A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
NeuroCard: One Cardinality Estimator for All Tables
[article]
2020
arXiv
pre-print
Preserved Fulltext
Other Versions
Query optimizers rely on accurate cardinality estimates to produce good execution plans. ...
Despite decades of research, existing cardinality estimators are inaccurate for complex queries, due to making lossy modeling assumptions and not capturing inter-table correlations. ...
ACKNOWLEDGMENTS We thank Joe Hellerstein for fruitful discussions and guidance, and Michael Whittaker, Richard Liaw, and Chenggang Wu for their insightful comments on this paper. ...
arXiv:2006.08109v2
fatcat:hdjtxxeduzhwjdjxa4uamgtlqi
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. ...
We have made all of the benchmark data and evaluation code publicly available at https://github.com/Nathaniel-Han/End-to-End-CardEst-Benchmark. ...
of) full outer join of all tables has poor scalability and low accuracy for numerous tables in a DB. ...
arXiv:2109.05877v3
fatcat:ufqtoe6f7nbmxcfotdlbqga7ja
Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we study cardinality estimation for SQL queries with a focus on uncertainty, which we believe is important in database systems when dealing with a large number of user queries on various ...
The approach we explore is different from the direction of deploying sophisticated DL models in database systems to build cardinality estimators. ...
Acknowledgement We thank Zongheng Yang, the author of NeuroCard [66] for his help in testing the estimator. ...
arXiv:2107.08706v1
fatcat:t35s7o5x6ve5hdw2mcxxd32jki
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
It can estimate cardinality for both single table queries and multi table join queries. ...
Query optimizers rely on accurate cardinality estimation (CardEst) to produce good execution plans. ...
When a query touches tables in multiple models, all local probabilities are corrected and merged together to estimate the final cardinality. ...
arXiv:2011.09022v5
fatcat:z5sdcuqgafgulnwgnx6u44sciu
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
2021
Proceedings of the 2021 International Conference on Management of Data
unpublished
doi:10.1145/3448016.3452830 fatcat:xkytyytqefamxfbonuzzdo3dqi
doi:10.1145/3448016.3452830 fatcat:xkytyytqefamxfbonuzzdo3dqi
Cardinality estimation is a fundamental problem in database systems. ...
To capture the rich joint data distributions of a relational table, most of the existing work either uses data as unsupervised information or uses query workload as supervised information. ...
We would like to thank Zizhong Meng (NTU) for helping with some of the experiments, and the anonymous reviewers for providing constructive feedback and valuable suggestions. ...
arXiv:2107.12295v1
fatcat:hmvedppunnbihfc5m72estcweu
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Cardinality estimation (CardEst) is an essential component in query optimizers and a fundamental problem in DBMS. ...
Our experimental results on several single-table and multi-table benchmarks indicate BayesCard's superiority over existing state-of-the-art CardEst methods: BayesCard achieves comparable or better accuracy ...
In our testing, we randomly sample 1% of all tuples for CardEst. 3). Naru/NeuroCard [52, 53] are DAR-based CardEst methods for single table and multi-table join queries, respectively. 4). ...
arXiv:2012.14743v2
fatcat:b2wf3gs5x5antmrces4jatpafq
AutoML: From Methodology to Application
2021
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
For each topic, we will motivate it with application examples from industry, illustrate the state-of-the-art methodologies, and discuss some future research directions based on our experience from industry ...
Machine Learning methods have been adopted for a wide range of real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. ...
On the other hand, data-driven cardinality estimation methods directly model the joint data distribution of all tables in the database. ...
doi:10.1145/3459637.3483279
fatcat:cu5pqsaulrah7kzvvz6oihaaci
Are We Ready For Learned Cardinality Estimation?
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 accepted doi:10.14778/3461535.3461552 arXiv:2012.06743v4 fatcat:jbodszefkzashajqmzsz5l3g7iOther Versions
Cardinality estimation is a fundamental but long unresolved problem in query optimization. ...
Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. ...
Unlike JOB, we focus on single table cardinality estimation. ...
arXiv:2012.06743v3
fatcat:5elzqkv4djfpjdw3naoetug4xa
Dependency Structures in Differentially Coded Cardiovascular Time Series
2017
Computational and Mathematical Methods in Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The latter ones were additionally binary encoded for a joint conditional entropy application. ...
This paper analyses temporal dependency in the time series recorded from aging rats, the healthy ones and those with early developed hypertension. ...
Suave Lobodzinski, UCLA, for the inspiring discussion during and after Neurocard 2016. ...
doi:10.1155/2017/2082351
pmid:28127384
pmcid:PMC5240046
fatcat:zrxxving2jez5fvm5o3ew6vf2q
Phoebe: A Learning-based Checkpoint Optimizer
[article]
2021
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. ...
For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. ...
Table 1 : 1 Cost model features Feature Group
Feature Name
Feature
De-
scription
Query Optimizer
Features
Estimated Cost, Estimated Input
Cardinality, Estimated Exclusive
Cost, Estimated Cardinality ...
doi:10.14778/3476249.3476298
arXiv:2110.02313v1
fatcat:x4x3kuq2w5di7czzl6kbdqruw4