A distributed intelligent mobile application for analyzing travel big data analytics
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A distributed intelligent mobile application for analyzing travel big data analytics L. Maria Michael Visuwasam 1 & D. Paul Raj 2 Received: 24 May 2019 / Accepted: 2 August 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Data mining and data analytics is promptly growing filed due to the incredible development of Big-Data. One of the financial supporting and economic strength of any government industries is tourist and development. This paper motivated to provide a good solution in social point of view to increase the economical strengths of the country. Some of the existing research works have been proposed a semi-automatic web application for guiding tourist people, but the customer satisfaction is not good. To improve the customer satisfaction, and make use of it, to improve the tourist and development industry, this paper introduced a Cloud Based Distributed algorithm named “I Guide You - (IGY)” is designed and developed as a Mobile Application. In order to access IGY at anywhere by anyone, it is designed as a parallel and distributed algorithm for improving the efficiency in terms of serving to the users. IGY is a peer-to-peer network application, since any device can directly communicate with other device in the network. All the devices connected in the IGY are share equal responsible processing the data. IGY can also be executed parallelly on every mobile device over peer to peer network on distributed travel data available in the internet. The target-location information obtained from distributed cloud, shared and served to multiple processors executing IGY parallelly. IGY is developed as a tourist assistance application anywhere based on internet with N-tier architecture. The experimental results obtained from IGY is comparing with the existing approaches and the performance is evaluated. Keywords Big data . Data analytics . Tourist data processing . Data mining . Google data . Google map
1 Introduction Data mining is a task of structuring the raw data into user required pattern. It can be obtained using various mathematical models and algorithms to produce novel information and reveal the different insights. The user required pattern provides a meaningful and relative data from the data warehouse. There are two different types of data mining methods are used This article is part of the Topical Collection: Special Issue on AI-based Future Intelligent Internet of Things Guest Editors: Kelvin K.L. Wong, Quan Zou, and Pourya Shamsolmoali * L. Maria Michael Visuwasam [email protected]; [email protected] D. Paul Raj [email protected] 1
Department of Computer Science and Engineering, Rajalakshmi Institute of technology, Chennai, India
2
Department of Computer Science and Engineering, R. M. D. Engineering College, Kavarapettai, Chennai, India
such as descriptive and predictive. Description method gives the detailed information about the organized data whereas predictive method forecasts the data. Data mining can extract a specific pattern within a la
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