San Francisco / Brisbane / Helsinki / Boston / New York / Washington / DC / Portland / Columbus / Vancouver / Prague / Miami / Lyon / /
Company
Neural Information Processing Systems / Google / Red Hat / MapReduce / Intel / using MapReduce / /
Country
France / United States / Canada / Australia / Finland / Czech Republic / /
Currency
USD / / /
Facility
Carnegie Mellon University / College Park / Michael Schatz University of Maryland / University of Maryland / /
IndustryTerm
protein interaction networks / protein-protein interaction networks / peta-scale graph mining system / large-scale hypertextual Web search engine / large shared-memory systems / graph processing / large web graph / Web Conference / information processing tasks / largescale graph processing / large-scale graph processing / web graph setting / parallel breadth-first search / graph algorithms / power law distributions / large-scale graph algorithms / remote network / iterative graph algorithms / dataintensive applications / web crawl / graph algorithm / data processing / real-world web graph / power law / web graph / social networks / computing / web graphs / social network / proteinprotein interaction networks / cloud computing / graph analysis algorithms / parallel breath-first search / power law distribution / text processing / search results / /
OperatingSystem
Red Hat Enterprise Linux / /
Organization
National Institute of Health / National Science Foundation / University of Maryland / IMC / University of Maryland / College Park / School of Computer Science / Carnegie Mellon University / /
Person
Kiri / Esther / Nima Asadi / Stanford InfoLab / /
Position
driver / first author / programmer / /
ProvinceOrState
Ohio / Maryland / Oregon / British Columbia / New York / California / Massachusetts / /
Design Patterns for Efficient Graph Algorithms in MapReduce Jimmy Lin and Michael Schatz University of Maryland, College Park {jimmylin,mschatz}@umd.edu