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International audience; Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. However, the heterogeneity and the peculiarities of spatial data, such as the use of different coordinate reference systems, make it difficult for data users, Web applications, and services to discover, interpret and use the information in the large and distributed system that is the Web. To make spatial data more effectively available, this paper summarizes the work of the joint W3C/OGC Working Group on Spatial Data on the Web that identifies 14 best practices for publishing spatial data on the Web. The paper extends that work by presenting the identified challenges and rationale for selection of the recommended best practices, framed by the set of principles that guided the selection. It describes best practices that are employed to enable publishing, discovery and retrieving (querying) spatial data on the Web, and identifies some areas where a best practice has not yet emerged.

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.

International audience; The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.

Large RDF interconnected datasets, especially in the form of open as well as enterprise knowledge graphs, are constructed and consumed in several domains. Reasoning over such large knowledge graphs poses several performance challenges. In practice, although there has been some prior work on scalable approaches to RDF reasoning, the interest in this field started gathering momentum with the rising popularity of modern big data processing systems (e.g., Hadoop, Spark). In this chapter, we cover five main categories of distributed RDF reasoning systems: (1) Peer-to-Peer RDF reasoning systems, (2) NoSQL-based RDF reasoning systems, (3) Hadoop-based RDF reasoning systems, (4) Spark-based RDF reasoning systems, and (5) shared memory RDF reasoning systems.

With increasing sizes of RDF datasets, executing complex queries on a single node has turned to be impractical especially when the node’s main memory is dwarfed by the volume of the dataset. Therefore, there was a crucial need for distributed systems with a high degree of parallelism that can satisfy the performance demands of complex SPARQL queries. In this chapter, we give an overview of various techniques and systems for efficiently querying large RDF datasets in distributed environments.

We are witnessing a paradigm shift, where real-time, time-dependent data is becoming ubiquitous. As Linked Data facilitates the data integration process among heterogenous data sources, RDF Stream Data has the same goal with respect to data streams. It bridges the gap between stream and more static data sources. To support the processing on RDF stream data, there is a need on investigating how to extend RDF to model and represent stream data. Then, from the RDF-based data representation, the query model processing models need to be defined to build the stream processing engine that is tailored for streaming data. This chapter provides an overview on how such requirements are addressed in the current state-of-the-art of RDF Stream Data processing.

The wide adoption of the RDF data model has called for efficient and scalable RDF query processing schemes. As a response to this call, a number of centralized RDF query processing systems have been designed to tackle this challenge. In these systems, the storage and query processing of RDF datasets are managed on a single node. In this chapter, we give an overview of various techniques and systems for centrally querying RDF datasets.

The nature of Web data is changing. The popularity of news feeds and social media, the rise of the Web of Things, and the adoption of sensor technologies are examples of streaming data that reached the Web scale. The different nature of streaming data calls for specific solutions to problems like data integration and analytics. There is a need for streaming-specific Web resources: new vocabularies to describe, find and select streaming data sources, and systems that can cooperate dynamically to solve stream processing tasks. To foster interoperability between these streaming services on the Web, we propose the Vocabulary & Catalog of Linked Streams (VoCaLS). VoCaLS is a three-module ontology to (i) publish streaming data following Linked Data principles, (ii) describe streaming services and (iii) track the provenance of stream processing.

The term Provenance refers to the origin of information and is used to describe where and how the data was obtained. Provenance is versatile and could include various types of information, such as the source of the data, information on the processes that led to a certain result, date of creation or last modification, and authorship. Recording and managing the provenance of data is of paramount importance, as it allows supporting trust mechanisms, access control and privacy policies, digital rights management, quality management and assessment, in addition to reputability, reliability and accountability of data sources.

Standards and benchmarking have traditionally been used as the main tools to formally define and provably illustrate the level of the adequacy of systems to address the new challenges. In this chapter, we discuss benchmarks for RDF query engines and instance matching systems. In practice, benchmarks are used to inform users of the strengths and weaknesses of competing tools and approaches, but more importantly, they encourage the advancement of technology by providing both academia and industry with clear targets for performance and functionality.

International audience; The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.

The Internet of Things (IoT) with billions of connected devices has been generating an enormous amount of data every hour. Connecting every data item generated by IoT to the rest of the digital world to turn this data into meaningful actions will create new capabilities, richer experiences, and unprecedented economic opportunities for businesses, individuals, and countries. However, providing an integrated view for exploring and querying such data at real-time is extremely challenging due to its Big Data natures: big volume, fast real-time update and messy data sources. To address this challenge, we provide a unified integrated and live view for heterogeneous IoT data sources using Linked Data, called the Graph of Things (GoT). GoT is backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time. GoT makes approximately a half of million stream data sources queryable via a SPARQL endpoint and a continuous query channel that enable us to create a live explorer of GoT (http://graphofthings.org/) with just HTML and Javascript.

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics for the Internet of Things by using the OpenIoT1 middleware ; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project—OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. ( www.github.com/OpenIotOrg ). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.

Mobile devices are becoming a central data integration hub for personal information. Thus, an up-to-date, comprehensive and consolidated view of this information across heterogeneous personal information spaces is required. Linked Data offers various solutions for integrating personal information, but none of them comprehensively addresses the specific resource constraints of mobile devices. To address this issue, this paper presents a unified data integration framework for resource-constrained mobile devices. Our generic, extensible framework not only provides a unified view of personal data from different personal information data spaces but also can run on a user's mobile device without any external server. To save processing resources, we propose a data normalisation approach that can deal with ID-consolidation and ambiguity issues without complex generic reasoning. This data integration approach is based on a triple storage for Android devices with small memory footprint. We evaluate our framework with a set of experiments on different devices and show that it is able to support complex queries on large personal data sets of more than one million triples on typical mobile devices with very small memory footprint.