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  • xWCPS: Bridging the Gap Between Array and Semi-structured Data

    The ever growing amount of information collected by scientific instruments and the presence of descriptive metadata accompanying them calls for a unified way of querying over array and semi-structured data. We present xWCPS, a novel query language that bridges the path between these two different worlds, enhancing the expressiveness and user-friendliness of previous approaches.

  • Technoeconomic analysis of PON architectures for FTTH deployments (Next Generation access networks)

    In this paper the cost for the deployment of a PON FTTH network is calculated in terms of NPV, IRR and payback period. A comparison is made between three PON technologies: GPON, XGPON and NG-PON2. Different scenarios regarding population density and bitrates are examined. It is shown that for high data rate scenarios in areas with high population density NG-PON2 can become a viable and future proof alternative to GPON and XGPON.

  • Dataflow Processing and Optimization on Grid and Cloud

    Complex on-demand data retrieval and processing is a characteristic of several applications and combines the notions of querying & search, information filtering & retrieval, data transformation & analysis, and other data manipulations. Such rich tasks are typically represented by data processing graphs, having arbitrary data operators as nodes and their producer-consumer interactions as edges.

  • The gCube interoperability framework

    Interoperability is one of the most challenging issues in the design and operation of large-scale computing infrastructures for multidisciplinary research. It requires solutions that can “embrace” heterogeneity, i.e. accommodate multiple incarnations of similar resources, as well as “hide” it, i.e. provide consumers with a homogeneous view of diverse resources. This paper overviews the measures put in place in the D4Science infrastructure to address a range of interoperability problems.

  • A Distributed Infrastructure for Earth-Science Big Data Retrieval

    Earth-Science data are composite, multidimensional and of significant size, and as such, continue to pose a number of on-going problems regarding their management. With new and diverse information sources emerging as well as rates of generated data continuously increasing, a persistent challenge becomes more pressing: to make the information existing in multiple heterogeneous resources readily available. The widespread use of the XML data-exchange format has enabled the rapid accumulation of semi-structured metadata for Earth-Science data.

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