Query Rewriting in RDF Stream Processing

Querying and reasoning over RDF streams are two increasingly relevant areas in the broader scope of processing structured data on the Web. While RDF Stream Processing (RSP) has focused so far on extending SPARQL for continuous query and event processing, stream reasoning has concentrated on ontology evolution and incremental materialization. In this paper we propose a different approach for querying RDF streams over ontologies, based on the combination of query rewriting and stream processing. We show that it is possible to rewrite continuous queries over streams of RDF data, while maintaining efficiency for a wide range of scenarios. We provide a detailed description of our approach, as well as an implementation, StreamQR, which is based on the kyrie rewriter, and can be coupled with a native RSP engine, namely CQELS. Finally, we show empirical evidence of the performance of StreamQR in a series of experiments based on the SRBench query set

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

Towards a Rule Interchange Language for the Web

This articles discusses rule languages that are needed for a a full deployment of the SemanticWeb. First, it motivates the need for such languages. Then, it presents ten theses addressing (1) the rule and/or logic languages needed on the Web, (2) data and data processing, (3) semantics, and (4) engineering and rendering issues. Finally, it discusses two options that might be chosen in designing a Rule Interchange Format for the Web

Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics

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Streaming the Web: Reasoning over dynamic data.

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved

Addressing the Challenges of Semantic Citizen-Sensing

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Towards Ideal Semantics for Analyzing Stream Reasoning

The rise of smart applications has drawn interest to logical reasoning over data streams. Recently, different query languages and stream processing/reasoning engines were proposed in different communities. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches are given only informally. Towards clear specifications and means for analytic study, a formal framework is needed to define their semantics in precise terms. To this end, we present a first step towards an ideal semantics that allows for exact descriptions and comparisons of stream reasoning systems.Comment: International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), co-located with the 21st European Conference on Artificial Intelligence (ECAI 2014). Proceedings of the International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), pages 17-22, technical report, ISSN 1430-3701, Leipzig University, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-150562 2014,

DIVIDE : adaptive context-aware query derivation for IoT data streams

In the Internet of Things, it is a challenging task to inte-grate & analyze high velocity sensor data with domain knowledge &context information in real-time. Semantic IoT platforms typically con-sist of stream processing components that use Semantic Web technologiesto run a set of fixed queries processing the IoT data streams. Configur-ing these queries is still a manual task. To deal with changes in contextinformation, which happen regularly in IoT domains, queries typicallyrequire reasoning on all sensor data in real-time to derive relevant sen-sors & events. This can be an issue in real-time, as expressive reasoningis required to deal with the complexity of many IoT domains. To solvethese issues, this paper presents DIVIDE. DIVIDE automatically derivesqueries for stream processing components in an adaptive, context-awareway. When the context changes, it derives through reasoning which sen-sors & observations to filter, given the context & a use case goal, withoutrequiring any more reasoning in real-time. This paper presents the detailsof DIVIDE, and performs evaluations on a healthcare example showinghow it can reduce real-time processing times, scale better when there aremore sensors & observations, and can run efficiently on low-end devices