Efficient Sampling Methods for Shortest Path Query over Uncertain Graphs

2014 ◽  
pp. 124-140 ◽  
Author(s):  
Yurong Cheng ◽  
Ye Yuan ◽  
Guoren Wang ◽  
Baiyou Qiao ◽  
Zhiqiong Wang
Keyword(s):  
Shortest Path ◽  
Sampling Methods ◽  
Uncertain Graphs ◽  
Efficient Sampling ◽  
Shortest Path Query

Threshold-Based Shortest Path Query over Large Correlated Uncertain Graphs

2015 ◽  
Vol 30 (4) ◽  
pp. 762-780 ◽  
Author(s):  
Yu-Rong Cheng ◽  
Ye Yuan ◽  
Lei Chen ◽  
Guo-Ren Wang
Keyword(s):  
Shortest Path ◽  
Uncertain Graphs ◽  
Shortest Path Query

Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query

2017 ◽  
Vol 96 (2) ◽  
pp. 2305-2322 ◽  
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Bin Wang
Keyword(s):  
Shortest Path ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Shortest Path Query

A novel shortest path query algorithm

2018 ◽  
Vol 22 (S3) ◽  
pp. 6729-6740
Author(s):  
Wei Chen ◽  
Ziyang Chen ◽  
Jia Liu ◽  
Qingzhang Yang
Keyword(s):  
Shortest Path ◽  
Query Algorithm ◽  
Shortest Path Query

Tools for Minimizing Probabilistic Confidence Intervals

2005 ◽  
Author(s):  
Brian K. Beachkofski ◽  
Ramana V. Grandhi
Keyword(s):  
Confidence Interval ◽  
Evolutionary Algorithms ◽  
Confidence Intervals ◽  
Sampling Methods ◽  
Voronoi Tessellation ◽  
Common Goal ◽  
Efficient Sampling ◽  
Centroidal Voronoi Tessellation ◽  
Complex Engineering ◽  
Computational Budget

For probabilistic designs or assessments to be acceptable, they must have the statistically robust confidence intervals provided by sampling methods. However, sample-based analyses require the number of function evaluations to be so great as to be impractical for many complex engineering applications. Efficient sampling methods allow probabilistic analysis on more applications than basic methods, although they still require a significant computational budget. This paper reviews a series of tools that aim to reduce variance in individual failure rate estimates which would reduce the confidence interval for the same number of evaluations. Several methods share a common goal, lowering the sample discrepancy within the sample space, that will create near optimal low-discrepancy sample sets. The optimization approaches include evolutionary algorithms, piecewise optimization, and centroidal Voronoi tessellation. The results of the optimization procedures show a much lower discrepancy than previous methods.

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