392 research outputs found
Distributed RDF query processing and reasoning for big data / linked data
Title from PDF of title page, viewed on August 27, 2014Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 61-65)Thesis (M. S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014The Linked Data Movement is aimed at converting unstructured and semi-structured
data on the documents to semantically connected documents called the "web of data." This is
based on Resource Description Framework (RDF) that represents the semantic data and a
collection of such statements shapes an RDF graph. SPARQL is a query language designed
specifically to query RDF data. Linked Data faces the same challenge that Big Data does. We
now lead the way to a new wave of a new paradigm, Big Data and Linked Data that identify
massive amounts of data in a connected form. Indeed, utilizing Linked Data and Big Data
continue to be in high demand. Therefore, we need a scalable and accessible query system
for the reusability and availability of existing web data. However, existing SPAQL query
systems are not sufficiently scalable for Big Data and Linked Data.
In this thesis, we address an issue of how to improve the scalability and performance
of query processing with Big Data / Linked Data. Our aim is to evaluate and assess presently
available SPARQL query engines and develop an effective model to query RDF data that
should be scalable with reasoning capabilities. We designed an efficient and distributed
SPARQL engine using MapReduce (parallel and distributed processing for large data sets on
a cluster) and the Apache Cassandra database (scalable and highly available peer to peer distributed database system). We evaluated an existing in-memory based ARQ engine
provided by Jena framework and found that it cannot handle large datasets, as it only works
based on the in-memory feature of the system. It was shown that the proposed model had
powerful reasoning capabilities and dealt efficiently with big datasetsAbstract -- Illistrations -- Tables -- Introduction -- Background and related work -- Graph-store based SPARQL model -- Graph-store based SPARQL model implementation -- Results and evaluation -- Conclusion and future work -- Reference
MapReduce-based Solutions for Scalable SPARQL Querying
The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and rconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results
Distributed Semantic Web data management in HBase and MySQL cluster
Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management
Distributed Semantic Web Data Management in HBase and MySQL Cluster
Various computing and data resources on the Web are being enhanced with
machine-interpretable semantic descriptions to facilitate better search,
discovery and integration. This interconnected metadata constitutes the
Semantic Web, whose volume can potentially grow the scale of the Web. Efficient
management of Semantic Web data, expressed using the W3C's Resource Description
Framework (RDF), is crucial for supporting new data-intensive,
semantics-enabled applications. In this work, we study and compare two
approaches to distributed RDF data management based on emerging cloud computing
technologies and traditional relational database clustering technologies. In
particular, we design distributed RDF data storage and querying schemes for
HBase and MySQL Cluster and conduct an empirical comparison of these approaches
on a cluster of commodity machines using datasets and queries from the Third
Provenance Challenge and Lehigh University Benchmark. Our study reveals
interesting patterns in query evaluation, shows that our algorithms are
promising, and suggests that cloud computing has a great potential for scalable
Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing
(CLOUD'11
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
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