Tag Archives: OLTP

Solving the “One Concurrency Control Does Not Fit All” Problem for OLTP Databases

By Dixin Tang and Aaron J. Elmore, University of Chicago In this post, we present a new transactional database system that adaptively changes data organization and concurrency control protocols in response to workload changes. With the increasing memory sizes of … Continue reading

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Write-Behind Logging

By Joy Arulraj, Matthew Perron, and Andrew Pavlo, Carnegie Mellon University In a joint collaboration between Carnegie Mellon University and Intel Labs, we explore the changes required in the logging and recovery algorithms in non-volatile memory database management systems (DBMSs). The results of this work … 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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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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The End of Slow Networks: It’s Time for a Redesign

by Tim Kraska and Carsten Binnig, Brown University Data Management Research Group The next generation of high-performance RDMA-capable networks requires a fundamental rethinking of the design of modern distributed database systems (DDBMS). Current distributed databases are commonly designed under the … Continue reading

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2015: Momentum, Moments and Memories

Greetings of the season from the Intel Science and Technology Center for Big Data.  As 2015 comes to a close, we thought we would share some moments and memories that were captured here in the ISTC for Big Data blog … Continue reading

Posted in Analytics, Big Data Applications, Big Data Architecture, Data Management, Databases and Analytics, DBMS, High-Performance Computing, ISTC for Big Data Blog, Storage, Streaming Big Data, Visualizing Big Data | Tagged , , , , , , , , , , , , , , , , , , , | Leave a comment

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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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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Research Updates from the ISTC for Big Data

In August, researchers from Intel and participating institutions gathered at the Intel Science and Technology Center for Big Data’s annual Research Retreat at Intel’s Jones Farm campus in Hillsboro, Oregon to present their latest work and describe their progress. Here … Continue reading

Posted in Benchmarks, Big Data Architecture, DBMS, Graph Computation, ISTC for Big Data Blog, Storage | Tagged , , , , , | Leave a comment