NSPRING: the SPRING extension for subsequence matching of time series supporting normalization
Journal article
Authors/Editors
Strategic Research Themes
No matching items found.
Publication Details
Author list: Gong X., Fong S., Chan J.H., Mohammed S.
Publisher: Springer
Publication year: 2016
Journal: Journal of Supercomputing (0920-8542)
Volume number: 72
Issue number: 10
Start page: 3801
End page: 3825
Number of pages: 25
ISSN: 0920-8542
eISSN: 1573-0484
Languages: English-Great Britain (EN-GB)
View in Web of Science | View on publisher site | View citing articles in Web of Science
Abstract
Mining sequences and patterns in time series data streams is fast becoming a common practice in today’s world. The rapid progress of data collection and web technologies yields tremendous growth of flowing data in various complex forms that need to be analyzed in real time. Traditional data mining methods that typically require the process data to be scanned repeatedly are not feasible for stream data applications. However, new techniques like SPRING attempt to address these challenges by identifying sequences of patterns on time series streams, thus reducing the complexity to be linear in both time and space. Unfortunately, SPRING does not support data normalization, which renders it to be not applicable for most data sets. In this paper, we are proposing an approach called NSPRING based on SPRING that extends the advantages of SPRING, e.g., low in time and space complexity, while it can support normalization. Furthermore, NSPRING retains similar mining accuracy to SPRING. © 2015, Springer Science+Business Media New York.
Keywords
Data streams, DTW, Normalization, NSPRING, Subsequence matching, UCR-DTW
Contact Information