Differences between web sessions according to the origin of their visits

Ortega, José Luís; Aguillo, Isidro F.

doi:10.1016/j.joi.2010.02.001

Cited by 26 publications

(11 citation statements)

References 21 publications

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“…The assumption that the probability of leaving in each step is constant is consistent with the empirical observation that the number of websites visited during a session follows exponential decay; see, for example,Ortega and Aguillo [2010].…”

supporting

confidence: 67%

To Match or Not to Match

Ghosh

Mahdian

McAfee

et al. 2015

ACM Trans. Econ. Comput.

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Modern online advertising increasingly relies on the ability to follow the same user across the Internet using technology called cookie matching to increase efficiency in ad allocation. Web publishers today use this technology to share information about the websites a user has visited, making it possible to target advertisements to users based on their prior history. This begs the question: do publishers (who are competitors for advertising money) always have the incentive to share online information? Intuitive arguments as well as anecdotal evidence suggest that sometimes a premium publisher might suffer information sharing through an effect called information leakage: by sharing user information with the advertiser, the advertiser will be able to target the same user elsewhere on cheaper publishers, leading to a dilution of the value of the supply on the premium publishers.The goal of this article is to explore this aspect of online information sharing. We show that, when advertisers are homogeneous in the sense that their relative valuations of users are consistent, publishers always agree about the benefits of cookie matching in equilibrium: either all publishers' revenues benefit, or all suffer, from cookie matching. We also show using a simple model that, when advertisers are not homogeneous, the information leakage indeed can occur, with cookie matching helping one publisher's revenues while harming the other.

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supporting

confidence: 67%

To Match or Not to Match

Ghosh

Mahdian

McAfee

et al. 2015

ACM Trans. Econ. Comput.

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“…It is therefore important to characterize the probability distribution of session length k . Several studies presenting analyses of real Web server workloads suggest that session length follows a Weibull distribution, one of the heavy‐tailed distributions (Ortega and Aguillo ). The probability density function of session length can be expressed by Equation :

f (k; λ, f_{0}) = f_{0} \times e^{λ \times k}

where f 0 is the normalized initial probability of session length, and λ is the decay coefficient of the heavy‐tailed probability density curve.…”

Section: Help Workload For Wmts Performance Evaluationmentioning

confidence: 99%

“…The parameters of these probability distributions are studied and taken as inputs to guarantee accuracy of the generated workload. Many researchers have focused on Web workload characterization, including document size, transfer times, request arrivals, session length, think times (Barford et al 1999, Iyengar et al 1999, Busari and Williamson 2002, Latouche and Remiche 2002, Williams et al 2005, Ortega and Aguillo 2010.…”

Section: Workload Generation Approachesmentioning

confidence: 99%

Modeling Users' Behavior for Testing the Performance of a Web Map Tile Service

Guan

Cheng

Song

et al. 2014

Transactions in GIS

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Web Map Tile Services (WMTS) are widely used in many fields to quickly and efficiently visualize geospatial data for public use. To ensure that a WMTS can successfully fulfill users' expectations and requirements, the performance of a service must be measured to track latencies and bottlenecks that may downgrade the overall quality of service (QoS). Traditional synthetic workloads used to evaluate WMTS applications are usually generated by repeated static URLs, through randomized requests, or by an access log replay. These three methods do not take request characteristics and users' behaviors into consideration, while access logs are not available for systems still under development. Thus, the evaluation outcomes obtained by these methods cannot represent the real performance of online WMTS applications.In this article a new workload model named HELP (Hotspot/think-timE/Length/Path) is proposed to measure the performance of a prototype WMTS. This model describes how users browse a WMTS map and statistically characterizes complete map navigation behaviors. Then, the HELP model is implemented in HP LoadRunner and used to generate a synthetic workload to evaluate the target WMTS. Experimental results illustrate that the performance representation of the HELP workload is more accurate than that of the other two models, and how a bottleneck in the target system was identified. Additional statistical analysis of request logs and "hotspots" visualizations further validate the proposed HELP workload.

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“…Several works focussed on analysing the search skill and attitudes of the principal search engine users such as AltaVista (Silverstein et al 1998), Excite and Alltheweb (Jansen et al 2005) and Yahoo! Data mining was used for the identification of web sessions; to estimate their duration and their length in clicks (Pitkow et al 1997); to classify content according to the pages requested by their visitors (Wang and Zaiane 2002) and to show navigational differences between different points of access (Ortega and Aguillo 2010). Data mining was used for the identification of web sessions; to estimate their duration and their length in clicks (Pitkow et al 1997); to classify content according to the pages requested by their visitors (Wang and Zaiane 2002) and to show navigational differences between different points of access (Ortega and Aguillo 2010).…”

Section: The Present and The Future: The Possibilitiesmentioning

confidence: 99%