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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1759-1760
Number of pages2
ISBN (Electronic)9781538655207
DOIs
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Other

Other34th IEEE International Conference on Data Engineering, ICDE 2018
CountryFrance
CityParis
Period16/4/1819/4/18

Fingerprint

Probability density function
Global positioning system
Interpolation
Availability
Data storage equipment
Sensors
Estimator
Data streams
Kernel
Density estimation
Density function
Sensor

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