a b s t r a c tWithin the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information.As part of moving building data to the cloud there is a critical need to reflect on the design of cloudbased data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems.Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.

Event processing follows a decoupled model of interaction in space, time, and synchronization. However, another dimension of semantic coupling also exists and poses a challenge to the scalability of event processing systems in highly semantically heterogeneous and dynamic environments such as the Internet of Things (IoT). Current state-of-the-art approaches of content-based and concept-based event systems require a significant agreement between event producers and consumers on event schema or an external conceptual model of event semantics. Thus, they do not address the semantic coupling issue. This article proposes an approach where participants only agree on a distributional statistical model of semantics represented in a corpus of text to derive semantic similarity and relatedness. It also proposes an approximate model for relaxing the semantic coupling dimension via an approximation-enabled rule language and an approximate event matcher. The model is formalized as an ensemble of semantic and top-k matchers along with a probability model for uncertainty management. The model has been empirically validated on large sets of events and subscriptions synthesized from real-world smart city and energy management systems. Experiments show that the proposed model achieves more than 95% F 1 Score of effectiveness and thousands of events/sec of throughput for medium degrees of approximation while not requiring users to have complete prior knowledge of event semantics. In semantically loosely-coupled environments, one approximate subscription can compensate for hundreds of exact subscriptions to cover all possibilities in environments which require complete prior knowledge of event semantics. Results indicate that approximate semantic event processing could play a promising role in the IoT middleware layer.

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Event-based systems have loose coupling within space, time and synchronization, providing a scalable infrastructure for information exchange and distributed workflows. However, event-based systems are tightly coupled, via event subscriptions and patterns, to the semantics of the underlying event schema and values. The high degree of semantic heterogeneity of events in large and open deployments such as smart cities and the sensor web makes it difficult to develop and maintain event-based systems. In order to address semantic coupling within event-based systems, we propose vocabulary free subscriptions together with the use of approximate semantic matching of events. This paper examines the requirement of event semantic decoupling and discusses approximate semantic event matching and the consequences it implies for event processing systems. We introduce a semantic event matcher and evaluate the suitability of an approximate hybrid matcher based on both thesauri-based and distributional semantics-based similarity and relatedness measures. The matcher is evaluated over a structured representation of Wikipedia and Freebase events. Initial evaluations show that the approach matches events with a maximal combined precision-recall F 1 score of 75.89% on average in all experiments with a subscription set of 7 subscriptions. The evaluation shows how a hybrid approach to semantic event matching outperforms a single similarity measure approach.

This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.