Enabling Query Technologies for the Semantic Sensor Web

Corcho, Óscar; Calbimonte, Jean-Paul; Jeung, Hoyoung; Aberer, Karl

doi:10.4018/jswis.2012010103

Cited by 119 publications

(104 citation statements)

References 23 publications

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“…These efforts resulted in a series of implementation and approaches in this area, proposing their own set of stream models and extensions to SPARQL [5,11,7,3,9]. These and other RSP engines have focused on the execution of SPARQL streaming queries and the possible optimization and techniques that can be applied in that context.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Reactive Processing of RDF Streams of Events

Calbimonte

Aberer

2015

The Semantic Web: ESWC 2015 Satellite Events

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Abstract. Events on the Web are increasingly being produced in the form of data streams, and are present in many different scenarios and applications such as health monitoring, environmental sensing or social networks. The heterogeneity of event streams has raised the challenges of integrating, interpreting and processing them coherently. Semantic technologies have shown to provide both a formal and practical framework to address some of these challenges, producing standards for representation and querying, such as RDF and SPARQL. However, these standards are not suitable for dealing with streams for events, as they do not include the concpets of streaming and continuous processing. The idea of RDF stream processing (RSP) has emerged in recent years to fill this gap, and the research community has produced prototype engines that cover aspects including complex event processing and stream reasoning to varying degrees. However, these existing prototypes often overlook key principles of reactive systems, regarding the event-driven processing, responsiveness, resiliency and scalability. In this paper we present a reactive model for implementing RSP systems, based on the Actor model, which relies on asynchronous message passing of events. Furthermore, we study the responsiveness property of RSP systems, in particular for the delivery of streaming results.

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Section: Related Work and Discussionmentioning

confidence: 99%

Reactive Processing of RDF Streams of Events

Calbimonte

Aberer

2015

The Semantic Web: ESWC 2015 Satellite Events

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“…Our next plans focus on integrating ERI within the next version of morphstreams [3] , with the purpose of scaling to higher input data rates, minimizing data exchange among processing nodes and serving a small set of retrieving features on the compressed data. This will come together with other new features, including an adaptive query processor aware of the compression dimension during the application of optimization strategies.…”

Section: Discussionmentioning

confidence: 99%

“…We define three different categories of datasets: streaming (10), statistics (3) and general (3). Obviously, Streaming datasets are our main application focus; the first six datasets in Table 1 have been already used in the evaluation of RDSZ [10] and correspond to RDF messages in the public streamline of a microblogging site (Identica), Wikipedia edition monitoring (Wikipedia), information from weather stations in Spain (AEMET-1 and AEMET-2), credit card transactions in petrol stations (Petrol) and a random mix of these datasets (Mix).…”

Section: Datasetsmentioning

confidence: 99%

Efficient RDF Interchange (ERI) Format for RDF Data Streams

Fernández¹,

Llaves²,

Corcho³

2014

Lecture Notes in Computer Science

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Abstract. 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.

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“…Context information such as hit count, battery, and response time are used to select the services. An interesting proposal is by Calbimonte et al [21], who have proposed an ontology-based approach for providing data access and query capabilities to streaming data sources. This work allows the users to express their needs at a conceptual level, independent of implementation.…”

Section: Background and Related Workmentioning

confidence: 99%

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

173

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Abstract-The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Index Terms-Internet of Things, context awareness, sensors, search and selection, indexing and ranking, semantic querying, quantitative reasoning, multi-dimensional data fusion.

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Enabling Query Technologies for the Semantic Sensor Web

Cited by 119 publications

References 23 publications

Reactive Processing of RDF Streams of Events

Reactive Processing of RDF Streams of Events

Efficient RDF Interchange (ERI) Format for RDF Data Streams

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

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