The Algorithm of the Snail: An Example to Grasp the Window of Opportunity to Boost Big Data
Abstract This work explores a new application which can effectively meet different localization accuracy requirements of most data location services studying the interactions between customers and suppliers. It helps to have the status or position of what is sought with respect to an address that summarizes thus a reference point which is the point of research. This proposal explains what snail algorithm is and how we can benefit from using it for the localization of information for business applications especially in the field of analytics. A business application using our algorithm has been developed by the Autour.com company (located in the department of Herault, Montpellier city) to illustrate its feasibility and availability. The results show that our algorithm can improve the localization accuracy.
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Cranshaw, J., Schwartz, R., Hong, J. I, and Sadeh, N., The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. In Proceedings of ICWSM 2012, 2012.
Hoey J., Chantier M. J., Trucco E. et al, Tracking using flock of features, with application to assisted handwashing, British Machine Vision Conference BMVC, pp. 367-376, 2006.
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Zheng, Y., Zhang, L., Ma, Z., Xie, X., and Ma, W. Y, Recommending Friends and Locations Based on Individual Location History, ACM Transactions on the Web (TWEB) 5, no. 1, 5, 2011. https://doi.org/10.1145/1921591.1921596
Zhou J., and Shi J., RFID localization algorithms and applications—a review, Journal of Intelligent Manufacturing, vol. 20, no. 6, pp. 695-707, 2008. https://doi.org/10.1007/s10845-008-0158-5
Monino, J. L., & Sedkaoui, S. (2016). The Algorithm of the Snail: An Example to Grasp the Window of Opportunity to Boost Big Data. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 5(3), 63–71. https://doi.org/10.14201/ADCAIJ2016536371
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