Data stream mining in medical sensor-cloud
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Sun, Le (2016) Data stream mining in medical sensor-cloud. PhD thesis, Victoria University.
Abstract
Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Along with the advent of hundreds and thousands of health monitoring sensors, the traditional wireless sensor networks and anomaly detection techniques cannot handle huge amounts of information. Sensor-cloud makes the processing and storage of big sensor data much easier. Sensor-cloud is an extension of Cloud by connecting the Wireless Sensor Networks (WSNs) and the cloud through sensor and cloud gateways, which consistently collect and process a large amount of data from various sensors located in different areas. In this thesis, I will focus on analysing a large volume of medical sensor data streams collected from Sensor-cloud. To analyse the Medical data streams, I propose a medical data stream mining framework, which is targeted on tackling four main challenges ...
| Item type | Thesis (PhD thesis) |
| URI | https://vuir.vu.edu.au/id/eprint/31032 |
| Subjects | Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Historical > FOR Classification > 0805 Distributed Computing Historical > FOR Classification > 1005 Communications Technologies Historical > FOR Classification > 1117 Public Health and Health Services Current > Division/Research > College of Science and Engineering |
| Keywords | segment data streams, abnormal subsequence, classification-based framework, anomaly detection, variable-length motifs, silhouette-value based approach, limited-length suffix array based motif discovery algorithm, LiSAM, cloud services, anomaly detection, pattern recognition, time series motifs, medical sensor-clouds, Cloud-FuSeR, fuzzy ontology, cloud computing, MCDM, healthcare, sensors |
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