Distributed data analysis task scheduling system

Abstract

The invention provides a distributed data analysis task scheduling system. The system comprises a distributed data storage service module, a resource-based distributed task scheduling engine module, adistributed message queue module, a distributed application coordination service module and an automatic execution engine module. The invention provides a distributed task scheduling framework for performing data analysis by utilizing an R language; distributed resource management is realized by utilizing a resource management platform, so that distributed scheduling execution of R language analysis is realized; and by utilizing an automatic scheduling engine, automatic calling of a data fragmentation task is realized, so that the automatic analysis demand of data tracking in an industrial process is met.

Description

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AI Technical Summary

Benefits of technology

This technology uses a system that allows companies to analyze their business processes more efficiently while still maintaining high reliability over time by providing efficient processing capabilities at different stages within them. It achieves this by distributing work across multiple servers or machines instead of relying on one single machine. Overall, it improves efficiency and productivity during complex industries such as production management.

Problems solved by technology

Technological Problem addressed in this patented study describes how current methods work well when analyzed datasets require frequent reconfiguration due to their high complexity. Existing techniques involve manually distributing and storing data across nodes without considering future needs. Therefore, these limitations lead to overuse of resources like computing power and communication bandwidth during peak periods where most assets run out.

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