Research On Catering Big Data Analysis System Based On FP-tree And Map Memory Optimization

With the continuous development of big data technology,it is a new technological revolution for all sectors of society.The popularization of related technologies is very obvious for scientific and technological breakthroughs.The use of big data by the catering industry and its development and changes in the era of big data will greatly enhance the competitiveness of the catering industry.The catering industry must actively use the new processing model to collect,store,manage,and analyze catering big data.Big data can effectively and cost-effectively reduce costs,strengthen management,improve business performance,and improve consumer experience for catering companies.Since its establishment in the 1990 s,a catering group has established more than 110 restaurants and pastry shops in Hong Kong and South China.These shops are located in prime locations in their respective regions,and the people are prosperous,attracting a large number of well-to-do mid-market customers.The catering group's management of its branches depends on a catering management system for management.With the increasing number of stores,the scope of coverage is constantly expanding,the amount of data stored in the system is increasing,and the data is becoming more and more complex.These data are the core of the group.The senior management of the group hopes to be able to analyze useful information from these discrete and massive data to provide reference for further expansion and store management.This article starts with the actual conditions and needs of the various restaurants of the group,researches and analyzes the system and sales data currently used by the group,and develops a big data analysis system based on the FP-tree correlation algorithm to provide user data storage and data.Processing,data analysis,association rule mining and other functional modules,and optimized for system performance issues.Firstly,the data of the existing systems of the group's branches are processed through ETL preprocessing operations and integrated into the Hive data warehouse.Then,the aggregated data are aggregated and statistically analyzed using Spark,and the sales data of the branches is performed using the FP-tree correlation algorithm.Analyze and contrast,expect to get the customer's relevant eating habits,and provide some decisionmaking support for the promotion of the next store and the development of new dishes.