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Rethinking Data-Intensive Science Using Scalable Analytics Systems
2015
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15
Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines. In this paper, we describe ADAM, an example genomics pipeline that leverages the
doi:10.1145/2723372.2742787
dblp:conf/sigmod/NothaftMDZLYKAH15
fatcat:nokfli3y4fe6zi6avrluhncvau