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Querying of Time Series for Big Data Analytics

Querying of Time Series for Big Data Analytics

Vasileios Zois, Charalampos Chelmis, Viktor K. Prasanna
Copyright: © 2016 |Pages: 28
ISBN13: 9781466687677|ISBN10: 1466687673|EISBN13: 9781466687684
DOI: 10.4018/978-1-4666-8767-7.ch013
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MLA

Zois, Vasileios, et al. "Querying of Time Series for Big Data Analytics." Handbook of Research on Innovative Database Query Processing Techniques, edited by Li Yan, IGI Global, 2016, pp. 364-391. https://doi.org/10.4018/978-1-4666-8767-7.ch013

APA

Zois, V., Chelmis, C., & Prasanna, V. K. (2016). Querying of Time Series for Big Data Analytics. In L. Yan (Ed.), Handbook of Research on Innovative Database Query Processing Techniques (pp. 364-391). IGI Global. https://doi.org/10.4018/978-1-4666-8767-7.ch013

Chicago

Zois, Vasileios, Charalampos Chelmis, and Viktor K. Prasanna. "Querying of Time Series for Big Data Analytics." In Handbook of Research on Innovative Database Query Processing Techniques, edited by Li Yan, 364-391. Hershey, PA: IGI Global, 2016. https://doi.org/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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