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文件名称：Frontiers in Massive Data Analysis
文件大小：1.06MB
文件格式：PDF
更新时间：2018-01-31 14:53:03
bigdata Massive Data
Experiments, observations, and numerical simulations in many areas of science and business are currently generating terabytes of data, and in some cases are on the verge of generating petabytes and beyond. Analyses of the information contained in these data sets have already led to major breakthroughs in fields ranging from genomics to astronomy and high-energy physics and to the development of new information-based industries. Traditional methods of analysis have been based largely on the assumption that analysts can work with data within the confines of their own computing environment, but the growth of “big data” is changing that paradigm, especially in cases in which massive amounts of data are distributed across locations.
While the scientific community and the defense enterprise have long been leaders in generating and using large data sets, the emergence of e-commerce and massive search engines has led other sectors to confront the challenges of massive data. For example, Google, Yahoo!, Microsoft, and other Internet-based companies have data that is measured in exabytes (1018 bytes). Social media (e.g., Facebook, YouTube, Twitter) have exploded beyond anyone’s wildest imagination, and today some of these companies have hundreds of millions of users. Data mining of these massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. It is also transforming how we think about information storage and retrieval. 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.