Interactive Search and Exploration over Large Multidimensional Data

by Alexander Kalinin, Ugur Cetintemel and Stan Zdonik of Brown University In the Big Data era, professionals across scientific areas need efficient, interactive ad-hoc data analysis. Ideally, they need generic and reusable systems tools for interactive search, exploration and mining … Continue reading

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Analytic Monitoring for the Internet of Things

By Peter Bailis, Stanford Infolab, and Sam Madden, MIT CSAIL An increasing proportion of data today is generated by automated processes, sensors and devices—collectively called the Internet of Things (IoT).   Inexpensive hardware, widespread access to communication networks, and decreased … Continue reading

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Genomics Data, Analytics and the Future of Climate Change

By Vijay Gadepally, MIT CSAIL, in collaboration with the Chisholm Laboratory at MIT Meet Prochlorococcus marinus, a marine cyanobacterium that’s intricately linked to the global carbon cycle, widely present in seawater, and possibly holds secrets to future climate change. These … Continue reading

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Urban Analytics for Smart Cities: Connecting Data to People

By Kristin Tufte, Portland State University “A Smart City is one where data and technology improve people’s lives.¹” Governments, NGOs and academic researchers are looking to data and analytics to create more livable cities. Ideas and innovation are flowering. The … Continue reading

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Simplifying and Scaling Data Discovery

By Raul Castro Fernandez, MIT CSAIL People who need access to data for their jobs are spending more and more time searching for data of interest to the task at hand. This is particularly true for data-driven companies, where the … Continue reading

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Rethinking Streaming: Correct State Matters!

by Nesime Tatbul, Intel Labs and MIT CSAIL; Kristin Tufte, Portland State University; and Stan Zdonik, Brown University Stream processing has largely been thought of as real-time analytics. Data enters the system as streams and analytic functions (aggregates) are computed on the … Continue reading

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ModelDB: A System for Managing Machine Learning Models

By Manasi Vartak, Harihar Subramanyam, Wei-En Lee, Srinidhi Viswanathan, Saadiyah Husnoo, Sam Madden and Matei Zaharia, MIT CSAIL Building real-world machine learning (ML) algorithms is an iterative process. A data scientist will build many 10s to 100s of models before arriving … Continue reading

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Larger-than-Memory Data Management on Modern Storage Hardware for In-Memory OLTP Database Systems

By Lin Ma, Carnegie Mellon University; Joy Arulraj, Carnegie Mellon University; Sam Zhao, Brown University; Andrew Pavlo, Carnegie Mellon University; Subramanya R. Dulloor, Intel Labs; Michael J. Giardino, Georgia Institute of Technology; Jeff Parkhurst, Jason L. Gardner, Kshitij Doshi, Intel Labs; and Col. Stanley Zdonik, Brown … Continue reading

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TicToc: Time Traveling Optimistic Concurrency Control

By Xiangyao Yu, MIT CSAIL; Andrew Pavlo, Carnegie Mellon; Daniel Sanchez and Srinivas Devadas, MIT CSAIL The TicToc algorithm enables scalable and high-performing concurrency control for future multi- and many-core systems. Large-scale, highly parallel transaction processing systems can be built with TicToc. We … Continue reading

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PolyPEG: A Proposal for Polystore Optimization

By Dylan Hutchison, Bill Howe, Dan Suciu, and Zachary Tatlock, University of Washington There has been a “cambrian explosion” of systems and languages for large-scale data analytics:  Postgres and H-Store accept SQL queries; Datomic and Myria accept Datalog; SciDB accepts … Continue reading

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