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 (US)
Title of host publication	2018 IEEE 34th International Conference on Data Engineering (ICDE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1759-1760
Number of pages	2
ISBN (Print)	9781538655207
DOIs	https://doi.org/10.1109/ICDE.2018.00237
State	Published - Oct 25 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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10.1109/ICDE.2018.00237

https://repository.kaust.edu.sa/bitstream/10754/630308/1/TKDE_ICDE-kde-track.pdf

Cite this

@inproceedings{b9a79fd277f140f8b125a80a873c2707,

title = "KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)",

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.",

author = "Abdulhakim Qahtan and Suojin Wang and Xiangliang Zhang",

note = "KAUST Repository Item: Exported on 2020-10-01",

year = "2018",

month = oct,

day = "25",

doi = "10.1109/ICDE.2018.00237",

language = "English (US)",

isbn = "9781538655207",

pages = "1759--1760",

booktitle = "2018 IEEE 34th International Conference on Data Engineering (ICDE)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract). / Qahtan, Abdulhakim; Wang, Suojin; Zhang, Xiangliang.
2018 IEEE 34th International Conference on Data Engineering (ICDE). Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1759-1760.

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

TY - GEN

T1 - KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)

AU - Qahtan, Abdulhakim

AU - Wang, Suojin

AU - Zhang, Xiangliang

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2018/10/25

Y1 - 2018/10/25

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/630308

UR - https://ieeexplore.ieee.org/document/8509458

UR - http://www.scopus.com/inward/record.url?scp=85057100373&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2018.00237

DO - 10.1109/ICDE.2018.00237

M3 - Conference contribution

SN - 9781538655207

SP - 1759

EP - 1760

BT - 2018 IEEE 34th International Conference on Data Engineering (ICDE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)

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