Transaction processing optimization method of input-driven storage and computing integration for typed resources

Abstract

The invention discloses a transaction processing optimization method of input-driven storage and computing integration for typed resources, gives out resource sources including explanation of data, information, knowledge and the like and concept representation of data mapping, information mapping and knowledge mapping and belongs to the field of intersection of distributed computing and software engineering technology. The storage lies in that a scheme is collaboratively adjusted by computing, storage is determined by computing, storage serves search, the scheme finding out resource type transfer cost and computing cost with the most reasonable cost according to user input, a resource object processing mechanism and a resource space organization scheme are determined reasonably, and spatial and temporal efficiency in transaction resource processing is optimized.

Description

technical field [0001] The present invention is an input-driven transaction processing optimization method that integrates storage and computing for typed resources, and models resources that exist in the form of data, information, and knowledge based on a framework composed of data graphs, information graphs, and knowledge graphs. , organize and store resources rationally from the perspective of resource processing and processing optimization, and optimize the time-space efficiency of resource processing, which belongs to the interdisciplinary field of distributed computing and software engineering technology. Background technique [0002] Traditional search engine technology quickly retrieves and sorts webpage resources according to user query requirements. For a large number of resources fed back by search engines, users need to manually search and filter, resulting in low search efficiency and difficulty in meeting users' needs for quickly obtaining resources. Existing t...

AI Technical Summary

Problems solved by technology

[0002] Traditional search engine technology quickly retrieves and sorts web resources according to user query requirements. For a large number of resources fed back by search engines, users need to manually search and filter, resulting in low search efficiency and difficulty in meeting users' needs for quickly obtaining resources.

Existing techniques including machine learning-based approaches and ontology-based approaches have made many contributions to address this challenge, however, machine learning-based approaches lack effective mechanisms to explicitly incorporate empirical knowledge with trained models, Ontology-based approaches are limited by the heavy burden on the part of human experts

Images