Computing and Information Systems - Research Publications
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ItemIOQP: A simple Impact-Ordered Query Processor written in Rust(RWTH, Aachen University, 2023-01-01)Impact-ordered index organizations are suited to score-at-a-time query evaluation strategies. A key advantage of score-at-a-time processing is that query latency can be tightly controlled, leading to lower tail latency and less latency variance overall. While score-at-a-time evaluation strategies have been explored in the literature, there is currently only one notable system that promotes impact-ordered indexing and efficient score-at-a-time query processing. In this paper, we propose an alternative implementation of score-at-a-time retrieval over impact-ordered indexes in the Rust programming language. We detail the efficiency-effectiveness characteristics of our implementation through a range of experiments on two test collections. Our results demonstrate the efficiency of our proposed model in terms of both single-threaded latency, and multi-threaded throughput capability. We make our system publicly available to benefit the community and to promote further research in efficient impact-ordered query processing.
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ItemNo Preview AvailableAlgorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableLossy Compression Options for Dense Index Retention(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableKey Considerations for The Design of Technology for Enrichment in Residential Aged Care: An Ethnographic Study(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableThe Future Can’t Help Fix The Past: Assessing Program Repair In The Wild(IEEE, 2023-10-01)
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly Detection(IEEE, 2023-01-01)
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ItemIdentification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning(SCITEPRESS - Science and Technology Publications, 2024)Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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