Intelligent Distributed Computing (Advances in Intelligent Systems and Computing, Volume 321)

Intelligent Distributed Computing (Advances in Intelligent Systems and Computing, Volume 321)

Language: English

Pages: 310

ISBN: 3319488287

Format: PDF / Kindle (mobi) / ePub

* Recent research in Intelligent Distributed Computing
* Carefully reviewed post-conference proceedings of the Third International Symposium on Intelligent Informatics (ISI'14) held in Delhi, India during September 24-27, 2014
* The papers are organized in topical sections on Intelligent Distributed Computing , data mining, clustering, multi agent systems, pattern recognition, and signal and image processing

This book contains a selection of refereed and revised papers of the Intelligent Distributed Computing Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers selected for this Track cover several Distributed Computing and related topics including Peer-to-Peer Networks, Cloud Computing, Mobile Clouds, Wireless Sensor Networks, and their applications.



















thereby making the network congestion free and improving the effective search time. 3.2 P-Skip Graph Operations The operations of the P-skip-graph are exactly the same as the traditional skip graph. There is no change in the existing algorithms. However, only the search algorithm needs to be modified for constructing the P-level. Since P-level is just another level in a node, only the maxlevel needs to be incremented by 1. 3.2.1 Modified Search Algorithm for P-Skip-Graph The algorithm is

optimistic approach in distributed database concurrency control. In: Proceedings of 5th International Conference on Computer Science and Information Technology (CSIT), pp. 71–75 (2013) 25. Simion, E., Basista, E., Canal, G., Ziadeh, K.: The Birthday paradox. Operational Research and Optimization (Master EESJI) (December 2012) 26. The Pigeonhole Principle. The Hong Kong University of Science and Technology, Department of Mathematics,

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describe about related work that has been done. Section 3 elaborates the proposed model for Recommendation System. Section 4 describes data set and results. Section 5 focuses on conclusion and future enhancement. Customization of Recommendation System Using Collaborative Filtering Algorithm 2 3 Related Work Zhi-Dan Zhao and Ming-Sheng Shang [12] have used based Collaborative Filtering using Hadoop as distributed framework. The approach is scalable but the response time taken for a single

increasing threshold value, probability of recommending correct item get increases because it has been liked by many users. Flow can be seen in fig. 3. 6 T. Senthil Kumar and S. Pandey Fig. 3 Combined Result 4 Experiment and Result 4.1 Dataset For experiment, we have used MovieLens dataset of size 1M. The dataset contains 10000054 ratings and 95580 tags applied to 10681 movies by 71567 users. There are three files, movies.dat, ratings.dat and tags.dat [24]. Ratings data file has atleast

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