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.

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Open Data initiatives are increasingly considered as defining elements of emerging smart cities. However, few studies have attempted to provide a better understanding of the nature of this convergence and the impact on both domains.

Business models for open data have emerged in response to the economic opportunities presented by the increasing availability of open data. However, scholarly efforts providing elaborations, rigorous analysis and comparison of open data models are very limited. This could be partly attributed to the fact that most discussions on Open Data Business Models (ODBMs) are predominantly in the practice community. This shortcoming has resulted in a growing list of ODBMs which, on closer examination, are not clearly delineated and lack clear value orientation. This has made the understanding of value creation and exploitation mechanisms in existing open data businesses difficult and challenging to transfer. Following the Design Science Research (DSR) tradition, we developed a 6-Value (6-V) business model framework as a design artifact to facilitate the explication and detailed analysis of existing ODBMs in practice. Based on the results from the analysis, we identify business model patterns and emerging core value disciplines for open data businesses. Our results not only help streamline existing ODBMs and help in linking them to the overall business strategy, but could also guide governments in developing the required capabilities to support and sustain the business models.

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.