Frontiers in massive data analysis
著者
書誌事項
Frontiers in massive data analysis
The National Academies Press, 2013
- : pbk
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
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  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data.
Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Table of Contents
Front Matter
Summary
1 Introduction
2 Massive Data in Science, Technology, Commerce, National Defense,
Telecommunications, and Other Endeavors
3 Scaling the Infrastructure for Data Management
4 Temporal Data and Real-Time Algorithms
5 Large-Scale Data Representations
6 Resources, Trade-offs, and Limitations
7 Building Models from Massive Data
8 Sampling and Massive Data
9 Human Interaction with Data
10 The Seven Computational Giants of Massive Data Analysis
11 Conclusions
Appendixes
Appendix A: Acronyms
Appendix B: Biographical Sketches of Committee Members
目次
- 1 Front Matter
- 2 Summary
- 3 1 Introduction
- 4 2 Massive Data in Science, Technology, Commerce, National Defense, Telecommunications, and Other Endeavors
- 5 3 Scaling the Infrastructure for Data Management
- 6 4 Temporal Data and Real-Time Algorithms
- 7 5 Large-Scale Data Representations
- 8 6 Resources, Trade-offs, and Limitations
- 9 7 Building Models from Massive Data
- 10 8 Sampling and Massive Data
- 11 9 Human Interaction with Data
- 12 10 The Seven Computational Giants of Massive Data Analysis
- 13 11 Conclusions
- 14 Appendixes
- 15 Appendix A: Acronyms
- 16 Appendix B: Biographical Sketches of Committee Members
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