Input-driven transaction-oriented time-space efficiency optimization method for storage and calculation-integrated coordinated adjustment

Abstract

The invention discloses an input-driven transaction-oriented time-space efficiency optimization method for storage and calculation-integrated coordinated adjustment, provides interpretation of resource forms, including data, information and knowledge, and concept representation of a data map, an information map and a knowledge map, and belongs to the field of crossover of distributed calculation and a software engineering technology. Storage is determined by calculation and search is served by storage, and which layer resources should be stored on cannot be judged just from the resource type. The resource type transfer cost and calculation cost scheme with the most reasonable cost is found out according to user input, a resource object search mechanism and a resource space organization scheme are reasonably determined, and improvement of the resource search efficiency is facilitated.

Description

technical field [0001] The present invention is an input-driven storage and computing integrated and coordinated adjustment transaction-oriented spatio-temporal efficiency optimization method, which comprehensively considers resource organization schemes based on storage costs and search costs for resources in the form of data, information, and knowledge. It is mainly used to rationally organize and store resources in the form of data, information, and knowledge through data graphs, information graphs, and knowledge graphs to improve search efficiency. It 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 ...

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

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