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 |
|---|---|
| Title of host publication | Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1759-1760 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538655207 |
| DOIs | |
| Publication status | Published - 24 Oct 2018 |
| Event | 34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 |
Other
| Other | 34th IEEE International Conference on Data Engineering, ICDE 2018 |
|---|---|
| Country | France |
| City | Paris |
| Period | 16/4/18 → 19/4/18 |
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Keywords
- Adaptive Resampling
- Data Stream
- Density Visualization
- Dynamic Density
ASJC Scopus subject areas
- Information Systems
- Information Systems and Management
- Hardware and Architecture
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - KDE-Track
T2 - An efficient dynamic density estimator for data streams (extended abstract)
AU - Qahtan, Abdulhakim
AU - Wang, Suojin
AU - Zhang, Xiangliang
PY - 2018/10/24
Y1 - 2018/10/24
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.
KW - Adaptive Resampling
KW - Data Stream
KW - Density Visualization
KW - Dynamic Density
UR - http://www.scopus.com/inward/record.url?scp=85057100373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057100373&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00237
DO - 10.1109/ICDE.2018.00237
M3 - Conference contribution
AN - SCOPUS:85057100373
SP - 1759
EP - 1760
BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -