Abstract
Different business types may have distinct businesses functioning dynamics, i.e., popularity times, that can be dictated not only by the service offered but also due to other aspects. Performing the business popularity time comprehension allows us, for instance, to use this information as a business descriptor that could be explored in new services. Recently, Google launched a service, namely Popular Times, which provides the popularity times of commercial establishments. In this study, we collected and analyzed a large-scale dataset provided by that service for business in different cities in Brazil and in the United States. Our main contributions are: (1) clustering and analysis of the collected business popularity times dataset in each studied city; (2) approach for identifying the signature that represents the behavior of specific categories of venues; (3) training and evaluation of an inference model for categories of establishments; (4) user evaluation of some of our results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., PéRez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Figueiredo, F., Almeida, J.M., Gonçalves, M.A., Benevenuto, F.: On the dynamics of social media popularity: a Youtube case study. ACM Trans. Internet Technol. (TOIT) 14(4), 24 (2014)
Google: Google popular times (2017). https://support.google.com/business/answer/6263531. Accessed 10 Sept 2017
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
Hartigan, J.A.: Clustering Algorithms, vol. 209. Wiley, New York (1975)
Krumm, J., Rouhana, D.: Placer: semantic place labels from diary data. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Zurich, Switzerland, pp. 163–172. ACM (2013)
Liao, T.W.: Clustering of time series data - a survey. Pattern Recogn. 38(11), 1857–1874 (2005)
Neves, Y.C., Sindeaux, M.P., Souza, W., Kozievitch, N.P., Loureiro, A.A., Silva, T.H.: Study of Google popularity times series for commercial establishments of Curitiba and Chicago. In: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, Teresina, Piauí, Brazil, pp. 303–310. ACM (2016)
Petitjean, F., Ketterlin, A., Gançarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. 44(3), 678–693 (2011)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)
Silva, T.H., Loureiro, A.A.: Users in the urban sensing process: challenges and research opportunities. In: Pervasive Computing: Next Generation Platforms for Intelligent Data Collection, pp. 45–95. Academic Press (2016)
Silva, T.H., Vaz de Melo, P.O.S., Almeida, J.M., Salles, J., Loureiro, A.A.F.: A picture of Instagram is worth more than a thousand words: workload characterization and application, pp. 123–132, May 2013
Silva, T.H., de Melo, P.O.V., Almeida, J.M., Musolesi, M., Loureiro, A.A.: A large-scale study of cultural differences using urban data about eating and drinking preferences. Inf. Syst. 72(Suppl. C), 95–116 (2017). https://doi.org/10.1016/j.is.2017.10.002, http://www.sciencedirect.com/science/article/pii/S0306437917300261
Tostes, A.I.J., Silva, T.H., Assuncao, R., Duarte-Figueiredo, F.L.P., Loureiro, A.A.F.: Strip: a short-term traffic jam prediction based on logistic regression. In: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, Canada (2016)
Vaca, C.K., Quercia, D., Bonchi, F., Fraternali, P.: Taxonomy-based discovery and annotation of functional areas in the city. In: Proceedings of ICWSM 2015, Oxford, UK (2015)
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Kowloon, Hong Kong, pp. 177–186. ACM. Kowloon (2011)
Ye, M., Shou, D., Lee, W.C., Yin, P., Janowicz, K.: On the semantic annotation of places in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 520–528. ACM. San Diego (2011)
Yelp: Yelp developers (2017). https://www.yelp.com/developers/documentation/v3. Accessed 10 Sept 2017
Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 38 (2014)
Acknowledgements
This work was partially supported by the project CNPq-URBCOMP (process 403260/2016-7), CAPES, and Fundação Araucária. The authors would also like to thank all the volunteers for the valuable help in this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
da Silva, L.d.A., Silva, T.H. (2019). Extraction and Exploration of Business Categories Signatures. In: Oliveira, J., Farias, C., Pacitti, E., Fortino, G. (eds) Big Social Data and Urban Computing. BiDU 2018. Communications in Computer and Information Science, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-11238-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-11238-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11237-0
Online ISBN: 978-3-030-11238-7
eBook Packages: Computer ScienceComputer Science (R0)