Semantically-enriched and semi-Autonomous collaboration framework for the Web of Things. Design, implementation and evaluation of a multi-party collaboration framework with semantic annotation and representation of sensors in the Web of Things and a case study on disaster management
KeywordSemantic Web; Web of Things; Multi-party Collaboration Framework; Semantic annotation; Semantic Sensor Network ontology (SSN); Cloud computing; Service-oriented Architecture (SoA); Resource-based data model; Resource-oriented access control; Disaster management
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentFaculty of Engineering & Informatics
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AbstractThis thesis proposes a collaboration framework for the Web of Things based on the concepts of Service-oriented Architecture and integrated with semantic web technologies to offer new possibilities in terms of efficient asset management during operations requiring multi-actor collaboration. The motivation for the project comes from the rise in disasters where effective cross-organisation collaboration can increase the efficiency of critical information dissemination. Organisational boundaries of participants as well as their IT capability and trust issues hinders the deployment of a multi-party collaboration framework, thereby preventing timely dissemination of critical data. In order to tackle some of these issues, this thesis proposes a new collaboration framework consisting of a resource-based data model, resource-oriented access control mechanism and semantic technologies utilising the Semantic Sensor Network Ontology that can be used simultaneously by multiple actors without impacting each other’s networks and thus increase the efficiency of disaster management and relief operations. The generic design of the framework enables future extensions, thus enabling its exploitation across many application domains. The performance of the framework is evaluated in two areas: the capability of the access control mechanism to scale with increasing number of devices, and the capability of the semantic annotation process to increase in efficiency as more information is provided. The results demonstrate that the proposed framework is fit for purpose.
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When Pain Memories Are Lost: A Pilot Study of Semantic Knowledge of Pain in DementiaOosterman, J.M.; Hendriks, H.; Scott, S.; Lord, Kathryn; White, N.; Sampson, E.L. (2014)Objective It has been documented that pain in people with dementia is often under-reported and poorly detected. The reasons for this are not clearly defined. This project aimed to explore semantic concepts of pain in people with dementia and whether this is associated with clinical pain report. Design Cohort study with nested cross-sectional analysis. Setting Acute general hospital medical wards for older people. Subjects People with dementia (N = 26) and control participants (N = 13). Methods Two subtests of semantic memory for pain: 1) Identifying painful situations from a standardized range of pictures; 2) Describing the concept of pain. Participants also indicated whether they were in pain or not, were observed for pain (PAINAD scale) and completed the Wong–Baker FACES scale to indicate pain severity. Results Compared with the control group, people with dementia were less able to identify painful situations and used fewer categories to define their concept of pain. In turn, the performance on these two measures was related to the reported presence and, albeit less strongly, to the reported severity of pain, indicating that a reduction in semantic memory for pain is associated with a decline in reported pain. Conclusions This study is the first to show that semantic memory for pain is diminished in dementia patients. When using clinical pain tools, clinicians should consider these effects which may bias clinical pain ratings when they evaluate and manage pain in these patients. This might improve the recognition and management of pain in people with dementia.
Musical training and semantic integration in sentence processing: Tales of the unexpectedFeatherstone, C.R.; Morrison, Catriona M.; Waterman, M.G.; MacGregor, L.J. (2014)Building on models of transfer effects between musical training and language processing and on evidence of similarities in the way the brain responds to unexpected elements in music and language, we investigated whether effects of musical training could be observed at the level of sentence processing. Using sentences that tax the semantic processes involved in natural comprehension and avoid outright anomalies, we showed a striking difference between musicians and non-musicians: contrary to non-musicians, musicians showed no N400 response to novel metaphorical words which were more difficult to integrate semantically into their context than literal controls. This difference between musicians and non-musicians in semantic processing in sentences shows an effect of musicianship at the highest level of music–language transfer effects demonstrated so far in the literature. As well as adding to the growing body of evidence surrounding the relationship between musical training and language processing, this work provides support for theories which suggest shared resources, computations, and neural areas underpinning the high-level processing of music and language.
A note on exploration of IoT generated big data using semanticsRanjan, R.; Thakker, Dhaval; Haller, A.; Buyya, R. (2017-11)Welcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies.