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Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness., The work shown in this article has been funded jointly by European Commission under the Cooperation Programme, Horizon2020 grant agreement No 690116 (EUBra-BIGSEA) and the Min-istériode Ciência,Tecnologiae Inovação(MCTI) from Brazil

This work introduces Ophidia, a big data analytics research effort aiming at supporting the access, analysis and mining of scientific (n-dimensional array based) data. The Ophidia platform extends, in terms of both primitives and data types, current relational database system implementations (in particular MySQL) to enable efficient data analysis tasks on scientific array-based data. To enable big data analytics it exploits well-known scientific numerical libraries, a distributed and hierarchical storage model and a parallel software framework based on the Message Passing Interface to run from single tasks to more complex dataflows. The current version of the Ophidia platform is being tested on NetCDF data produced by CMCC climate scientists in the context of the international Coupled Model Intercomparison Project Phase 5 (CMIP5).

Today many DataGrid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage and promote the publication, sharing and integration of scientifica data (distributed across several Virtual Organizations) in a more open manner than is currently the case, and many e-Science pojects have an urgent need to interconnect legacy and independently operated databases through a set os data access and integration services. The complexity of data management within a Computational Grid comes from the distribution, scale and heterogeneity of data sources. A set of dynamic and adaptive services could address specific issues related to automatic data management providing high performance and transparency as well as fully exploiting a grid infrastructure. These services should involved data migration and integration, discovery of data sources and so on, providing a transparent and dynamic layer of data virtualization. In this pape we introduce the Grid-DBMS concept, a framework for dynamic data management in a grid enviroment, highlighting its requirements, architecture, components and services. We also present an overview about the Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI of the University of Lecce, which represents a partial implementation of a Grid-DBMS for the Globus Community.

The goal of this paper is to show that computer modelling techniques can be used to solve the real life problem of Diesel Engine performance improvement. The purpose of our work is to achieve the lowest emissions level and improved fuel efficiency with respect to European Norm for emissions. Particularly, we are interested to reduce NO + HC and soot emissions and to maximize the PMI, a pressure proportional to engine power. These parameters depend on combustion chamber geometry too, so we propose its optimization turning to the new micro Genetic Algorithms (micro-GA) technique. The idea consists in the automated random generation of a lot of meshes. each of these representing a different chamber geometry, with respect to some common geometric constraints: then in the use of the micro-GA to optimize, at each iteration, the results obtained in the previous steps. The innovative feature of our work is the multiobjective nature of the optimization process. This is the main reason to chose micro-GA rather than simple Genetic Agorithms. Emissions level and fuel efficiency can be evaluated using a modified version of KIVA3 code that outputs three values, each of these related to one of the three specific fitness functions to be maximized. The optimization process involves the execution of a lot of KIVA3 simulations to calculate fitness values of the chamber geometries taken in consideration during all of the optimization steps. We propose the use of Grid Computing technologies to increase the performance of the KIVA-micro-GA, showing how a distributed environment allows to reduce the computational time needed by the optimization process, taking advantage of intrinsic parallelism of micro-GA. In fact, their structure allows executing simultaneously KIVA3 simulations over the random meshes and over the geometries that populate the micro-population at each iteration. The services offered by the system are the micro-GA parameters definition. the submission of the optimization process and the monitoring of the process status. A trusted user can access the implemented services using a grid portal, called DESGrid (Grid for Diesel Engine Simulation). The analysis of the results, achieved by execution of KIMA-micro-GA on three ES40 Compaq nodes, each one equipped by four processors, shows a good reduction in both emissions and fuel consumption. In the paper we show numerical values and related geometries representation obtained after the first steps of the global optimization process execution.