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Big Data and Its Technical Challenges

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In a broad range of application areas, data is being collected at an unprecedented scale. Decisions that previously were based on guesswork, or on painstakingly handcrafted models of reality, can now be made using data-driven mathematical models. Such Big Data analysis now drives nearly every aspect of society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences.

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As an example, consider scientific research, which has been revolutionized by Big Data.1,12 The Sloan Digital Sky Survey23 has transformed astronomy from a field where taking pictures of the sky was a large part of an astronomer's job to one where the pictures are already in a database, and the astronomer's task is to find interesting objects and phenomena using the database. In the biological sciences, there is now a well-established tradition of depositing scientific data into a public repository, and also of creating public databases for use by other scientists. Furthermore, as technology advances, particularly with the advent of Next Generation Sequencing (NGS), the size and number of experimental datasets available is increasing exponentially.13


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