Big Data promises a better world. A world where data will be used to make better decisions, from how we invest money to how we manage our healthcare to how we educate our children and manage our cities and resources. These changes are enabled by a proliferation of new technologies and tools that...
The MIT Big Data Challenge
Take me to the
CITY OF BOSTON TRANSPORTATION challenge page!
OVERVIEW
The MIT Big Data Initiative at CSAIL is organizing competitions designed to spur innovation in how we think about and use data to address major societal issues. Big Data promises a better...
Take me to the
CITY OF BOSTON TRANSPORTATION challenge page!
OVERVIEW
The MIT Big Data Initiative at CSAIL is organizing competitions designed to spur innovation in how we think about and use data to address major societal issues. Big Data promises a better...
MapD (Massively Parallel Database) is an analytics database being built by Todd Mostak and Prof. Sam Madden at MIT that allows interactive querying of big datasets.
It takes advantage of the immense computational power and memory bandwidth available in commodity-level, of-the-shelf multiocore...
It takes advantage of the immense computational power and memory bandwidth available in commodity-level, of-the-shelf multiocore...
MIT Big Data Challenge - Transportation in the City of Boston [Nov 12, 2013 - Jan 20, 2014]
FINAL CHALLENGE EVENT: FEB 5, 2014 5:00-7:00pm
Guest Speaker: Nigel Jacob, City of Boston
Register Here to Attend the Final Event!
Featured at the MIT Big Data Initiative Annual Meeting Nov 2013 - “Language, Robots, Babies, TV, Twitter and Big Data” by Deb Roy, Associate Professor MIT Media Lab and Chief Media Scientist, Twitter
The goal of the MIT Big Data Initiative, a multi-year effort launched in May 2012, is to identify and develop new technologies needed to solve next generation data challenges that will require the ability to scale well beyond what today's computing platforms, algorithms, and methods can provide. We want to enable people to leverage Big Data by developing systems and platforms that are reusable and scalable across multiple application domains.
Our approach includes two important aspects. First, we will work closely with key industry and government stakeholders to provide real-world applications and drive impact. Promoting in-depth interactions between academic researchers, industry and government is a key goal. Second, we believe the solution to Big Data is fundamentally multi-disciplinary. The team includes faculty and researchers hailing from diverse research backgrounds, including algorithms, architecture, data management, machine learning, privacy and security, user interfaces, and visualization, as well as domain experts in finance, industrial, medical, smart infrastructure, education and science.
