Advertisement

Extraction and Exploration of Business Categories Signatures

  • Leonardo de Assis da Silva
  • Thiago H. SilvaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 926)

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.

Keywords

Google Popular Times Time series Signature Large scale urban assessment 

Notes

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.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Google: Google popular times (2017). https://support.google.com/business/answer/6263531. Accessed 10 Sept 2017
  5. 5.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  6. 6.
    Hartigan, J.A.: Clustering Algorithms, vol. 209. Wiley, New York (1975)zbMATHGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Liao, T.W.: Clustering of time series data - a survey. Pattern Recogn. 38(11), 1857–1874 (2005)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefGoogle Scholar
  12. 12.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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 2013Google Scholar
  15. 15.
    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/S0306437917300261CrossRefGoogle Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Yelp: Yelp developers (2017). https://www.yelp.com/developers/documentation/v3. Accessed 10 Sept 2017
  21. 21.
    Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 38 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Leonardo de Assis da Silva
    • 1
  • Thiago H. Silva
    • 1
    Email author
  1. 1.Department of InformaticsFederal University of Technology - ParanáCuritibaBrazil

Personalised recommendations