With the rapid development of tourism and transportation, the explosive growth of travel information, travel data has formed a huge amount of information space. How to quickly, accurately and conveniently analyze the customer relationship of the massive amount of travel data that reflects the passenger information accumulated daily is of great significance for analyzing the operation status of the tourism market, predicting the impact of tourism on related industries, and adjusting the macro policy of tourism. This paper mainly studies the massive travel data based on cloud computing. This paper studies the mining and analysis of massive travel data, and proposes a parallel algorithm for travel data mining based on constraint association rules. The algorithm can solve the problem that the existing data mining analysis method is difficult to apply due to the long frequent itemsets of massive travel data. The algorithm can organize and mine various travel data according to the main characteristics of travel data, and can provide more valuable information for a specific region, a specific group of people or a specific need. The experimental data in this paper shows that when the number of concurrent requests increases, the response time of the system before and after the improvement is quite different. When the number of concurrent requests is 2000, the response time of the system is not much different. When the number of concurrent requests is 3000, the response time of the system is obviously different. When the number of concurrent requests is greater than 4000, the difference gradually becomes larger and larger, which indicates that the improved dynamic weighted round-robin algorithm is better than the original in terms of system response time.

Yanlin Ma, Wengu Ren and Jiahong Huang. Massive Travel Data Based on Cloud Computing. Machine Learning Theory and Practice (2021), Vol. 2, Issue 2: 17-31. https://doi.org/10.38007/ML.2021.020203.

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