single-jc.php

JACIII Vol.18 No.3 pp. 306-310
doi: 10.20965/jaciii.2014.p0306
(2014)

Paper:

The Analysis of Portals Considering Mobile Clients

Gergely Kocsis, Péter Ekler, and István Albert

Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Magyar Tudsok krtja 2., 1117 Budapest, Hungary

Received:
April 25, 2013
Accepted:
August 8, 2013
Published:
May 20, 2014
Keywords:
website load, mobile websites, resource requirement estimation
Abstract

Web analytics are used to retrieve anonymous information about users. We focus here on websites that support mobile clients. This information is important from the perspective of business analysis as web analytics help in making appropriate design decisions. Popular web sites may handle several million page views a day, so poor system design – even that related only to collecting statistics on user behavior – may produce performance bottlenecks or even system failures. This paper presents measurements based on a userdata database for a large mobile supported website and a model used when designing such sites.

Cite this article as:
G. Kocsis, P. Ekler, and I. Albert, “The Analysis of Portals Considering Mobile Clients,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.3, pp. 306-310, 2014.
Data files:
References
  1. [1] “Gartner SaysWorldwide Smartphone Sales Soared in Fourth Quarter of 2011 With 47 Percent Growth,” Gartner Newsroom (Feb. 15, 2012), Available at:
    http://www.gartner.com/it/page.jsp?id=1924314 [Accessed March 18, 2014].
  2. [2] T. Bickmore and B. Schilit, “Digestor: device-independent access to the world wide web,” Computer Networks and ISDN Systems, Vol.29, No.8-13, pp. 1075-1082, 1997.
  3. [3] G. Schmiedl, M. Seidl, and K. Temper, “Mobile phone web browsing: a study on usage and usability of the mobile web,” Proc. of MobileHCI’09, No.70, 2009.
  4. [4] K. R. Suneetha and R. Krishnamoorthi, “Identifying User Behavior by Analyzing Web Server Access Log File,” IJCSNS, Int. J. of Computer Science and Network Security, Vol.9. No.4, pp. 327-332, April 2009.
  5. [5] M. Heydari, R. A. Helal, and K. I. Ghauth, “A graph-based web usage mining method considering client side data,” Int. Conf. on Electrical Engineering and Informatics 2009 (ICEEI’09), Vol.1, pp. 147-153, Aug. 5-7, 2009.
  6. [6] H. Obendorf, H. Weinreich, E. Herder, and M. Mayer, “Web page revisitation revisited: implications of a long-term click-stream study of browser usage,” Proc. of the SIGCHI Conf. on Human factors in computing systems (CHI ’07), pp. 597-606, 2007.
  7. [7] Unideb. Definition of exponential distribution,
    http://www.inf.unideb.hu/valseg/JEGYZET/valseg/node139.htm [Accessed March 18, 2014], Aug. 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 16, 2018