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Fay Chang

dblp:13/6463 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2008
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Distributed and cloud data management · 57% Database system architecture and tuning · 43%
Computer architecture, parallel and distributed computing, and storage systems
3 papers
Storage systems · 82% High-performance computing · 18%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 2 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
distributed storage
0.122008
Bigtable: A Distributed Storage System for Structured Data · ACM Trans. Comput. Syst. 2008
Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!) · OSDI 2006
High-performance computing › cluster computing
commodity cluster
0.012008
Bigtable: A Distributed Storage System for Structured Data · ACM Trans. Comput. Syst. 2008

Methods — techniques the papers use, named apart from their topics

tablet partitioning · 0.2sparse table data model · 0.2
YearPublicationVenuePosition
2008 Bigtable: A Distributed Storage System for Structured Data
abstract
Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Michael Burrows, Tushar Chandra, Andrew Fikes, Robert Gruber
ACM Trans. Comput. Syst.1
2006 Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!)
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Michael Burrows, Tushar Chandra, Andrew Fikes, Robert Gruber
OSDI1
2003 Operating System I/O Speculation: How Two Invocations Are Faster Than One
Keir Faser, Fay Chang
USENIX ATC, General Track2