Abstract
Selectivity estimation is an integral part of query optimization. In this paper, we propose a novel approach to approximate data density functions of relations and use them to estimate selectivities. A data density function here is approximated by a partial sum of an orthogonal series. Such approximate density functions can be derived easily, stored efficiently, and maintained dynamically. Experimental results show that our approach yields comparable or better estimation accuracy than the Wavelet and DCT methods, especially in the high dimensional spaces.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
