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Efficient 3-D Scene Prefetching From Learning user access patterns

IEEE TRANSACTIONS ON MULTIMEDIA 2015年第7期17卷 1081-1095页

作者： Zhou, Zhong Chen, Ke Zhang, Jingchang Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

Rendering large-scale 3-D scenes on a thin client is attracting increasing attention with the development of the mobile Internet. Efficient scene prefetching to provide timely data with a limited cache is one of the most critical issues for remote 3-D data scheduling in networked virtual environment applications. Existing prefetching schemes predict the future positions of each individual user based on user traces. In this paper, we investigate scene content sequences accessed by various users instead of user viewpoint traces and propose a user access pattern-based 3-D scene prefetching scheme. We make a relationship graph-based clustering to partition history user access sequences into several clusters and choose representative sequences from among these clusters as user access patterns. Then, these user access patterns are prioritized by their popularity and users' personal preference. Based on these access patterns, the proposed prefetching scheme predicts the scene contents that will most likely be visited in the future and delivers them to the client in advance. The experiment results demonstrate that our user access pattern-based prefetching approach achieves a high hit ratio and outperforms the prevailing prefetching schemes in terms of access latency and cache capacity.

关键词： 3-D scenes networked virtual environment prefetching user access patterns

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Music Recommendation Based on Acoustic Features and user access patterns

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2009年第8期17卷 1602-1611页

作者： Shao, Bo Wang, Dingding Li, Tao Ogihara, Mitsunori Florida Int Univ Sch Comp Sci Miami FL 33199 USA Univ Miami Dept Comp Sci Coral Gables FL 33146 USA

Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://*** demonstrate the effectiveness of the proposed approach.

关键词： Dynamic audio similarity music recommendation user access patterns

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Low-complexity fuzzy relational clustering algorithms for Web mining

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2001年第4期9卷 595-607页

作者： Krishnapuram, R Joshi, A Nasraoui, O Yi, LY Indian Inst Technol IBM India Res Lab New Delhi 110016 India Colorado Sch Mines Dept Math & Comp Sci Golden CO 80401 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Univ Memphis Dept Elect Engn Memphis TN 38152 USA BoldTech Syst Inc Denver CO 80202 USA

This paper presents new algorithms - fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd) - for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.

关键词： document clustering fuzzy relational clustering snippet clustering user access patterns user profiles Web mining

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Robots Still Outnumber Humans in Web Archives, But Less Than Before 26th

Robots Still Outnumber Humans in Web Archives, But Less Than...

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26th International Conference on Theory and Practice of Digital Libraries (TPDL)

作者： Jayanetti, Himarsha R. Garg, Kritika Alam, Sawood Nelson, Michael L. Weigle, Michele C. Old Dominion Univ Norfolk VA 23529 USA Internet Arch Wayback Machine San Francisco CA 94118 USA

ISBN: (纸本)9783031168024;9783031168017

To identify robots and humans and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012 and 2019, as well as ***'s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the two years of IA access logs (2012 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 is greater than in IA 2019 (21% more in requests and 18% more in sessions). Robots account for 98% of requests (97% of sessions) in *** (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.

关键词： Web archiving user access patterns Web server logs Web usage mining Web robot detection

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Extracting Usage patterns from Web Server Log 2

Extracting Usage Patterns from Web Server Log

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2nd International Conference on Green High Performance Computing (ICGHPC)

作者： Jeba, J. Monisha Privthy Bhuvaneswari, M. S. Muneeswaran, K. Mepco Schlenk Engn Coll Sivakasi India

ISBN: (纸本)9781467366151

Websites are the primary medium of any organization to communicate to their customers. Navigational usability and accessibility of the website are crucial to gain competitive advantage. Understanding how the customer uses the website can provide insight into their behavior. Web server logs contain latent information about usage behavior of customers. user sessions are a sequence of pages accessed by users for a specific period. The sessions are reconstructed from the web server logs. Simulated Annealing technique is used to enhance the process of identifying sessions. Considering the non-deterministic browsing behavior, soft clustering methods are used for assigning membership value for each session to belong to a cluster. A modified form of Fuzzy C-Means is used for clustering. The framework involves access log preprocessing, user identification, session identification and Mountain density function (MDF)based fuzzy clustering. The obtained clusters represent common navigational behavior among the users.

关键词： Web server logs user access patterns Simulated Annealing MDFCM

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access patterns for Robots and Humans in Web Archives 13

Access Patterns for Robots and Humans in Web Archives

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13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)

作者： AlNoamany, Yasmin Weigle, Michele C. Nelson, Michael L. Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781450320764

Although user access patterns on the live web are well-understood, there has been no corresponding study of how users, both humans and robots, access web archives. Based on samples from the Internet Archive's public Wayback Machine, we propose a set of basic usage patterns: Dip (a single access), Slide (the same page at different archive times), Dive (different pages at approximately the same archive time), and Skim (lists of what pages are archived, i.e., Time-Maps). Robots are limited almost exclusively to Dips and Skims, but human accesses are more varied between all four types. Robots outnumber humans 10:1 in terms of sessions, 5:4 in terms of raw HTTP accesses, and 4:1 in terms of megabytes transferred. Robots almost always access Time-Maps (95% of accesses), but humans predominately access the archived web pages themselves (82% of accesses). In terms of unique archived web pages, there is no overall preference for a particular time, but the recent past (within the last year) shows significant repeat accesses.

关键词： Web Archiving Web Server Logs Web Usage Mining user access patterns Web Robot Detection

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Clustering Fuzzy Web Transactions with Rough k-Means

Clustering Fuzzy Web Transactions with Rough <i>k</i>-Means

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International e-Conference on Advanced Science and Technology

作者： Shi, Peilin Taiyuan Univ Technol Dept Math Taiyuan Shanxi Peoples R China

ISBN: (纸本)9780769536729

Time duration and presence of a web page are two factors disclosing web users' interest. The time duration on a web page is characterized as a fuzzy linguistic variable because it is easily understandable for people and the subtle difference between two durations is disregarded. Thus a web access pattern is transformed as a fuzzy web access pattern, which is a fuzzy vector that are composed of n fuzzy linguistic variable or 0. Furthermore, the clusters in web access patterns do not necessarily have crisp boundaries. This paper proposes a modified k-means clustering algorithm based oil properties of rough set to group the gained fuzzy, web access patterns. Finally, an example is provided for clustering the given web access patterns. The results are proved to be effective.

关键词： clustering fuzzy variable rough set user access patterns

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Robots still outnumber humans in web archives in 2019, but less than in 2015 and 2012

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INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES 2024年第3期25卷 537-553页

作者： Jayanetti, Himarsha R. Garg, Kritika Alam, Sawood Nelson, Michael L. Weigle, Michele C. Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Internet Arch Wayback Machine San Francisco CA USA

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as ***'s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the three years of IA access logs (2012 vs. 2015 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 (91% of requests) and IA 2015 (88% of requests) is greater than in IA 2019 (70% of requests). Robots account for 98% of requests in *** (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012 and 2015, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.

关键词： Web archiving user access patterns Web server logs Web usage mining Web robot detection

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