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30 results on '"Toyoda, Masashi"'

10. Analysis of the term ‘big data’: Usage in biomedical publications

Author

van Altena, A. J., Moerland, P. D., Zwinderman, A. H., Olabarriaga, S. D., Obradovic, Zoran, Baeza-Yates, Ricardo, Kepner, Jeremy, Nambiar, Raghunath, Wang, Chonggang, Toyoda, Masashi, Suzumura, Toyotaro, Hu, Xiaohua, Cuzzocrea, Alfredo, Tang, Jian, Zang, Hui, Nie, Jian-Yun, Ghosh, Rumi, Epidemiology and Data Science, APH - Methodology, and APH - Personalized Medicine

Subjects
business.industry, Computer science, 05 social sciences, Big data, Subject (documents), Data science, Term (time), 03 medical and health sciences, Market research, 0302 clinical medicine, Text mining, 030212 general & internal medicine, Meaning (existential), 0509 other social sciences, 050904 information & library sciences, business
Abstract

The meaning of the term 'Big Data' is still subject to debate, in spite of being widely used in biomedical publications. This confusion in definition leads to missed opportunities for peers to exchange knowledge and practices. A better understanding of Big Data may help researchers to identify themselves with the Big Data community. In this study, we investigate the most distinguishing features in 'Big Data'-labelled publications by comparing them against publication without this label (non-Big Data), using text mining and machine learning methods. Furthermore, the usage of the term Big Data was analysed over time. Our models could successfully make a distinction between publications labelled with 'Big Data' and those without. The most distinguishing features consisted of terms such as 'omics', 'computing', 'storage', and 'mining'. We observed that publications that do not use the term Big Data may also address topics that fall under well-accepted definitions of Big Data. Trends suggest that, while the use of the term Big Data increased, it is used less reliably now as compared to earlier years.

Published
2017
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29. The History of Web Archiving.

Author

Toyoda, Masashi and Kitsuregawa, Masaru

Subjects
WORLD Wide Web, INTERNETWORKING, INFORMATION technology, INFORMATION resources management, DATA protection
Abstract

This paper describes the history and the current challenges of archiving massive and extremely diverse amounts of user-generated data in an international environment on the World Wide Web and the technologies required for interoperability between service providers and for preserving their contents in the future. [ABSTRACT FROM PUBLISHER]

Published
2012
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30. Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data

Author

Hannu Reittu, Ilkka Norros, Fulop Bazso, Obradovic, Zoran, Baeza-Yates, Ricardo, Kepner, Jeremy, Nambiar, Raghunath, Wang, Chonggang, Toyoda, Masashi, Suzumura, Toyotaro, Hu, Xiaohua, Cuzzocrea, Alfredo, Tang, Jian, Zang, Hui, Nie, Jian-Yun, and Ghosh, Rumi

Subjects
FOS: Computer and information sciences, Power graph analysis, sampling, Theoretical computer science, Computer science, Computer Science - Information Theory, 02 engineering and technology, 01 natural sciences, Matrix decomposition, Matrix (mathematics), big data, Robustness (computer science), 020204 information systems, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Symmetric matrix, 010306 general physics, Sparse matrix, ta113, ta213, Information Theory (cs.IT), regular decomposition, Missing data, Graph, graph analysis, Visualization, Scalability
Abstract

A method for compression of large graphs and matrices to a block structure is further developed. Szemer\'edi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization., Comment: Accepted for publication in: Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, December 11, 2017, Bosto U.S.A

Published
2017
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