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Error Assessment for Multi-Join AQP using Bootstrap Sampling

10 pagesPublished: March 21, 2024

Abstract

Approximate query processing (AQP) is a computing efficient scheme to provide fast and accurate estimations for big data queries. However, assessing the error of an AQP estimation remains an open challenge for high-dimensional multi-relation data. Existing research often focuses on the online AQP methods which heavily rely on expensive auxil- iary data structures. The contribution of this research is three-fold. First, we develop a new framework employing a non-parametric statistic method, namely bootstrap sampling, towards error assessment for multi-join AQP query estimation. Second, we extend the cur- rent AQP schemes from providing point estimations to range estimations by offering the confidence intervals of a query estimation. Third, a prototype system is implemented to benchmark the proposed framework. The experimental results demonstrate the prototype system generates accurate confidence intervals for various join query estimations.

Keyphrases: approximate query processing, bootstrap sampling, error estimation

In: Ajay Bandi, Mohammad Hossain and Ying Jin (editors). Proceedings of 39th International Conference on Computers and Their Applications, vol 98, pages 46-55.

BibTeX entry
@inproceedings{CATA2024:Error_Assessment_Multi_Join,
  author    = {Sabin Maharjan and Lucy Kerns and Xiangjia Min and Feng Yu},
  title     = {Error Assessment for Multi-Join AQP using Bootstrap Sampling},
  booktitle = {Proceedings of 39th International Conference on Computers and Their Applications},
  editor    = {Ajay Bandi and Mohammad Hossain and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {98},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/CKd8},
  doi       = {10.29007/nqh9},
  pages     = {46-55},
  year      = {2024}}
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