KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2018-10-25Online Publication Date
2018-10-25Print Publication Date
2018-04Permanent link to this record
http://hdl.handle.net/10754/630308
Metadata
Show full item recordAbstract
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.Citation
Qahtan 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 name
34th IEEE International Conference on Data Engineering, ICDE 2018Additional Links
https://ieeexplore.ieee.org/document/8509458ae974a485f413a2113503eed53cd6c53
10.1109/ICDE.2018.00237