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Dataspaces: Where Structure and Schema Meet

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Learning Structure and Schemas from Documents

Part of the book series: Studies in Computational Intelligence ((SCI,volume 375))

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Abstract

In this chapter we investigate the crucial problem that poses the bases to the concept of dataspaces: the need for human interaction/intervention in the process of organizing (getting the structure of) unstructured data. We survey the existing techniques behind dataspaces to overcome that need, exploring the structure of a dataspace along three dimensions: dataspace profiling, querying and searching and application domain. We will further explore existing projects focusing on dataspaces, induction of data structure from documents, and data models where data schema and documents structure overlaps will be reviewed, such as Apache Hadoop, Cassandra on Amazon Dynamo, Google BigTable model and other DHT-based flexible data structures, Google Fusion Tables, iMeMex, U-DID, WebTables and Yahoo! SearchMonkey.

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Author information

Authors and Affiliations

  1. University of Cagliari, Italy

    Maurizio Atzori & Nicoletta Dessì

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  1. Maurizio Atzori
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  2. Nicoletta Dessì
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Editors and Affiliations

  1. University of New York Tirana, Rr. Komuna E Parisit,, Tirana, Albania

    Marenglen Biba

  2. Technical University of Catalonia, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

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Atzori, M., Dessì, N. (2011). Dataspaces: Where Structure and Schema Meet. In: Biba, M., Xhafa, F. (eds) Learning Structure and Schemas from Documents. Studies in Computational Intelligence, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22913-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-22913-8_5

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