KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Online Publication Date2018-10-25
Print Publication Date2018-04
Permanent link to this recordhttp://hdl.handle.net/10754/630308
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AbstractRecent 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.
CitationQahtan A, Wang S, Zhang X (2018) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract). 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00237.
Conference/Event name34th IEEE International Conference on Data Engineering, ICDE 2018