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Querying of Time Series for Big Data Analytics
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Author(s): Vasileios Zois (University of Southern California, USA), Charalampos Chelmis (University of Southern California, USA)and Viktor K. Prasanna (University of Southern California, USA)
Copyright: 2016
Pages: 28
Source title:
Handbook of Research on Innovative Database Query Processing Techniques
Source Author(s)/Editor(s): Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-4666-8767-7.ch013
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Abstract
Time series data emerge naturally in many fields of applied sciences and engineering including but not limited to statistics, signal processing, mathematical finance, weather and power consumption forecasting. Although time series data have been well studied in the past, they still present a challenge to the scientific community. Advanced operations such as classification, segmentation, prediction, anomaly detection and motif discovery are very useful especially for machine learning as well as other scientific fields. The advent of Big Data in almost every scientific domain motivates us to provide an in-depth study of the state of the art approaches associated with techniques for efficient querying of time series. This chapters aims at providing a comprehensive review of the existing solutions related to time series representation, processing, indexing and querying operations.
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