Tag Archives: Software

ISTC Releases Open Source Code for BigDAWG Polystore System

By Dr. Tim Mattson, Intel and Dr. Vijay Gadepally and Kyle O’Brien, MIT Lincoln Laboratory Today, the ISTC for Big Data released the first version of BigDAWG, our polystore system for simplifying integration and analytics of disparate data at scale. BigDAWG is … Continue reading

Posted in Analytics, Big Data Architecture, Data Management, Databases and Analytics, DBMS, ISTC for Big Data Blog, Polystores, Query Engines, Storage | Tagged , , , , , , , , | Leave a comment

The Changing Landscape of Data Systems

By Anant Bhardwaj, MIT CSAIL* Everyone continues to chase “Big Data” ─ from businesses to start-up companies to academic researchers. To understand better the changing landscape of data systems, I spent the last few months trying to learn about every data … Continue reading

Posted in Big Data Applications, Big Data Architecture, Data Management, ISTC for Big Data Blog, Tools for Big Data | Tagged , , , , | Leave a comment

ISTC for Big Data: 2015 and Beyond

By Sam Madden, ISTC for Big Data Co-Director Happy New Year, ISTC for Big Data blog readers! It’s been a busy year here at the ISTC for Big Data.  In this post, I call out a few of our highlights … Continue reading

Posted in Analytics, Big Data Applications, Big Data Architecture, Data Management, Databases and Analytics, DBMS, Graph Computation, ISTC for Big Data Blog, Math and Algorithms, Streaming Big Data, Visualizing Big Data | Tagged , , , , , , , , , , , , | Leave a comment

A Scalable, Multi-Access Flash Store for Big Data Analytics

Sang Woo Jun, Ming Liu and Arvind, MIT CSAIL Modern data-driven analytics often deal with datasets that do not fit in the main memory of the processing system. In such a scenario, the limiting factor in performance is usually the transportation … Continue reading

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Accelerated Linear Algebra on Big Data

By Jack Dongarra, University of Tennessee Knoxville and Innovative Computing Laboratory Often with Big Data come massive amounts of computations. For example, gene correlations may be analyzed with the Singular Value Decomposition as it is done in the GenMark benchmark. The SVD algorithm is a robust method that … Continue reading

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