SmartMiner: A New Framework for Mining Large Scale Web Usage Data

author: Murat Ali Bayir, Department of Computer Science and Engineering, University at Buffalo
author: Ismail Hakki Toroslu, Department of Computer Engineering, Middle East Technical University
author: Ahmet Cosar, Department of Computer Engineering, Middle East Technical University
author: Guven Fidan, Agmlab Information Technologies
published: May 20, 2009,   recorded: April 2009,   views: 5287
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Description

In this paper, we propose a novel framework called Smart- Miner for web usage mining problem which uses link information for producing accurate user sessions and frequent navigation patterns. Unlike the simple session concepts in the time and navigation based approaches, where sessions are sequences of web pages requested from the server or viewed in the browser, in Smart-Miner sessions are set of paths traversed in the web graph that corresponds to users' navigations among web pages. We have modeled session reconstruction as a new graph problem and utilized a new algorithm, Smart-SRA, to solve this problem efficiently. For the pattern discovery phase, we have developed an efficient version of the Apriori-All technique which uses the structure of web graph to increase the performance. From the experiments that we have performed on both real and simulated data, we have observed that Smart-Miner produces at least 30%more accurate web usage patterns than other approaches including previous session construction methods. We have also studied the effect of having the referrer information in the web server logs to show that different versions of Smart-SRA produce similar results. Another novel work is that we have implemented distributed version of the Smart Miner framework by employing Map-Reduce paradigm which enables processing huge size web server logs belonging to multiple web sites. To the best of our knowledge this paper is the first attempt to propose such large scale framework forweb usage mining problem. We conclude that we can efficiently process terabytes of web server logs belonging to multiple web sites by employing our scalable framework.

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