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A Framework for Effective Data Analytics for Tourism Sector: Big Data Approach

A Framework for Effective Data Analytics for Tourism Sector: Big Data Approach

Sapna Sinha, Vishal Bhatnagar, Abhay Bansal
Copyright: © 2017 |Volume: 9 |Issue: 4 |Pages: 13
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781522512066|DOI: 10.4018/IJGHPC.2017100106
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MLA

Sinha, Sapna, et al. "A Framework for Effective Data Analytics for Tourism Sector: Big Data Approach." IJGHPC vol.9, no.4 2017: pp.92-104. http://doi.org/10.4018/IJGHPC.2017100106

APA

Sinha, S., Bhatnagar, V., & Bansal, A. (2017). A Framework for Effective Data Analytics for Tourism Sector: Big Data Approach. International Journal of Grid and High Performance Computing (IJGHPC), 9(4), 92-104. http://doi.org/10.4018/IJGHPC.2017100106

Chicago

Sinha, Sapna, Vishal Bhatnagar, and Abhay Bansal. "A Framework for Effective Data Analytics for Tourism Sector: Big Data Approach," International Journal of Grid and High Performance Computing (IJGHPC) 9, no.4: 92-104. http://doi.org/10.4018/IJGHPC.2017100106

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Abstract

From BRICS nations, India is the second largest tourism market after China in Asia. Technological revolution has added new dimensions to the way technologies being used in all the sectors. Also, the use of electronic gadgets leaves trail of data, which is very huge in size, this data (Big Data) is exploited by every sector for providing better services and gaining competitive edge. This trend grabbed the attention of researchers and industry for development of more optimized tools and techniques. There are many general frameworks proposed by industry and researchers for implementation of Big Data in industry but, there is no framework proposed for tourism sector. In this paper, the authors propose unified IT infrastructure framework named as tAdvisor for effective data analytics using Big Data Analytics approach for increasing productivity in tourism sector. Various challenges and issues related with the implementation of Big Data Analytics is also discussed in the paper.

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