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عنوان فارسی مقاله:
علوم و کلان داده: افسانه و حقیقت
عنوان انگلیسی مقاله:
Big Data and Science: Myths and Reality
سال انتشار : 2015
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بخشی از مقاله انگلیسی:
Even if we are consciously thinking about Big Data in terms of the 4Vs, we immediately have a question of determining what the thresholds are to call something “Big”. For Variety and Veracity we know this is not even an answerable question, because we do not have measures in the first place. So let us just consider Volume and Velocity. The threshold, for some people, is at the limit of what we know how to handle. Obviously, this is a moving target. But it has the advantage of being inspirational. The fatal (in my opinion) drawback is that it limits the size of the market to 1: there is only one largest deployment in the world at any time (barring ties). Increasing the size of this deployment is definitely a worthwhile challenge, but not one that an entire industry can be built around and an entire academic field developed. The threshold, in some definitions, then becomes fixed, based on the dominant architecture at some point in time, say 2010. So a data set qualifies, in terms of Volume, as Big Data if it is larger than can be handled using the “standard” architectures in use at the beginning of the Big Data era. With the ever-growing popularity of Map-Reduce style computation, and the plethora of systems and tools in “the Big Data eco-system,” we then have a definition that is specific, even if it is both circular and self-serving: a Big Data problem is one that is best addressed using elements drawn from the Big Data “toolbox.” This definition is specific because there is general agreement about what tools are in the Big Data toolbox: most tool producers self categorize themselves appropriately. The definition is circular because it really does not define what goes into the toolbox. If we did not have an explicit listing, we would be defining a Big Data tool as a software system that addresses at least some aspects of a Big Data problem, or some such similar statement. The definition is self-serving because it anoints a set of tools and a style of system architecture as “the solution” to the Big Data problem. This definition is wrong because almost everything in the Big Data toolbox is focused on Volume (frequently in conjunction with Velocity), with very little consideration given to Variety and Veracity challenges. I believe that the cloud, and what is today considered the “Big Data Ecosystem,” has its place in the constellation of relevant technologies, but is neither a complete solution in itself nor a required piece of every solution.
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کلمات کلیدی:
Science's Big Data Problem | WIRED https://www.wired.com/insights/2014/08/sciences-big-data-problem/ Yet in this age of big data, science has a big problem: it is not doing nearly enough to encourage and enable the sharing, analysis and interpretation of the vast ... What Is Big Data? - Blog https://datascience.berkeley.edu/what-is-big-data Sep 3, 2014 - A commonly repeated definition of big data cites the three Vs: volume, velocity, and variety. ... Founder and CTO, Silicon Valley Data Science. BDU - Free Data Science and Big Data Courses https://bigdatauniversity.com/ Take your Data Science and Big Data career to the next level with our free data analytics courses. Big Data and Science Studies - The Center for Science and Society at ... scienceandsociety.columbia.edu/research/research.../big-data-and-science-studies/ RESEARCH CLUSTER: Big Data and Science Studies. LED BY: James R. Barker Professor of Contemporary Civilization Matthew Jones, Columbia University ... Cambridge Big Data www.bigdata.cam.ac.uk/ Big Data research at Cambridge brings together fundamental research into data analysis methods with applications in science, medicine, technology and ... Big data - Wikipedia https://en.wikipedia.org/wiki/Big_data Jump to Science - The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are ... Searches related to Big Data and Science masters in data science in europe masters in data science in germany masters in data analytics in europe masters in data analytics in germany masters in big data canada masters in data science switzerland big data masters uk big data analytics examples
