Tag Archives: Intel Labs

The Big Data ISTC: A Retrospection by Michael Stonebraker, Samuel Madden and Timothy Mattson

The Big Data ISTC is a research project sponsored by Intel that ran for five years (August 2012- August 2017).  This blog post highlights some of the accomplishments and lessons learned during this period. Big data is usually categorized into … Continue reading

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

ISTC Researchers to Present Papers at VLDB 2017 Conference

ISTC for Big Data principal Investigators, researchers, and their students will present a number of papers at the 2017 International Conference on Very Large Databases (VLDB 2017) in Munich, Germany, August 28-September 1, 2017.  (To read any of the papers, … Continue reading

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ISTC Releases Open Source Code for S-Store Transactional Streaming System

By John Meehan and Stan Zdonik, Brown University & Nesime Tatbul, Intel Labs and MIT Today, the ISTC for Big Data released the first version of our S-Store transactional stream processing system. S-Store is open-source software and available for download … Continue reading

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Intel and the ISTC for Big Data (2012-2017): A Powerful Collaboration

By Jeff Parkhurst, Ph.D. and Timothy G. Mattson, Ph.D., Intel  The year 2012 was arguably the year that Big Data went mainstream. Data was being hailed as a new class of economic asset, similar to currency or gold, from the … Continue reading

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TileDB and GenomicsDB Now Available in Open Source

by Stavros Papadopoulos, Intel Parallel Computing Lab The ISTC for Big Data has recently released TileDB, a novel efficient data management system for scientific data, such as graphs, DNA sequences, matrices, maps, and imaging. We have also released an adaptation of TileDB … Continue reading

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Let’s Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems

By Joy Arulraj and Andrew Pavlo, Carnegie Mellon; and Subramanya Dulloor, Intel Labs In a joint collaboration between Carnegie Mellon University and Intel Labs, we explore the changes required in future database management systems to fully leverage the unique set of characteristics of non-volatile memory (NVM) technologies. … Continue reading

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Academic Collaboration: Accelerating the Practical Application of Research

By Jeff Parkhurst, Ph.D., Intel Labs The Intel Science and Technology Center Program is a series of research collaborations that pairs Intel with U.S. universities to identify and prototype revolutionary technology opportunities. The centers are designed to encourage closer collaboration … Continue reading

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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

S-Store: A Big-Velocity Database System

By John Meehan, PhD Candidate, Brown University At the 2014 Intel Science and Technology Center for Big Data annual Research Retreat, Nesime Tatbul of Intel Labs and MIT provided an update on S-Store. Background Recently, the architectures of transactional processing DBMSs … Continue reading

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Building a New Application-to-Hardware Management Stack for Big Data

The Intel Science and Technology Center for Big Data partners – Intel Labs and its seven participating academic research institutions – recently unanimously agreed to focus their research on a pioneering effort: creating a new application-to-hardware big-data management stack. Unique in industry … Continue reading

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