Souleiman Hasan

Linking building data in the cloud: Integrating cross-domain building data using linked data

Within 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 cloud-based 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.

Approximate semantic matching of heterogeneous events

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 F1 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.

Approximate Semantic Matching of Events for the Internet of Things

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% F1Score 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.

Towards unified and native enrichment in event processing systems

Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.

Thingsonomy: Tackling Variety in Internet of Things Events

The Internet of Things (IoT) will connect billions of devices to the Internet and create a large-scale dynamic and open environment with high heterogeneity. To assure rapid adoption of IoT applications, application developers and users need to be abstracted from IoT infrastructure via scalable middleware. Event-processing systems have the potential to contribute in filling the gap between the IoT infrastructure and applications layers. Event processing follows a decoupled model of interaction in space, time, and synchronization. However, the dimension of semantic coupling still exists and poses a challenge to scalability in highly semantically heterogeneous and dynamic environments such as the IoT. Here, the authors describe an approach based on loosely coupled producers and consumers enabled with approximate semantic matching of events. They emphasize a practitioner perspective to IoT architectures for building software that can tackle heterogeneity of IoT events.

Thematic event processing

Event-based systems follow a decoupled mode of interaction between event producers and consumers in space, time, and synchronization to enable scalability within distributed systems. We recognize a fourth dimension of coupling due to the need for mutual agreements on terms that describe event types, attributes, and values. Semantic coupling is challenging in large-scale, open, and heterogeneous environments such as the Internet of Things (IoT). It requires event producers and consumers to agree on event semantics and can limit scalability due to the difficulties in establishing such agreements. In this paper we propose a new thematic event processing approach based on enhancing events and subscriptions with terms representing their themes to clarify their domains and meanings in addition to their pay-load. Experiments conducted using large heterogeneous sets of smart-city and energy management events suggest up to 85% of matching accuracy at a rate of 500 events/sec of throughput. This represents around 15% improvement in accuracy and 150% in throughput over non-thematic approaches. This suggests the viability of thematic event processing to scale to environments such as the IoT.

Using Formal Concept Analysis for Organizing and Discovering Sensor Capabilities

Smart environments rely on sensor data to provide necessary business intelligence in order to support decision-making. An efficient decision support model in such a context requires that sensor data are provided correctly and timely. Given the dynamicity of sensor environment, the diversity of their features and of user requirements, finding appropriate sensors having the required capabilities or replacing faulty ones constitutes a challenging task. Efficiently describing and organizing sensors in smart environments is essential to deliver a rapid adaptation to errors and availability of data. In this paper, we present an approach for organizing and indexing sensor services based on their capabilities. We introduce a feature-oriented capability model that puts forward the functional aspects of carried actions and model them as resource description framework (RDF) properties rather than focusing on the change in the state of the world. Using this model for describing sensor capabilities, we apply Formal Concept Analysis for organizing and indexing sensor services. We have experimented and evaluated our approach in the Digital Enterprise Research Institute, which has been retrofitted with various sensors to monitor temperature, motion, light and consumption of power within a building.

Curbing resource consumption using team-based feedback

This paper details a team-based feedback approach for reducing resource consumption. The approach uses paper printing within office environments as a case study. It communicates the print usage of each participants team rather than the participants individual print usage. Feedback is provided weekly via emails and contains normative information, along with eco-metrics and team-based comparative statistics. The approach was empirically evaluated to study the effectiveness of the feedback method. The experiment comprised of 16 people belonging to 4 teams with data on their print usage gathered over 58 weeks, using the first 30-35 weeks as a baseline. The study showed a significant reduction in individual printing with an average of 28%. The experiment confirms the underlying hypothesis that participants are persuaded to reduce their print usage in order to improve the overall printing behaviour of their teams. The research provides clear pathways for future research to qualitatively investigate our findings.

IoT-Enhanced User Experience for Smart Water and Energy Management

The two articles in this special section represent ongoing Internet of Things applications in the context of Europe trying to make solutions usable to people in daily times.

Organizing Capabilities Using Formal Concept Analysis

In this paper, we discuss the importance of the concept of capability for describing what an action does from a functional perspective. We introduce a conceptual model for representing capabilities as attribute features entities. Furthermore, we use Formal Concept Analysis to create concept lattices for indexing a repository of capabilities descriptions. We applied this approach on a set of sensor capabilities deployed within the Linked Energy Intelligence dataspace.