Using positional sequence patterns to estimate the selectivity of SQL LIKE queries

Aytimur, Mehmet; Çakmak, Ali

doi:10.1016/j.eswa.2020.113762

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches

Sontakke,

Jafari,

Saeedi

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches

Sontakke,

Jafari,

Saeedi

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries

Aytimur,

Reiner,

Wörteler

et al. 2024

Proc. ACM Manag. Data

View full text Add to dashboard Cite

Cardinality estimation is an important step in cost-based database query optimization. The accuracy of the estimates directly affects the ability of an optimizer to identify the most efficient query execution plan correctly. In this paper, we study cardinality estimation of LIKE-queries, i.e., queries that use the LIKE-operator to match a pattern with wildcards against string-valued attributes. While both traditional and machine-learning-based approaches have been proposed to tackle this problem, we argue that they all suffer from drawbacks. Most importantly, many state-of-the-art approaches are not designed for patterns that contain wildcards in-between characters. Based on past research on neural language models, we introduce the LIKE-Pattern Language Model (LPLM) that uses a new language and a novel probability distribution function to capture the semantics of general LIKE-patterns. We also propose a method to generate training data for our model. We demonstrate that our method outperforms state-of-the-art approaches in terms of precision (Q-error), while offering comparable runtime performance and memory requirements.

show abstract

Using positional sequence patterns to estimate the selectivity of SQL LIKE queries

Cited by 2 publications

References 30 publications

Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches

Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches

LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries

Contact Info

Product

Resources

About