2,043 research outputs found
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
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
An Empirical Study of Real-World SPARQL Queries
Understanding how users tailor their SPARQL queries is crucial when designing
query evaluation engines or fine-tuning RDF stores with performance in mind. In
this paper we analyze 3 million real-world SPARQL queries extracted from logs
of the DBPedia and SWDF public endpoints. We aim at finding which are the most
used language elements both from syntactical and structural perspectives,
paying special attention to triple patterns and joins, since they are indeed
some of the most expensive SPARQL operations at evaluation phase. We have
determined that most of the queries are simple and include few triple patterns
and joins, being Subject-Subject, Subject-Object and Object-Object the most
common join types. The graph patterns are usually star-shaped and despite
triple pattern chains exist, they are generally short.Comment: 1st International Workshop on Usage Analysis and the Web of Data
(USEWOD2011) in the 20th International World Wide Web Conference (WWW2011),
Hyderabad, India, March 28th, 201
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