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The Disjunctive Datalog System DLV

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Datalog Reloaded (Datalog 2.0 2010)

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Abstract

DLV is one of the most successful and widely used answer set programming (ASP) systems. It supports a powerful language extending Disjunctive Datalog with many expressive constructs, including aggregates, strong and weak constraints, functions, lists, and sets. The system provides database connectivity offering a simple way for powerful reasoning on top of relational databases. In this paper, we provide an ample overview of the DLV system. We illustrate its input language and give indications on how to use it for representing knowledge. We also provide a panorama on the system architecture and the main optimizations it incorporates. We then focus on DLVDB, an extension of the basic system which allows for tight coupling with traditional database systems. Finally, we report on a number industrial applications which rely on DLV.

This research has been partly supported by Regione Calabria and EU under POR Calabria FESR 2007-2013 within the PIA project of DLVSYSTEM s.r.l., and by MIUR under the PRIN project LoDeN.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Calabria, 87030, Rende, CS, Italy

    Mario Alviano, Wolfgang Faber, Nicola Leone, Simona Perri, Gerald Pfeifer & Giorgio Terracina

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  1. Mario Alviano
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  2. Wolfgang Faber
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  3. Nicola Leone
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  4. Simona Perri
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  5. Gerald Pfeifer
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  6. Giorgio Terracina
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Editors and Affiliations

  1. Department of Computer Science, Oxford University, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Oege de Moor

  2. Department of Computer Science, University of Oxford, UK

    Georg Gottlob , Tim Furche  & Andrew Sellers ,  & 

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Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G., Terracina, G. (2011). The Disjunctive Datalog System DLV. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds) Datalog Reloaded. Datalog 2.0 2010. Lecture Notes in Computer Science, vol 6702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24206-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-24206-9_17

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