Online Computing Quantile Summaries Over Uncertain Data Streams
Quantile summarization is a useful tool in data streams management and mining that can efficiently capture the distribution of the data. A quantile of a sequence of points is the point with a given rank in the sequence. Given a sequence of uncertain points S on the real line, each represented by one...
Main Authors: | Chunquan Liang, Mei Li, Bin Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8606044/ |
Similar Items
-
A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs
by: Chao Li, et al.
Published: (2021-01-01) -
Accurate Quantile Estimation for Skewed Data Streams Using Nonlinear Interpolation
by: Jun Liu, et al.
Published: (2018-01-01) -
Mining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows
by: fatemeh Abdi, et al.
Published: (2015-11-01) -
UDDSketch: Accurate Tracking of Quantiles in Data Streams
by: Italo Epicoco, et al.
Published: (2020-01-01) -
High-Performance Processing of Continuous Uncertain Data
by: Tran, Thanh Thi Lac
Published: (2013)