Gargiulo, Francesco (2017) A Semantic Index for Linked Open Data and Big Data Applications. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | A Semantic Index for Linked Open Data and Big Data Applications |
| Creators: | Creators Email Gargiulo, Francesco f.gargiulo@cira.it |
| Date: | 10 April 2017 |
| Number of Pages: | 106 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Ingegneria informatica ed automatica |
| Ciclo di dottorato: | 28 |
| Coordinatore del Corso di dottorato: | nome email Garofalo, Francesco francesco.garofalo@unina.it |
| Tutor: | nome email Picariello, Antonio UNSPECIFIED Moscato, Vincenzo UNSPECIFIED |
| Date: | 10 April 2017 |
| Number of Pages: | 106 |
| Keywords: | 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 |
| Date Deposited: | 09 Jun 2017 08:07 |
| Last Modified: | 14 Mar 2018 13:35 |
| URI: | http://www.fedoa.unina.it/id/eprint/11524 |
| DOI: | 10.6093/UNINA/FEDOA/11524 |
Collection description
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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