B
Bryan Scotney
Researcher at Ulster University
Publications - 312
Citations - 3588
Bryan Scotney is an academic researcher from Ulster University. The author has contributed to research in topics: Image processing & Feature extraction. The author has an hindex of 25, co-authored 297 publications receiving 3138 citations.
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Journal ArticleDOI
Smart City Architecture and its Applications Based on IoT
TL;DR: A Multi-Level Smart City architecture is proposed based on semantic web technologies and Dempster-Shafer uncertainty theory and described and explained in terms of its functionality and some real-time context-aware scenarios.
Journal ArticleDOI
Evidential fusion of sensor data for activity recognition in smart homes
TL;DR: The framework within which uncertainty can be managed is introduced and the effects that the number of sensors in conjunction with the reliability level of each sensor can have on the overall decision making process are demonstrated.
Journal ArticleDOI
Aggregation of imprecise and uncertain information in databases
TL;DR: This work considers the problem of aggregation using an imprecise probability data model that allows us to represent imprecision by partial probabilities and uncertainty using probability distributions to perform the operations necessary for knowledge discovery in databases.
Proceedings ArticleDOI
Markovian Workload Characterization for QoS Prediction in the Cloud
Sergio Pacheco-Sanchez,Giuliano Casale,Bryan Scotney,Sally McClean,Gerard Parr,Stephen Dawson +5 more
TL;DR: This paper investigates the Markovian Arrival Processes and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud, and defines a maximum likelihood method for fitting MAP parameters based on data commonly available in Apache log files, and a new technique to cope with batch arrivals.
Journal ArticleDOI
Cache performance models for quality of service compliance in storage clouds
Ernest Sithole,Aaron McConnell,Sally McClean,Gerard Parr,Bryan Scotney,Adrian Moore,Dave Bustard +6 more
TL;DR: Analytical models for characterising cache performance trends at storage cache nodes are presented and have potential for guiding efficient resource allocations during initial deployments of the storage cloud infrastructure and timely interventions during operation in order to achieve scalable and resilient service delivery.