A Big Data Platform Tourism Price Strategy Method with Map/Reduce

Abstract

The Google Hadoop platform Map/Reduce task scheduling and distribution mechanism of the Hadoop distributed computing framework applied to cloud computing and big data. The Quartz open source job scheduler regularly crawls into the websites of different tourist attractions, and stores the tourist attractions prices calculated by the price comparison algorithm to the Database HBase distribution Computing System. When the user enters the planned departure place, departure date, tourist attractions and other specific conditions, the cloud platform price comparison strategy system will display tourist routes according to certain logic, and generate price comparison data for tourist attractions, from the travel start point to the travel destination. The price comparison strategy of clothing, food, housing, transportation and consumption generates cost prices, recommends the best travel planning plan for customers, helps users choose the most economical tourist attractions and tourist routes to make quick choices, and obtain satisfactory returns for short vacations or holidays to avoid delay in decision-making time.