Gargiulo, Francesco (2017) A Semantic Index for Linked Open Data and Big Data Applications. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: A Semantic Index for Linked Open Data and Big Data Applications
Autori:
AutoreEmail
Gargiulo, Francescof.gargiulo@cira.it
Data: 10 Aprile 2017
Numero di pagine: 106
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Ingegneria informatica ed automatica
Ciclo di dottorato: 28
Coordinatore del Corso di dottorato:
nomeemail
Garofalo, Francescofrancesco.garofalo@unina.it
Tutor:
nomeemail
Picariello, Antonio[non definito]
Moscato, Vincenzo[non definito]
Data: 10 Aprile 2017
Numero di pagine: 106
Parole chiave: Large databases, distributed index, multiple queries, k-nearest neighbor query algorithm, semantic query
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Depositato il: 09 Giu 2017 08:07
Ultima modifica: 14 Mar 2018 13:35
URI: http://www.fedoa.unina.it/id/eprint/11524
DOI: 10.6093/UNINA/FEDOA/11524

Abstract

This work proposes a new approach to index multidimensional data based on kd-trees and proposes also a novel approach to query processing. The indexing data structure is distributed across a network of "peers", where each one hosts a part of the tree and uses message passing for communication among nodes. The advantages of this kind of approach are mainly two: it is possible to i) handle a larger number of nodes and points than a single peer based architecture and ii) to run in an efficient way the elaboration of multiple queries. In particular, we propose a novel version of the k-nearest neighbor algorithm that is able to start a query in a randomly chosen peer. Furthrmore, it returns the results without traverse the peer containing the root. Preliminary experiments demonstrated that on average in about 65% of cases a query starting in a random node, does not involve the peer containing the root of the tree. Also, on average in about 98% of cases, it returns the results without involving the root peer. This work also proposes an approach to cope with textual data and provides a way to perform semantic query over the text.

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