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Training Students to Extract Value from Big Data
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Data sets—whether in science and engineering, economics, health care, pub- lic policy, or business—have been growing rapidly; the recent National Research Council (NRC) report Frontiers in Massive Data Analysis documented the rise of “big data,” as systems are routinely returning terabytes, petabytes, or more of information (National Research Council, 2013). Big data has become pervasive because of the availability of high-throughput data collection technologies, such as information-sensing mobile devices, remote sensing, radiofrequency identification readers, Internet log records, and wireless sensor networks. Science, engineering, and business have rapidly transitioned from the longstanding state of striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human’s ability to examine, let alone absorb, it. Web companies—such as Yahoo, Google, and Amazon—commonly work with data sets that consist of billions of items, and they are likely to increase by an order of magnitude or more as the Internet of Things1 matures. In other words, the size and scale of data, which can be overwhelming today, are only increasing. In addition, data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.
978-0-309-31437-4
NONE
Information Technology
English
1-67
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