Research Repository

Data stream mining in medical sensor-cloud

Sun, Le (2016) Data stream mining in medical sensor-cloud. PhD thesis, Victoria University.

[img] Text
SUN Le - Thesis.pdf

Download (6MB)

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)
Uncontrolled 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
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0805 Distributed Computing
FOR Classification > 1005 Communications Technologies
FOR Classification > 1117 Public Health and Health Services
Faculty/School/Research Centre/Department > College of Science and Engineering
Depositing User: VU Library
Date Deposited: 06 Jul 2016 00:00
Last Modified: 06 Jul 2016 00:01
URI: http://vuir.vu.edu.au/id/eprint/31032
ePrint Statistics: View download statistics for this item

Repository staff only

View Item View Item

Search Google Scholar