Tag Archives: Storage

NVMRocks: RocksDB on Non-Volatile Memory Systems

By Jianhong Li (CMU), Andrew Pavlo (CMU), and Siying Dong (Facebook) Non-volatile memory (NVM) has been a game-changing memory technology. In contrast to traditional block-based durable storage devices, it provides low latency comparable to DRAM and byte-addressability. Although NVM is … 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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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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Decibel: Dataset Branching for Collaborative Data Management

by Michael Maddox, MIT CSAIL, and Aaron J. Elmore, University of Chicago* In Big Data’s wake has come demand for tools to curate, manage and analyze shared datasets collaboratively. For instance, consider researchers in a social media company concurrently working … 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

Interface Sharing between Data Storage and Analytics

By Jack Dongarra, Piotr Luszczek and Thomas Herault of the University of Tennessee Innovative Computing Laboratory It is trite to say that traditional RDBMS optimize the data movement by bringing the query close to the data and not the other way around. … Continue reading

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ISTC to Unveil New Big Data Federation Architecture at VLDB 2015

At the upcoming Very Large Data Bases 2015 Conference in Hawaii, the Intel Science and Technology Center for Big Data will unveil its first big “capstone” project: a federated reference architecture that enables query processing over multiple databases, where each … Continue reading

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Cutting the Cost and Power Consumption of Big Data Applications, in a Flash

It’s possible to cut the cost and power consumption of Big Data applications by using flash memory as a replacement for conventional RAM, according to new research from ISTC Principal Investigator Arvind and fellow researchers from MIT CSAIL, MIT, and Quanta Research … Continue reading

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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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Optimizing Transaction Processing for In-Memory Databases on Large Multicore Machines

By Stephen Tu, MIT CSAIL When people think of “big data” storage systems, they generally think of huge clusters with hundreds of machines working together to serve a large distributed datastore. To this end, lots of people in both academia … Continue reading

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