Data Systems Group at MIT

MIT, Brown Develop Interactive Machine Learning Tool for Analytics July 2019

July 01, 2019 – Researchers from MIT and Brown University have developed an interactive machine learning tool that lets anyone, from data scientists to small business owners, use analytics to solve real-world issues. The interactive system, called Northstar, runs in the cloud but has an interface that supports any touchscreen device, from smartphones to large interactive whiteboards. Users can…

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Why Is Enterprise Data Integration So Challenging? June 2019

A.M. Turing Award Laureate and database technology pioneer Michael Stonebraker delivered a welcome keynote at Data Summit 2019, titled “Big Data, Technological Disruption, and the 800-Pound Gorilla in the Corner.”

In his presentation, Stonebraker—who is an MIT Adjunct Professor and Tamr co-founder—offered his views on many of the thorny big data challenges facing enterprises today, the established and newer technologies available to address these issues, and the intractable problem that remains the 800-pound gorilla in the room.

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Drag-and-drop data analytics June 2019

System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more. http://news.mit.edu/2019/drag-drop-data-analytics-0627

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Michael Stonebraker on Big Data Upheaval and the 800-Pound Gorilla in the Room May 2019

A.M. Turing Award Laureate and database technology pioneer Michael Stonebraker delivered the welcome keynote at Data Summit 2019, titled “Big Data, Technological Disruption, and the 800-Pound Gorilla in the Corner.” http://www.dbta.com/Editorial/News-Flashes/Michael-Stonebraker-on-Big-Data-Upheaval-and-the-800-Pound-Gorilla-in-the-Room-131909.aspx

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Google, Intel and Microsoft team up with CSAIL on new data-driven initiative October 2018

Data Systems and AI Lab (DSAIL) will focus on using machine learning to improve data systems, and vice versa Recent years have seen an explosion in the creation of machine learning models for everything from self-driving cars to social media feeds. Despite the success of these models at perception and simple prediction, they have yet…

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Chief data officer skills tested by AI tech blitz – August 2018

A reporter’s notebook from a recent MIT symposium provides insights on chief data officer needs, as the AI wave starts to hit CDOs and affects them more than most other tech trends.

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A new way to automatically build road maps from aerial images – April 2018

“RoadTracer” system from the Computer Science and Artificial Intelligence Laboratory could reduce workload for developers of apps like Google Maps. http://news.mit.edu/2018/new-way-to-automatically-build-road-maps-with-aerial-images-0417

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The case for learned index structures – Part II 1/9/18

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data…

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The case for learned index structures – part I 1/8/18

Whenever efficient data access is needed, index structures are the answer, and a wide variety of choices exist to address the different needs of various access patterns. For example, B-Trees are the best choice for range requests (e.g., retrieve all records in a certain time frame); Hash-maps are hard to beat in performance for single…

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How machine learning will accelerate data management systems – December 2017

In this episode of the Data Show, I spoke with Tim Kraska, associate professor of computer science at MIT. To take advantage of big data, we need scalable, fast, and efficient data management systems. Database administrators and users often find themselves tasked with building index structures (“indexes” in database parlance), which are needed to speed up data…

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