THE ALGORITHM OF THE SNAIL: AN EXAMPLE TO GRASP THE WINDOW OF OPPORTUNITY TO BOOST BIG DATA

General information

Authors:
Jean Louis MONINO, Soraya SEDKAOUI

DOI:
10.14201/ADCAIJ2016536371

Volume:
Regular Issue 5 (3), 2016

Keywords:

Algorithm of the snail; localization; big data; real time analysis

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

References

Addlesee M., Curwen R., Hodges S. et al, Implementing a Sentient Computing System, IEEE Computer, vol. 34, no. 8, pp. 50-56, 2001. https://doi.org/10.1109/2.940013

Antoni J.P., Urban sprawl modelling : A methodological approach, Published in Cybergeo, European Journal of Geography, 2002, Topics, 12th European Colloquium on Quantitative and Theoretical Geography.St-Valery-en-Caux, France, September 7-11, 2001. St-Valery-en-Caux, France, September 7-11, 2001.

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.

Langlois, P. et Lajoie G., Cartographie par carroyage et précision spatiale, Mappemonde, vol. 49, no 1, p. 20-23, 1998.

Mazurek H. et Dayre P., Analyse de l'utilisation du sol par la méthode du carroyage: le District Urbain de Montpellier, Mappemonde, n°88/3, GIP. RECULS, pp. 27-30, 1988.

Mcnulty E., Big data: the seven V's, available at: http://dataconomy.com/seven-vs-big-data/, 2014.

Monino L.L., Sedkaoui S., Matouk J., Big data, éthique des données, et entreprises, Les Cahiers du CEDIMES, Dossier "Economie et gouvernance", vol. 8, no. 2, available at: http://cedimes.com/images/documents/cahiers_cedimes/cahier_cedimes_vol_8..., 2014.

Song, C., Qu, Z., Blumm, N., and Barabási, AL, Limits of Predictability in Human Mobility, Science 327, no. 5968, 1018-1021, 2010. https://doi.org/10.1126/science.1177170

Zhang, D., Guo, B., Li, B., and Yu, Z. Extracting Social and Community Intelligence from Digital Footprints: An Emerging Research Area, Ubiquitous Intelligence and Computing, 4-18, 2010.

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

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