539 research outputs found
RDF-TR: Exploiting structural redundancies to boost RDF compression
The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr (RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate
RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques
Scalable RDF Data Compression using X10
The Semantic Web comprises enormous volumes of semi-structured data elements.
For interoperability, these elements are represented by long strings. Such
representations are not efficient for the purposes of Semantic Web applications
that perform computations over large volumes of information. A typical method
for alleviating the impact of this problem is through the use of compression
methods that produce more compact representations of the data. The use of
dictionary encoding for this purpose is particularly prevalent in Semantic Web
database systems. However, centralized implementations present performance
bottlenecks, giving rise to the need for scalable, efficient distributed
encoding schemes. In this paper, we describe an encoding implementation based
on the asynchronous partitioned global address space (APGAS) parallel
programming model. We evaluate performance on a cluster of up to 384 cores and
datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art
MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent
scalability. These results illustrate the strong potential of the APGAS model
for efficient implementation of dictionary encoding and contributes to the
engineering of larger scale Semantic Web applications
Compressed k2-Triples for Full-In-Memory RDF Engines
Current "data deluge" has flooded the Web of Data with very large RDF
datasets. They are hosted and queried through SPARQL endpoints which act as
nodes of a semantic net built on the principles of the Linked Data project.
Although this is a realistic philosophy for global data publishing, its query
performance is diminished when the RDF engines (behind the endpoints) manage
these huge datasets. Their indexes cannot be fully loaded in main memory, hence
these systems need to perform slow disk accesses to solve SPARQL queries. This
paper addresses this problem by a compact indexed RDF structure (called
k2-triples) applying compact k2-tree structures to the well-known
vertical-partitioning technique. It obtains an ultra-compressed representation
of large RDF graphs and allows SPARQL queries to be full-in-memory performed
without decompression. We show that k2-triples clearly outperforms
state-of-the-art compressibility and traditional vertical-partitioning query
resolution, remaining very competitive with multi-index solutions.Comment: In Proc. of AMCIS'201
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Efficient dictionary compression for processing RDF big data using Google BigQuery
The Resource Description Framework (RDF) data model, is used on the Web to express billions of structured statements in a wide range of topics, including government, publications, life sciences, etc. Consequently, processing and storing this data requires the provision of high specification systems, both in terms of storage and computational capabilities. On the other hand, cloud-based big data services such as Google BigQuery can be used to store and query this data without any upfront investment. Google BigQuery pricing is based on the size of the data being stored or queried, but given that RDF statements contain long Uniform Resource Identifiers (URIs), the cost of query and storage of RDF big data can increase rapidly. In this paper we present and evaluate a novel and efficient dictionary compression algorithm which is faster, generates small dictionaries that can fit in memory and results in better compression rate when compared with other large scale RDF dictionary compression. Consequently, our algorithm also reduces the BigQuery storage and query cos
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