Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people's everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper

With the growing popularity of Internet of Things (IoT) and IoT-enabled smart city applications, RDF stream processing (RSP) is gaining increasing attention in the Semantic Web community. As a result, several RSP engines have emerged, which are capable of processing semantically annotated data streams on the fly. Performance, correctness and technical soundness of few existing RSP engines have been evaluated in controlled settings using existing benchmarks like LSBench and SRBench. However, these benchmarks focus merely on features of the RSP query languages and engines, and do not consider dynamic application requirements and data-dependent properties such as changes in streaming rate during query execution or changes in application requirements over a period of time. This hinders wide adoption of RSP engines for real-time applications where data properties and application requirements play a key role and need to be characterised in their dynamic setting, such as in the smart city domain.In this paper, we present CityBench, a comprehensive benchmarking suite to evaluate RSP engines within smart city applications and with smart city data. CityBench includes real-time IoT data streams generated from various sensors deployed within the city of Aarhus, Denmark. We provide a configurable testing infrastructure and a set of continuous queries covering a variety of data-and application-dependent characteristics and performance metrics, to be executed over RSP engines using CityBench datasets. We evaluate two state of the art RSP engines using our testbed and discuss our experimental results. This work can be used as a baseline to identify capabilities and limitations of existing RSP engines for smart city applications.

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedia's tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city environments, while offering citizens and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several issues such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic integration of data streams and complex event processing on top of real-time data aggregation and quality analysis in a Semantic Web environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open Data Aarhus by the City of Aarhus. We examine the framework utilising Symbolic Aggregate Approximation to reduce the size of data streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimisation of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.

When it comes to publishing data on the web, the level of access control required (if any) is highly dependent on the type of content exposed. Up until now RDF data publishers have focused on exposing and linking public data. With the advent of SPARQL 1.1, the linked data infrastructure can be used, not only as a means of publishing open data but also, as a general mechanism for managing distributed graph data. However, such a decentralised architecture brings with it a number of additional challenges with respect to both data security and integrity. In this paper, we propose a general authorisation framework that can be used to deliver dynamic query results based on user credentials and to cater for the secure manipulation of linked data. Specifically we describe how graph patterns, propagation rules, conflict resolution policies and integrity constraints can together be used to specify and enforce consistent access control policies.

With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users' requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets.

In recent years we have seen significant advances in the technology used to both publish and consume structured data using the existing web infrastructure, commonly referred to as the Linked Data Web. However, in order to support the next generation of e-business applications on top of Linked Data suitable forms of access control need to be put in place. This paper provides an overview of the various access control models, standards and policy languages, and the different access control enforcement strategies for the Resource Description Framework (the data model underpinning the Linked Data Web). A set of access control requirements that can be used to categorise existing access control strategies is proposed and a number of challenges that still need to be overcome are identified.

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