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Compressed Indexes for Fast Search of Semantic Data.

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Title: Compressed Indexes for Fast Search of Semantic Data.
Authors: Perego, Raffaele1 raffaele.perego@isti.cnr.it, Pibiri, Giulio Ermanno1 giulio.ermanno.pibiri@isti.cnr.it, Venturini, Rossano2 rossano.venturini@unipi.it
Source: IEEE Transactions on Knowledge & Data Engineering. Sep2021, Vol. 33 Issue 9, p3187-3198. 12p.
Subjects: Pattern matching, Problem solving, Data structures, RDF (Document markup language)
Abstract: The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30–60 percent less space and speeding up query execution by a factor of 2 – 81×. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Knowledge+%26+Data+Engineering%22">IEEE Transactions on Knowledge & Data Engineering</searchLink>. Sep2021, Vol. 33 Issue 9, p3187-3198. 12p.
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  Data: The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30–60 percent less space and speeding up query execution by a factor of 2 – 81×. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TKDE.2020.2966609
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        Text: English
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        Type: general
      – SubjectFull: Problem solving
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      – SubjectFull: Data structures
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      – SubjectFull: RDF (Document markup language)
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      – TitleFull: Compressed Indexes for Fast Search of Semantic Data.
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              Text: Sep2021
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              Y: 2021
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