A Hotspot Framework for Analyzing Geolocated Travel Data Using Spark

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L. Maria Michael Visuwasam, Subbiah Swaminathan, S. Rajalakshmi, K. Pradheep Kumar

Abstract

Nowadays, travelling is more important based on traveller perspective such as job, tourist, presentation, conference, etc. The effective prediction framework model is needed for analyzing travelling places and services such as hospitality, location, area and security. In this paper, we propose a Hotspot framework model for collecting tourist identification, transportation, preferences and utilization. This model can demonstrate geolocation-based travel data collected from social media dataset. The different attributes are involved such as agencies, transport and tourist. This graph based iterative approach and propagation learning algorithm is used for segmenting attributes using Geolocated travel data. The target groups are using this model and makes effective decisions. This is interactive approach and data is collected from social media real world dataset for analytics. The Spark tool is used for experiment the dataset and the performance is compared with existing results.

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How to Cite
L. Maria Michael Visuwasam, Subbiah Swaminathan, S. Rajalakshmi, K. Pradheep Kumar. (2021). A Hotspot Framework for Analyzing Geolocated Travel Data Using Spark. Annals of the Romanian Society for Cell Biology, 1956 - 1966. Retrieved from http://annalsofrscb.ro/index.php/journal/article/view/313
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