Incremental Computation of Queries

2018 ◽  
pp. 1830-1833
Author(s):  
Guozhu Dong ◽  
Jianwen Su
Keyword(s):  
Incremental Computation

scholarly journals Efficient EMD-based Similarity Search via Batch Pruning and Incremental Computation

2021 ◽  
pp. 1-1
Author(s):  
Yu Chen ◽  
Yong Zhang ◽  
Jin Wang ◽  
Jiacheng Wu ◽  
Chunxiao Xing
Keyword(s):  
Similarity Search ◽  
Incremental Computation

scholarly journals Incremental computation with names

2015 ◽  
Vol 50 (10) ◽  
pp. 748-766 ◽  
Author(s):  
Matthew A. Hammer ◽  
Joshua Dunfield ◽  
Kyle Headley ◽  
Nicholas Labich ◽  
Jeffrey S. Foster ◽  
...  
Keyword(s):  
Incremental Computation

Incremental Computation of Queries

2016 ◽  
pp. 1-4
Author(s):  
Guozhu Dong ◽  
Jianwen Su
Keyword(s):  
Incremental Computation

ORBITS

2020 ◽  
Vol 14 (3) ◽  
pp. 294-306
Author(s):  
Mourad Khayati ◽  
Ines Arous ◽  
Zakhar Tymchenko ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Online Data ◽  
Incremental Computation ◽  
Online Recovery ◽  
Recovery Technique ◽  
Centroid Decomposition

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data, or cannot recover more than one time series at a time. In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient.

Incremental computation of set difference views

1997 ◽  
Vol 9 (2) ◽  
pp. 251-261 ◽  
Author(s):  
L. Baekgaard ◽  
L. Mark
Keyword(s):  
Incremental Computation
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