The Internet of Things (IoT) is not only about interconnecting embedded devices to the Internet, but also about providing knowledge on such devices and what they sense from the physical world. One focus of IoT is put on extracting actionable knowledge and providing value-added services by means of reasoning techniques. Stream reasoning techniques offer a promising solution for processing dynamic, heterogeneous, and volume data for IoT. In this article, we identify the challenges for utilizing stream reasoners from the IoT point of view, review the landscape of stream reasoning techniques, and examine their capabilities to meet the challenges of IoT. Moreover, we present an experimental IoT system implementing stream reasoning and perform a gap analysis to evaluate stream reasoners. Finally, based on the analysis, we suggest several recommendations for future development of stream reasoners in order to overcome the identified gaps.

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In most government and business organizations alike, statistical data provides the foundation for strategic planning and for the management of operations. In this context, the use of increasingly abundant statistical data available on the web creates new opportunities for interesting applications and facilitates more informed decision-making. For the majority of end users, however, viable means to explore statistical data sets available on the web are still scarce. Gathering and relating statistical data from multiple sources is hence typically a tedious manual process that requires significant technical expertise. Data that is being published with associated semantics, using standards such as the W3C RDF Data Cube Vocabulary, lays the foundation to overcome such limitations. In this paper, we develop a semantic metadata repository that describes each statistical data set and develop mechanisms for the interconnection of data sets based on their metadata. Finally, we support users in exploring data sets through interactive mashups that facilitate data integration, comparisons, and visualization.

Today, public statistical data plays an increasingly important role both in public policy formation and as a facilitator for informed decision-making in the private sector. In line with the increasing adoption of open data policies, the amount of data published by governments and organizations on the web is growing rapidly. To increase the value of such data, the W3C recommends the RDF Data Cube Vocabulary to facilitate the publication of data in a more structured and interlinked manner. Although important first steps toward building a web of statistical Linked Datasets have been made, providing adequate facilities for end users to interactively explore and make use of the published data remains an unresolved challenge. This paper presents a widgetbased approach to deal with this issue. In particular, we introduce a mashup platform that allows users lacking advanced skills and knowledge of Semantic Web technologies to interactively analyze datasets through widget compositions and visualizations. Furthermore, we provide mechanisms for the interconnection of datasets to support sophisticated knowledge extraction.