US 12,436,950 B1
Machine learning accelerated semantic equivalence detection
Brandon Barry Haynes, Seattle, WA (US); Rana Bijad M Alotaibi, Redmond, WA (US); Anna Pavlenko, Edmonds, WA (US); Yuanyuan Tian, San Jose, CA (US); Jyoti Leeka, Sunnyvale, CA (US); and Alekh Jindal, Seattle, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Mar. 29, 2024, as Appl. No. 18/622,857.
Int. Cl. G06F 16/00 (2019.01); G06F 16/2453 (2019.01); G06N 5/01 (2023.01)
CPC G06F 16/24542 (2019.01) [G06N 5/01 (2023.01)] 20 Claims
OG exemplary drawing
 
1. An equivalence detection system comprising:
a processor; and
a memory comprising computer-readable instructions, the processor, the memory and the computer-readable instructions configured to cause the processor to:
convert a query plan tree associated with a first subexpression into a matrix, the first subexpression being a portion of a database query from a computational workload, a node in the query plan tree being represented as a row of the matrix;
convert the matrix into a first vector of a predetermined length;
determine that the first subexpression is equivalent to a second subexpression based on comparing the first vector to a second vector associated with the second subexpression, the comparing including computing a distance between the first and second vectors that is lower than a distance threshold; and
alter the computational workload, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.