Main Article Content

Manuel Gómez Zotano
Corporación Radio Televisión Española
Jorge Goméz-Sanz
Universidad Complutense de Madrid
Juan Pavón
Universidad Complutense Madrid
Vol. 4 No. 3 (2015), Articles, pages 47-56
Accepted: Jun 7, 2016


Mass media websites can be worthy to understand user trends in web services. RTVE, the National Broadcaster in Spain is a sample of such kind of service. Trend points to a shorter user interaction over the last three years, and a more straight access to content. Besides the number of pages consumed in a visit is becoming smaller as well. This article reviews these trends with data obtained from public sources, and analyze the distribution of web pages in the client layer and the corresponding distribution observed in the server layer. The two distributions can be characterized by Zipf-like distributions and ?, the degree of disparity in the popularity distribution, is calculated for both. In all cases ? is higher to one implying a huge concentration of popularity on a few objects.


Download data is not yet available.

Article Details


Adobe Marketing Cloud. Metric Descriptions. [Access 26 April 2016]

Breslau, L., Cao, P., Fan, L., Phillips, G., and Shenker, S., 1999. Web caching and Zipf-like distributions: Evidence and implications. In INFOCOM’99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 1, pages 126–134. IEEE.

Calzarossa, M. C., Massari, L., and Tessera, D., 2016. Workload Characterization: A Survey Revisited. ACM Computing Surveys (CSUR), 48(3):48.

Cherubini, F. and Nielsen, R. K., 2016. Editorial Analytics: How News Media Are Developing and Using Audience Data and Metrics. Available at SSRN 2739328.

Clauset, A., Shalizi, C. R., and Newman, M. E., 2009. Power-law distributions in empirical data. SIAM review, 51(4):661–703.

Danaher, P. J., Mullarkey, G. W., and Essegaier, S., 2006. Factors affecting web site visit duration: a cross- domain analysis. Journal of Marketing Research, 43(2):182–194.

Doyle, R. P., Chase, J. S., Gadde, S., and Vahdat, A. M., 2002. The trickle-down effect: Web caching and server request distribution. Computer Communications, 25(4):345–356.

Google, 2016. Google Analytics Web Site. [Online; accessed 17-April-2016].

Krashakov, S. A., Teslyuk, A. B., and Shchur, L. N., 2006. On the universality of rank distributions of website popularity. Computer Networks, 50(11):1769–1780.

Mahanti, A., Carlsson, N., Arlitt, M., and Williamson, C., 2013. A tale of the tails: Power-laws in internet measurements. Network, IEEE, 27(1):59–64.

Mahanti, A., Williamson, C., and Wu, L., 2009. Workload characterization of a large systems conference Web server. In Communication Networks and Services Research Conference, 2009. CNSR’09. Seventh Annual, pages 55–64. IEEE.

Nair, T. and Jayarekha, P., 2010. A rank based replacement policy for multimedia server cache using zipf-like law. arXiv preprint arXiv:1003.4062.

OJD, 2016. Evolucion Audiencias RTVE.ES. [Online; accessed 17-April-2016].

Plaza, B., 2011. Google Analytics for measuring website performance. Tourism Management, 32(3):477–481. Podlipnig, S. and Böszörmenyi, L., 2003. A survey of web cache replacement strategies. ACM Computing Surveys (CSUR), 35(4):374–398.

Shi, L., Gu, Z., Wei, L., and Shi, Y., 2005. Quantitative analysis of zipf’s law on web cache. Parallel and Distributed Processing and Applications, pages 845–852.

Singal, H., Kohli, S., and Sharma, A. K., 2014. Web analytics: State-of-art & literature assessment. In Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference-, pages 24–29. IEEE.

Urdaneta, G., Pierre, G., and Van Steen, M., 2009. Wikipedia workload analysis for decentralized hosting. Computer Networks, 53(11):1830–1845.

Zotano, M. G., Gómez-Sanz, J., and Pavón, J., 2015a. Impact of traffic distribution on web cache performance. International Journal of Web Engineering and Technology, 10(3):202–213.

Zotano, M. G., Sanz, J. G., and Pavón, J., 2015b. Analysis of Web Objects Distribution. In Distributed Computing and Artificial Intelligence, 12th International Conference, pages 105–112. Springer.