (the Apache project implementing MapReduce) while benefiting from secondary indexes and partitioning in HBase. Our main contribution is the comparison of different access plans and the definition of criteria (i.e., cost estimation) to choose among them in terms of consumed resources (namely CPU, bandwidth and I/O).Peer Reviewe

PRIMEBALL: A Parallel Processing Framework Benchmark for Big Data Applications in the Cloud

In this paper, we draw the specifications of a novel benchmark for comparing parallel processing frameworks in the context of big data applications hosted in the cloud. We aim at filling several gaps in already existing cloud data processing benchmarks, which lack a real-life context for their processes, thus losing relevance when trying to assess performance for real applications. Hence, we propose a fictitious news site hosted in the cloud that is to be managed by the framework under analysis, together with several objective use case scenarios and measures for evaluating system performance. The main strengths of our benchmark are parallelization capabilities supporting cloud features and big data properties.Comment: 5th TPC Technology Conference on Performance Evaluation and Benchmarking (VLDB/TPCTC 13), Riva del Garda : Italy (2013

Tuning small analytics on Big Data: Data partitioning and secondary indexes in the Hadoop ecosystem

In the recent years the problems of using generic storage (i.e., relational) techniques for very specific applications have been detected and outlined and, as a consequence, some alternatives to Relational DBMSs (e.g., HBase) have bloomed. Most of these alternatives sit on the cloud and benefit from cloud computing, which is nowadays a reality that helps us to save money by eliminating the hardware as well as software fixed costs and just pay per use. On top of this, specific querying frameworks to exploit the brute force in the cloud (e.g., MapReduce) have also been devised. The question arising next tries to clear out if this (rather naive) exploitation of the cloud is an alternative to tuning DBMSs or it still makes sense to consider other options when retrieving data from these settings.; In this paper, we study the feasibility of solving OLAP queries with Hadoop (the Apache project implementing MapReduce) while benefiting from secondary indexes and partitioning in HBase. Our main contribution is the comparison of different access plans and the definition of criteria (i.e., cost estimation) to choose among them in terms of consumed resources (namely CPU, bandwidth and I/O).Peer Reviewe

The interest of the Spanish network of investigators in back pain for rehabilitation physician

Background: The Spanish Back Pain Research Network (REIDE) brings together teams of researchers and clinicians who are interested in nonspecific neck and back pain (BP). Its objective is to improve the efficacy, safety, effectiveness, and efficiency of the clinical management of BP. Method: The Network welcomes clinicians and researchers interested in BP. The only requirement to become a member of REIDE is to take part in one of its research projects, and any member can propose a new one. The Network supports those projects that are of interest to two or more groups by assuming their administration and management, which allows the researchers to focus on their task. Its working method ensures methodological quality, a multidisciplinary approach, and the clinical relevance of those projects that are carried out. Results: 179 researchers from 11 areas in Spain are involved in REIDE, including experts in all of the relevant fields of BP research. Most Spanish studies on BP that have been published in international scientific journals come from the teams involved in REIDE, and it currently has 13 ongoing research projects. Conclusions: The Network can help to enhance research among rehabilitation specialists who are interested in BP, and can contribute to the development of research projects which are of interest to the specialty. © 2005 Sociedad Española de Rehabilitación y Medicina Física (SERMEF) y Elsevier España, S.L