Efficient RDF Interchange (ERI) format for RDF data streams

RDF streams are sequences of timestamped RDF statements or graphs, which can be generated by several types of data sources (sensors, social networks, etc.). They may provide data at high volumes and rates, and be consumed by applications that require real-time responses. Hence it is important to publish and interchange them efficiently. In this paper, we exploit a key feature of RDF data streams, which is the regularity of their structure and data values, proposing a compressed, efficient RDF interchange (ERI) format, which can reduce the amount of data transmitted when processing RDF streams. Our experimental evaluation shows that our format produces state-of-the-art streaming compression, remaining efficient in performance

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

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

SRBench: A Streaming RDF/SPARQL Benchmark

We introduce {\it SRBench}, the first general-purpose \underline{bench}mark primarily designed for \underline{s}treaming \underline{R}DF/SPARQL engines, completely based on real-world datasets from the Linked Open Data cloud. 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 streaming data. To help researchers and users to compare streaming RDF/SPARQL (StrRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a StrRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The datasets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a consice set of queries that cover the major aspects of StrRS engines, ranging from simple pattern matching queries on a single streaming dataset to queries with complex reasoning tasks on multiple interlinked datasets. Since StrRS processing is a fresh topic and so the most systems in this area are still experimental, in this paper, we complement our benchmarking work with a functional evaluation on three currently leading StrRS engines, \sst, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art

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

Linked Data - A Paradigm Shift for Geographic Information Science

The Linked Data paradigm has made significant inroads into research and practice around spatial information and it is time to reflect on what this means for GIScience. Technically, Linked Data is just data in the simplest possible data model (that of triples), allowing for linking records or data sets anywhere across the web using controlled semantics. Conceptually, Linked Data offers radically new ways of thinking about, structuring, publishing, discovering, accessing, and integrating data. It is of particular novelty and value to the producers and users of geographic data, as these are commonly thought to require more complex data models. The paper explains the main innovations brought about by Linked Data and demonstrates them with examples. It concludes that many longstanding problems in GIScience have become approachable in novel ways, while new and more specific research challenges emerge

M.: A native and adaptive approach for unified processing of linked streams and linked data

Abstract. In this paper we address the problem of scalable, native and adaptive query processing over Linked Stream Data integrated with Linked Data. Linked Stream Data consists of data generated by stream sources, e.g., sensors, enriched with semantic descriptions, following the standards proposed for Linked Data. This enables the integration of stream data with Linked Data collections and facilitates a wide range of novel applications. Currently available systems use a “black box ” approach which delegates the processing to other engines such as stream/event processing engines and SPARQL query processors by translating to their provided languages. As the experimental results described in this paper show, the need for query translation and data transformation, as well as the lack of full control over the query execution, pose major drawbacks in terms of efficiency. To remedy these drawbacks, we present CQELS (Continuous Query Evaluation over Linked Streams), a native and adaptive query processor for unified query processing over Linked Stream Data and Linked Data. In contrast to the existing systems, CQELS uses a “white box ” approach and implements the required query operators natively to avoid the overhead and limitations of closed system regimes. CQELS provide