A distributed data mining workflow-oriented task scheduling system

Abstract

The invention discloses a distributed data mining workflow-oriented task scheduling system, and relates to the field of data mining. Comprising a function module, a search module, a control module and a canvas module. Each module comprises a function area corresponding to the function module, a search area corresponding to the search module, a control area corresponding to the control module and a canvas area corresponding to the canvas module in a user interface of the system; aiming at the problems existing in the traditional data mining task scheduling technology, the invention designs a distributed data mining workflow-oriented task scheduling system. A long task first (LTF) scheduling algorithm is designed for the index of total completion time of parallel subtasks in a data mining workflow task. According to the invention, the data mining task is performed in a distributed manner by using a distributed technology, so that the execution efficiency of the data mining task is greatly improved.

Description

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AI Technical Summary

Benefits of technology

This patented method allows users to efficiently analyze large amounts of complex datasets without having to manually sort through irrelevant parts or data points. It uses flowcharts and graphs techniques to create tasks with specific timing requirements based on their importance level. A scheduler module predicts how long each step should be completed before starting another one. By doing these calculations, it helps optimize resource allocation among different components while reducing overall processing times.

Problems solved by technology

This patents describes two technical problem addressed by this patented method: current job scheduler systems only consider single jobs but cannot efficiently allocate resources across multiple workloads simultaneously due to lack of clarity about what each individual process needs prioritized over other processes' responsiveness during execution.

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