Indexing method and system of concurrent hash index data structure based on machine learning

Abstract

The invention provides an indexing method and system for a concurrent hash index data structure based on machine learning. The indexing method comprises the steps of M1, searching a group node where atarget key value pair is located for from a root node according to a key in a request; M2, in the group nodes obtained through positioning, calculating data positions according to a machine learningmodel of the group nodes; and M3, searching target data according to the data position, and carrying out corresponding operation according to the request type. Compared with an existing machine learning hash index, the system can retrain the machine learning model, hash expansion operation is carried out when the hash conflict probability exceeds a threshold value, the machine learning model is made to adapt to newly inserted data, and the high performance of the index is kept.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented method improves how well it works for both traditional binary search indexes like LUTS or SHA-1. It also allows for parallel processing without slowdown due to sequential indexing techniques such as merge caching and sparse matrix factorization. Additionally, there are advantages over current methods including improved efficiency and reduced latencies associated with these new systems compared to previous ones. Overall, this improvement makes them more efficient than older technologies at performing complex searches faster and easier.

Problems solved by technology

Technologies described involve improving the performance and scalability of conventional systems like LISAR, XOR logic, etc., particularly relating to identifying objects called tokens. These technical problem addressed by this patents involves efficiently performing parallel searches over these attributes without compromising their accuracy. Additionally, current approaches require prior knowledge about how much space they occupy before selecting relevant attributes, making them slow down even further.

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