Education cloud application statistics method based on parallelized association rule algorithm

Abstract

The invention relates to an education cloud application statistics method based on a parallelized association rule algorithm. The method comprises the following steps of firstly performing data modeling to education cloud application access conditions and storing source data in an HDFS (Hadoop Distributed File System) in the form of Boolean matrix; secondly performing parallelized optimization to the association rule algorithm based on a Map Reduce framework, respectively compiling a Map function and a Reduce function, and performing mining analysis to the source data stored in the HDFS, thereby obtaining the visitor's using status of the education cloud application and recommending characteristics. According to the technical scheme of the method, the frequency of scanning data item sets is greatly reduced and the I / O (Input / Output) consumption of the system is reduced.

Description

technical field

[0001] The invention relates to a statistical method for educational cloud applications based on a parallelized association rule algorithm, and belongs to the technical field of computer statistics. Background technique

[0002] In recent years, with the development of the Internet and cloud computing technology, the speed and scale of data generation have greatly exceeded the past. Massive data contains a lot of value. How to use data quickly and effectively is a major challenge we face in the era of big data. The education cloud platform is the migration of cloud computing technology to the education field, including all the hardware computing resources necessary for education informatization. After these resources are virtualized, they provide users with a platform to provide education cloud services in the form of cloud applications. The education cloud platform deploys education cloud applications on the SaaS (Software as a service) layer, accesses them...

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