525 research outputs found
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
Geographica: A Benchmark for Geospatial RDF Stores
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently
been defined and corresponding geospatial RDF stores have been implemented.
However, there is no widely used benchmark for evaluating geospatial RDF stores
which takes into account recent advances to the state of the art in this area.
In this paper, we develop a benchmark, called Geographica, which uses both
real-world and synthetic data to test the offered functionality and the
performance of some prominent geospatial RDF stores
Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers
While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools
A scalable analysis framework for large-scale RDF data
With the growth of the Semantic Web, the availability of RDF datasets from multiple domains
as Linked Data has taken the corpora of this web to a terabyte-scale, and challenges
modern knowledge storage and discovery techniques. Research and engineering on RDF
data management systems is a very active area with many standalone systems being introduced.
However, as the size of RDF data increases, such single-machine approaches meet
performance bottlenecks, in terms of both data loading and querying, due to the limited
parallelism inherent to symmetric multi-threaded systems and the limited available system
I/O and system memory. Although several approaches for distributed RDF data processing
have been proposed, along with clustered versions of more traditional approaches, their
techniques are limited by the trade-off they exploit between loading complexity and query
efficiency in the presence of big RDF data. This thesis then, introduces a scalable analysis
framework for processing large-scale RDF data, which focuses on various techniques to
reduce inter-machine communication, computation and load-imbalancing so as to achieve
fast data loading and querying on distributed infrastructures.
The first part of this thesis focuses on the study of RDF store implementation and parallel
hashing on big data processing. (1) A system-level investigation of RDF store implementation
has been conducted on the basis of a comparative analysis of runtime characteristics
of a representative set of RDF stores. The detailed time cost and system consumption is
measured for data loading and querying so as to provide insight into different triple store
implementation as well as an understanding of performance differences between different
platforms. (2) A high-level structured parallel hashing approach over distributed memory is
proposed and theoretically analyzed. The detailed performance of hashing implementations
using different lock-free strategies has been characterized through extensive experiments,
thereby allowing system developers to make a more informed choice for the implementation
of their high-performance analytical data processing systems.
The second part of this thesis proposes three main techniques for fast processing of large
RDF data within the proposed framework. (1) A very efficient parallel dictionary encoding
algorithm, to avoid unnecessary disk-space consumption and reduce computational complexity of query execution. The presented implementation has achieved notable speedups
compared to the state-of-art method and also has achieved excellent scalability. (2) Several
novel parallel join algorithms, to efficiently handle skew over large data during query processing.
The approaches have achieved good load balancing and have been demonstrated
to be faster than the state-of-art techniques in both theoretical and experimental comparisons.
(3) A two-tier dynamic indexing approach for processing SPARQL queries has been
devised which keeps loading times low and decreases or in some instances removes intermachine
data movement for subsequent queries that contain the same graph patterns. The
results demonstrate that this design can load data at least an order of magnitude faster than
a clustered store operating in RAM while remaining within an interactive range for query
processing and even outperforms current systems for various queries
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
Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web
If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches
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