NDLI: Data Mining of Mass Storage Based on Cloud Computing

Content Provider	IEEE Xplore Digital Library
Author	Jianzong Wang Jiguang Wan Zhuo Liu Peng Wang
Copyright Year	2010
Abstract	Cloud computing is an elastic computing model that the users can lease the resources from the rentable infrastructure. Cloud computing is gaining popularity due to its lower cost, high reliability and huge availability. To utilize the powerful and huge capability of cloud computing, this paper is to import it into data mining and machine learning field. As one of the most influential and open competition in machine learning area, Netflix Prize attached with mass storage had driven thousands of teams across the world to attack the problem, among which the final winner was BellKor’s Pragmatic Chaos team, who bested Netflix’s own algorithm for predicting ratings by 10%. Their solution is an ensemble of a large number of models, each of which specializes in addressing a different aspect of the data. Among such different models, k-nearest neighbors (KNN) and Restricted Boltzmann Machine (RBM) are reported to be two most important and successful models. As a result, we build two predictors based on such two model respectively with the order to testify their performance based on cloud computing platforms. The results show that KNN can achieve root mean square deviation (rmse) with 0:9468 after the Global Effect (GE) data preprocessing, which is better than the Cinematch’s performance with rmse being 0:951. The rmse for RBM algorithm is about 0:9670 on the raw dataset, which can be further improved by KNN model.
Starting Page	426
Ending Page	431
File Size	178904
Page Count	6
File Format	PDF
ISBN	9781424493340
DOI	10.1109/GCC.2010.89
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Cloud computing Correlation Cloud Computing Computational modeling Neurons Motion pictures Data Mining Probes Mass Storage
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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