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Dennis Fetterly
Researcher at Microsoft
Publications - 36
Citations - 6445
Dennis Fetterly is an academic researcher from Microsoft. The author has contributed to research in topics: Web page & Static web page. The author has an hindex of 21, co-authored 36 publications receiving 6275 citations. Previous affiliations of Dennis Fetterly include Hewlett-Packard & Google.
Papers
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Proceedings ArticleDOI
Dryad: distributed data-parallel programs from sequential building blocks
TL;DR: The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.
Proceedings ArticleDOI
DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language
Yuan Yu,Michael Isard,Dennis Fetterly,Mihai Budiu,Úlfar Erlingsson,Pradeep Kumar Gunda,Jon Currey +6 more
TL;DR: It is shown that excellent absolute performance can be attained--a general-purpose sort of 1012 Bytes of data executes in 319 seconds on a 240-computer, 960- disk cluster--as well as demonstrating near-linear scaling of execution time on representative applications as the authors vary the number of computers used for a job.
Proceedings ArticleDOI
Detecting spam web pages through content analysis
TL;DR: Some previously-undescribed techniques for automatically detecting spam pages are considered, and the effectiveness of these techniques in isolation and when aggregated using classification algorithms is examined.
Proceedings ArticleDOI
A large-scale study of the evolution of web pages
TL;DR: It is found that the average degree of change varies widely across top-level domains, and that larger pages change more often and more severely than smaller ones.
Proceedings ArticleDOI
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
TL;DR: This paper proposes that some spam web pages can be identified through statistical analysis, and examines a variety of properties, including linkage structure, page content, and page evolution, and finds that outliers in the statistical distribution of these properties are highly likely to be caused by web spam.