Data Reduction in Very Large Spatio-Temporal Data Sets
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|Title:||Data Reduction in Very Large Spatio-Temporal Data Sets||Authors:||Whelan, Michael
|Permanent link:||http://hdl.handle.net/10197/7847||Date:||30-Jun-2010||Abstract:||Today, huge amounts of data are being collected with spatial and temporal components from sources such as metrological, satellite imagery etc.. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge from very large size of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. This paper presents a data reduction technique based on clustering to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2010 IEEE||Keywords:||Data mining;Spatio-temporal datasets;Clustering;Data reduction||DOI:||10.1109/WETICE.2010.23||Language:||en||Status of Item:||Peer reviewed||Conference Details:||2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE 2010), Larissa, Greece, 28-30 June, 2010|
|Appears in Collections:||Computer Science Research Collection|
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