574 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL
Abstract. Social semantic data are becoming a reality, but apparently their streaming nature has been ignored so far. Streams, being unbounded sequences of time-varying data elements, should not be treated as persistent data to be stored “forever ” and queried on demand, but rather as transient data to be consumed on the fly by queries which are registered once and for all and keep analyzing such streams, producing answers triggered by the streaming data and not by explicit invocation. In this paper, we propose an approach to continuous queries and realtime analysis of social semantic data with C-SPARQL, an extension of SPARQL for querying RDF streams
Expressive Stream Reasoning with Laser
An increasing number of use cases require a timely extraction of non-trivial
knowledge from semantically annotated data streams, especially on the Web and
for the Internet of Things (IoT). Often, this extraction requires expressive
reasoning, which is challenging to compute on large streams. We propose Laser,
a new reasoner that supports a pragmatic, non-trivial fragment of the logic
LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser
implements a novel evaluation procedure which annotates formulae to avoid the
re-computation of duplicates at multiple time points. This procedure, combined
with a judicious implementation of the LARS operators, is responsible for
significantly better runtimes than the ones of other state-of-the-art systems
like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP
solver Clingo. This enables the application of expressive logic-based reasoning
to large streams and opens the door to a wider range of stream reasoning use
cases.Comment: 19 pages, 5 figures. Extended version of accepted paper at ISWC 201
Enabling Ontology-based data access to streaming sources
The availability of streaming data sources is progressively increasing thanks to the development of ubiquitous data capturing tech- nologies such as sensor networks. The heterogeneity of these sources in- troduces the requirement of providing data access in a uni
ed and co- herent manner, whilst allowing the user to express their needs at an ontological level. In this paper we describe an ontology-based streaming data access service. Sources link their data content to ontologies through s2o mappings. Users can query the ontology using sparqlStream, an ex- tension of sparql for streaming data. A preliminary implementation of the approach is also presented. With this proposal we expect to set the basis for future e
orts in ontology-based streaming data integration
Benchmarking RDF Storage Engines
In this deliverable, we present version V1.0 of SRBench, the first benchmark for Streaming RDF engines, designed in the context of Task 1.4 of PlanetData, completely based on real-world datasets. With the increasing problem of too much streaming data but not enough knowledge, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for the publishing, sharing, analysing and understanding of such data. Various approaches are emerging. To help researchers and users to compare streaming RDF engines in a standardised application scenario, we propose SRBench, with which one can assess the abilities of a streaming RDF engine to cope with a broad range of use cases typically encountered in real-world scenarios. We offer a set of queries that cover the major aspects of streaming RDF engines, ranging from simple pattern matching queries to queries with complex reasoning tasks. To give a first baseline and illustrate the state of the art, we show results obtained from implementing SRBench using the SPARQLStream query-processing engine developed by UPM
Distributed stream reasoning
Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne
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