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

Publication of RDF streams with Ztreamy

Proceedings of ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25–29, 2014There is currently an interest in the Semantic Web community for the development of tools and techniques to process RDF streams. Implementing an effective RDF stream processing system requires to address several aspects including stream generation, querying, reasoning, etc. In this work we focus on one of them: the distribution of RDF streams through the Web. In order to address this issue, we have developed Ztreamy, a scalable middleware which allows to publish and consume RDF streams through HTTP. The goal of this demo is to show the functionality of Ztreamy in two different scenarios with actual, heterogeneous streaming data.This work has been partially funded by the Spanish Government through the project HERMES-SMARTDRIVER (TIN2013-46801-C4-2-R)

Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL

On correctness in RDF stream processor benchmarking

Two complementary benchmarks have been proposed so far for the evaluation and continuous improvement of RDF stream processors: SRBench and LSBench. They put a special focus on different features of the evaluated systems, including coverage of the streaming extensions of SPARQL supported by each processor, query processing throughput, and an early analysis of query evaluation correctness, based on comparing the results obtained by different processors for a set of queries. However, none of them has analysed the operational semantics of these processors in order to assess the correctness of query evaluation results. In this paper, we propose a characterization of the operational semantics of RDF stream processors, adapting well-known models used in the stream processing engine community: CQL and SECRET. Through this formalization, we address correctness in RDF stream processor benchmarks, allowing to determine the multiple answers that systems should provide. Finally, we present CSRBench, an extension of SRBench to address query result correctness verification using an automatic method

RDSZ: an approach for lossless RDF stream compression

In many applications (like social or sensor networks) the in- formation generated can be represented as a continuous stream of RDF items, where each item describes an application event (social network post, sensor measurement, etc). In this paper we focus on compressing RDF streams. In particular, we propose an approach for lossless RDF stream compression, named RDSZ (RDF Differential Stream compressor based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding mechanism with the general purpose stream compressor Zlib. Empirical evaluation using several RDF stream datasets shows that this combi- nation produces gains in compression ratios with respect to using Zlib alone

Moving real-time linked data query evaluation to the client

Traditional RDF stream processing engines work completely server-side, which contributes to a high server cost. For allowing a large number of concurrent clients to do continuous querying, we extend the low-cost Triple Pattern Fragments (TPF) interface with support for timesensitive queries. In this poster, we give the overview of a client-side rdf stream processing engine on top of tpf. Our experiments show that our solution significantly lowers the server load while increasing the load on the clients. Preliminary results indicate that our solution moves the complexity of continuously evaluating real-time queries from the server to the client, which makes real-time querying much more scalable for a large amount of concurrent clients when compared to the alternatives

Presence of e-EDCs in surface water and effluents of pollution sources in Sai Gon and Dong Nai river basin

© 2016 This study aimed to assess the presence of estrogenic endocrine disrupting compounds (e-EDCs) including estriol, bisphenol A (BPA), atrazine (ATZ), octylphenol, octylphenol diethoxylate, octylphenol triethoxylate, nonylphenol, Nonylphenol triethoxylate (NPE3), nonylphenol diethoxylate (NPE2) and 17β-estradiol in: (i) Sai Gon and Dong Nai river waters which have been major raw water sources for drinking water supply for Ho Chi Minh City (HCMC) and neighbouring provinces, and (ii) water pollution sources located in their catchment basin. NPE3 and NPE2 were detected in most of the surface water samples. Concentrations of NPE3 were in a range of less than 5.9–235 ng L−1, whereas BPA was detected at significantly high concentrations in the dry season in canals in HCMC. In the upstream of Sai Gon and Dong Nai Rivers, ATZ concentrations were observed at water intake of water treatment plants served for HCMC water supply system. Similarly, high potential risk of NPE2 and NPE3 contamination at Phu Cuong Bridge near Hoa Phu water intake was identified. The significant correlation between NPE2, dissolved organic carbon and total nitrogen was found. Estrogenic equivalent or estrogenic activity of Sai Gon and Dong Nai Rivers was lower than those of the previous studies. Compared with other studies, e-EDCs of pollution in Sai Gon river basin were relatively low

Approximate Semantic Matching Over Linked Data Streams

In the Internet of Things (IoT),data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users’ needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users’ data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls