KDE-Track: An efficient dynamic density estimator for data streams (extended abstract)

Abdulhakim Qahtan; Suojin Wang; Xiangliang Zhang

doi:10.1109/ICDE.2018.00237

KDE-Track: An efficient dynamic density estimator for data streams (extended abstract)

Abdulhakim Qahtan, Suojin Wang, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream.

Original language	English
Title of host publication	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1759-1760
Number of pages	2
ISBN (Electronic)	9781538655207
DOIs	https://doi.org/10.1109/ICDE.2018.00237
Publication status	Published - 24 Oct 2018
Event	34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018

Other

Other	34th IEEE International Conference on Data Engineering, ICDE 2018
Country	France
City	Paris
Period	16/4/18 → 19/4/18

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Keywords

Adaptive Resampling
Data Stream
Density Visualization
Dynamic Density

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Hardware and Architecture

Cite this

Qahtan, A., Wang, S., & Zhang, X. (2018). KDE-Track: An efficient dynamic density estimator for data streams (extended abstract). In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 1759-1760). [8509458] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2018.00237

KDE-Track : An efficient dynamic density estimator for data streams (extended abstract). / Qahtan, Abdulhakim; Wang, Suojin; Zhang, Xiangliang.

Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1759-1760 8509458.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Qahtan, A, Wang, S & Zhang, X 2018, KDE-Track: An efficient dynamic density estimator for data streams (extended abstract). in Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018., 8509458, Institute of Electrical and Electronics Engineers Inc., pp. 1759-1760, 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16/4/18. https://doi.org/10.1109/ICDE.2018.00237

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Access to Document

10.1109/ICDE.2018.00237