ART2 Clustering of Multiple Web Objects for Qualitative Web Prefetching

ABSTRACT The web resources in the World Wide Web are rising, to large extent due to the services and applications provided by it. Because web traffic is large, gaining access to these resources incurs user-perceived latency. Although the latency can never be avoided, it can be minimized to a larger extent. Web prefetching is identified as a technique that anticipates the user’s future requests and fetches them into the cache prior to an explicit request made. Because web objects are of various types, a new algorithm is proposed that concentrates on prefetching embedded objects, including audio and video files. Further, clustering is employed using adaptive resonance theory (ART)2 in order to prefetch embedded objects as clusters. For comparative study, the web objects are clustered using ART2, ART1, and other statistical techniques. The clustering results confirm the supremacy of ART2 and, thereby, prefetching web objects in clusters is observed to produce a high hit rate.

[1]  S. Sudha,et al.  MePPM- Memory efficient prediction by partial match model for web prefetching , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[2]  Stephen Grossberg,et al.  Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world , 2013, Neural Networks.

[3]  José A. Gil,et al.  Evaluation, Analysis and Adaptation of Web Prefetching Techniques in Current Web , 2010 .

[4]  Torsten Kuhlen,et al.  Comparative analysis of fuzzy ART and ART-2A network clustering performance , 1998, IEEE Trans. Neural Networks.

[5]  Stephen Grossberg,et al.  Adaptive Resonance Theory , 2010, Encyclopedia of Machine Learning.

[6]  Josep Doménech i de Soria Evaluation, Analysis and adaptation of web prefetching techniques in current web , 2011 .

[7]  T. R. Gopalakrishnan Nair,et al.  Strategic Prefetching of VoD Programs Based on ART2 driven Request Clustering , 2011 .

[8]  S. Sitharama Iyengar,et al.  Adaptive neural network clustering of Web users , 2004, Computer.

[9]  Sueli Aparecida Mingoti,et al.  Comparing SOM neural network with Fuzzy c , 2006, Eur. J. Oper. Res..

[10]  Gholam Ali Montazer,et al.  Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process , 2012 .

[11]  Lee Luan Ling,et al.  A Neural Architecture Based on the Adaptive Resonant Theory and Recurrent Neural Networks , 2007, Int. J. Comput. Sci. Appl..

[12]  Gaurav Kumar,et al.  An hybrid clustering algorithm for optimal clusters in Wireless sensor networks , 2014, 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science.

[13]  George Pallis,et al.  A clustering-based prefetching scheme on a Web cache environment , 2008, Comput. Electr. Eng..

[14]  Gongzhu Hu,et al.  Web Object Prefetching: Approaches and a New Algorithm , 2010, 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[15]  Ana Pont,et al.  An Intelligent Technique for Controlling Web Prefetching Costs at the Server Side , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[16]  R. Venkatesan,et al.  Semantic Web Prefetching Scheme using Naïve Bayes Classifier , 2010, Int. J. Comput. Sci. Appl..

[17]  Ajay D. Kshemkalyani,et al.  Objective-optimal algorithms for long-term Web prefetching , 2006, IEEE Transactions on Computers.

[18]  Kam-yiu Lam,et al.  Temporal pre-fetching of dynamic web pages , 2006, Inf. Syst..

[19]  S. Sitharama Iyengar,et al.  Faster Web Page Allocation with Neural Networks , 2002, IEEE Internet Comput..

[20]  Manish Parashar,et al.  Optimizing Web Servers Using Page Rank Prefetching for Clustered Accesses , 2004, World Wide Web.

[21]  Athena Vakali,et al.  An Overview of Web Data Clustering Practices , 2004, EDBT Workshops.

[22]  Jeffrey C. Mogul,et al.  Using predictive prefetching to improve World Wide Web latency , 1996, CCRV.

[23]  Hong Jiang,et al.  A Novel Weighted-Graph-Based Grouping Algorithm for Metadata Prefetching , 2010, IEEE Transactions on Computers.

[24]  Marwan Krunz,et al.  Performance analysis of a client-side caching/prefetching system for Web traffic , 2007, Comput. Networks.