Tag Archives: Algorithms

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

Posted in Big Data Architecture, Data Management, DBMS, ISTC for Big Data Blog, Math and Algorithms, Storage | Tagged , , , , , , , | Leave a comment

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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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 for Big Data Researchers Present Work at NEDB Day 2015

ISTC for Big Data principal investigators (PIs) and researchers presented a broad base of research work at New England Database Day 2015, which was sponsored by Microsoft and held at the Stata Center at MIT in Cambridge, Mass., on Friday, … Continue reading

Posted in Big Data Applications, Big Data Architecture, Computer Architecture, Data Management, DBMS, Graph Computation, ISTC for Big Data Blog, Math and Algorithms, Query Engines, Streaming Big Data | Tagged , , , , , , , , , | Leave a comment

Graph Analytics: The New Use Case for Relational Databases

ByAlekh Jindal, MIT CSAIL Graph analytics are becoming increasingly popular with several new big-data application domains such as social networks, transportation networks, ad networks, e-commerce, and web search. However, these graph analytics workloads are seen as quite different from traditional database … Continue reading

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Fast Data Analysis with SVD

By Jack Dongarra, University of Tennessee Knoxville and Innovative Computing Laboratory The GenBase benchmark was developed as a collaboration with the Intel Parallel Computing Lab, the Broad Institute and Novartis, and the MIT Database Group. Among many challenging tests that the benchmark includes is a computation of the Singular Value … Continue reading

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Benchmarking Graph Databases – Updates

By Alekh Jindal, MIT CSAIL After our last post about benchmarking graph databases, Neo Technology representatives contacted us and said that they repeated the shortest path queries on Neo4j with the Facebook dataset and they observed 2-4 orders of magnitude better … Continue reading

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Approximate Analytics: Keeping Pace with Big Data using Parallel Locality Sensitive Hashing

By Narayanan Sundaram and Nadathur Satish, Intel Parallel Computing Lab We tend to think of big data problems as those involving finding a needle in a haystack. However, many big data problems tend to be those estimating the shape or … Continue reading

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Benchmarking Graph Databases

ByAlekh Jindal, MIT CSAIL Graph data management has recently received a lot of attention, particularly with the explosion of social media and other complex, inter-dependent datasets. As a result, a number of graph data management systems have been proposed. But … Continue reading

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Accelerated Linear Algebra on Big Data

By Jack Dongarra, University of Tennessee Knoxville and Innovative Computing Laboratory Often with Big Data come massive amounts of computations. For example, gene correlations may be analyzed with the Singular Value Decomposition as it is done in the GenMark benchmark. The SVD algorithm is a robust method that … Continue reading

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