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


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 (US)
Title of host publication2018 IEEE 34th International Conference on Data Engineering (ICDE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages2
ISBN (Print)9781538655207
StatePublished - Oct 25 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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