Binary RDF Representation for Publication and Exchange (HDT)

Fernández, Javier D.; Martínez‐Prieto, Miguel A.; Gutiérrez, Claudio; Polleres, Axel; Arias, Mario Lobo

doi:10.2139/ssrn.3198999

Cited by 52 publications

(93 citation statements)

References 27 publications

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“…HDT [7] is a compressed serialization format for single RDF graphs, which also allows for triple pattern retrieval over the compressed data. HDT encodes an RDF graph G into three components: the Header holds metadata (provenance, signatures, etc.)…”

Section: Hdtmentioning

confidence: 99%

“…Logically speaking, T organizes all triples into a forest of trees, one per different subject, as shown in Figure 2 (b): subjects are the roots of the trees, where the middle level comprises the ordered list of predicates associated with each subject, and the leaves list the objects related to each (subject, predicate) pair. This underlying representation is practically encoded with the so-called BitmapTriples approach [7], shown in Figure 2 (c). It comprises two sequences: Sp and So, concatenating all predicate IDs in the middle level and all object IDs in the leaves, respectively; and two bitsequences: Bp and Bo, which are aligned with Sp and So respectively, using a 1-bit to mark the end of each list.…”

Section: Hdtmentioning

confidence: 99%

“…HDT [7] tackles this issue by proposing a compact, self-indexed serialization of RDF. That is, HDT keeps big datasets compressed for RDF preservation and sharing and -at the same time-provides basic query functionality without prior decompression.…”

Section: Introductionmentioning

confidence: 99%

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HDTQ: Managing RDF Datasets in Compressed Space

Fernández

Martínez‐Prieto

Polleres

et al. 2018

Lecture Notes in Computer Science

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Abstract. HDT (Header-Dictionary-Triples) is a compressed representation of RDF data that supports retrieval features without prior decompression. Yet, RDF datasets often contain additional graph information, such as the origin, version or validity time of a triple. Traditional HDT is not capable of handling this additional parameter(s). This work introduces HDTQ (HDT Quads), an extension of HDT that is able to represent quadruples (or quads) while still being highly compact and queryable. Two HDTQ-based approaches are introduced: Annotated Triples and Annotated Graphs, and their performance is compared to the leading opensource RDF stores on the market. Results show that HDTQ achieves the best compression rates and is a competitive alternative to well-established systems.

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Section: Hdtmentioning

confidence: 99%

Section: Hdtmentioning

confidence: 99%

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HDTQ: Managing RDF Datasets in Compressed Space

Fernández

Martínez‐Prieto

Polleres

et al. 2018

Lecture Notes in Computer Science

Self Cite

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show abstract

“…If so, it is sent to the client; if not, the request is forwarded to the application server. The application server then parses the request, and retrieves the dbpedia data from an hdt file [5] that is loaded into memory. It is then serialized in a format according to the Accept header, sent to the client, and stored in the cache.…”

Section: Server Specificationsmentioning

confidence: 99%

dbpedia’s Triple Pattern Fragments: Usage Patterns and Insights

Verborgh

2015

The Semantic Web: ESWC 2015 Satellite Events

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Abstract. Queryable Linked Data is published through several interfaces, including sparql endpoints and Linked Data documents. In October 2014, the dbpedia Association announced an official Triple Pattern Fragments interface to its popular dbpedia dataset. This interface proposes to improve the availability of live queryable data by dividing query execution between clients and servers. In this paper, we present a usage analysis between November 2014 and July 2015. In 9 months time, the interface had an average availability of 99.99%, handling 16,776,170 requests, 43.0% of which were served from cache. These numbers provide promising evidence that low-cost Triple Pattern Fragments interfaces provide a viable strategy for live applications on top of public, queryable datasets.

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“…4 In this paper, we focus on the efficient transmission of RDF streams, a necessary step to ensure higher throughput for RDF Stream processors. Previous work on RDF compression [9,14] shows important size reductions of large RDF datasets, hence enabling an efficient RDF exchange. However, these solutions consider static RDF datasets, and need to read the whole dataset to take advantage of data regularities.…”

Section: Introductionmentioning

confidence: 99%

Efficient RDF Interchange (ERI) Format for RDF Data Streams

Fernández¹,

Llaves²,

Corcho³

2014

Lecture Notes in Computer Science

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Abstract. RDF streams are sequences of timestamped RDF statements or graphs, which can be generated by several types of data sources (sensors, social networks, etc.). They may provide data at high volumes and rates, and be consumed by applications that require real-time responses. Hence it is important to publish and interchange them efficiently. In this paper, we exploit a key feature of RDF data streams, which is the regularity of their structure and data values, proposing a compressed, efficient RDF interchange (ERI) format, which can reduce the amount of data transmitted when processing RDF streams. Our experimental evaluation shows that our format produces state-of-the-art streaming compression, remaining efficient in performance.

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Binary RDF Representation for Publication and Exchange (HDT)

Cited by 52 publications

References 27 publications

HDTQ: Managing RDF Datasets in Compressed Space

HDTQ: Managing RDF Datasets in Compressed Space

dbpedia’s Triple Pattern Fragments: Usage Patterns and Insights

Efficient RDF Interchange (ERI) Format for RDF Data Streams

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