approximate aggregation
Recently Published Documents


TOTAL DOCUMENTS

45
(FIVE YEARS 3)

H-INDEX

11
(FIVE YEARS 0)

2021 ◽  
Vol 14 (11) ◽  
pp. 2341-2354
Author(s):  
Daniel Kang ◽  
John Guibas ◽  
Peter Bailis ◽  
Tatsunori Hashimoto ◽  
Yi Sun ◽  
...  

Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using expensive deep neural networks (DNNs). As these DNNs are expensive and because many applications can tolerate approximate answers, analysts are interested in accelerating these queries via approximations. Unfortunately, standard approximate query processing techniques to accelerate such queries are not applicable because they assume the result of the predicates are available ahead of time. Furthermore, recent work using cheap approximations (i.e., proxies) do not support aggregation queries with predicates. To accelerate aggregation queries with expensive predicates, we develop and analyze a query processing algorithm that leverages proxies (ABAE). ABAE must account for the key challenge that it may sample records that do not satisfy the predicate. To address this challenge, we first use the proxy to group records into strata so that records satisfying the predicate are ideally grouped into few strata. Given these strata, ABAE uses pilot sampling and plugin estimates to sample according to the optimal allocation. We show that ABAE converges at an optimal rate in a novel analysis of stratified sampling with draws that may not satisfy the predicate. We further show that ABAE outperforms on baselines on six real-world datasets, reducing labeling costs by up to 2.3X.


Author(s):  
Stephen Macke ◽  
Maryam Aliakbarpour ◽  
Ilias Diakonikolas ◽  
Aditya Parameswaran ◽  
Ronitt Rubinfeld

Author(s):  
Zaobo He ◽  
Akshita Maradapu Vera Venkata Sai ◽  
Yan Huang ◽  
Daehee seo ◽  
Hanzhou Zhang ◽  
...  

2017 ◽  
Vol 57 (2) ◽  
pp. 437-473 ◽  
Author(s):  
Xixian Han ◽  
Bailing Wang ◽  
Jianzhong Li ◽  
Hong Gao

2017 ◽  
pp. 1668-1668
Author(s):  
Iosif Lazaridis ◽  
Sharad Mehrotra

2016 ◽  
pp. 1-1
Author(s):  
Iosif Lazaridis ◽  
Sharad Mehrotra

Sign in / Sign up

Export Citation Format

Share Document