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Shih-Hao Li

dblp:64/792 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2014
—ORCID · none

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

Databases, data management, data science and information retrieval · 2 · 2 first-authorComputer networks · 1

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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Computer networks
2 papers
Software-defined and programmable networks · 76% Network management and operations · 24%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › multi-tenancy
multi-tenant datacenter
0.212014
Network Virtualization in Multi-tenant Datacenters · NSDI 2014
Cloud and datacenter computing › virtualization
network virtualization
0.212014
Network Virtualization in Multi-tenant Datacenters · NSDI 2014
Software-defined and programmable networks
network virtualization
0.112014
Network Virtualization in Multi-tenant Datacenters · NSDI 2014
Information retrieval › retrieval models
boolean retrieval
0.011997
Boolean Similarity Measures for Resource Discovery · IEEE Trans. Knowl. Data Eng. 1997
Information retrieval
retrieval models
0.011997
Boolean Similarity Measures for Resource Discovery · IEEE Trans. Knowl. Data Eng. 1997
Network management and operations › network resource management
resource discovery
0.011997
Boolean Similarity Measures for Resource Discovery · IEEE Trans. Knowl. Data Eng. 1997
Information retrieval
ranking
0.011997
Boolean Similarity Measures for Resource Discovery · IEEE Trans. Knowl. Data Eng. 1997

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

boolean similarity measures · 0.0
YearPublicationVenuePosition
2014 Network Virtualization in Multi-tenant Datacenters
Teemu Koponen, Keith Amidon, Peter Balland, Martín Casado, Anupam Chanda, Bryan Fulton, Igor Ganichev, Jesse Gross, Paul Ingram, Ethan J. Jackson, Andrew Lambeth, Romain Lenglet, Shih-Hao Li, Amar Padmanabhan, Justin Pettit, Ben Pfaff, Rajiv Ramanathan, Scott Shenker, Alan Shieh, Jeremy Stribling, Pankaj Thakkar, Dan Wendlandt, Alexander Yip
NSDI13
1997 Boolean Similarity Measures for Resource Discovery
abstract
As the number of Internet servers increases rapidly, it becomes difficult to determine the relevant servers when searching for information. The authors develop a new method to rank Internet servers for Boolean queries. Their method reduces time and space complexity from exponential to polynomial in the number of Boolean terms. They contrast it with other known methods and describe its implementation.
Shih-Hao Li, Peter B. Danzig
IEEE Trans. Knowl. Data Eng.1
1996 Precision and Recall of Ranking Information-Filtering Systems
Shih-Hao Li, Peter B. Danzig
J. Intell. Inf. Syst.1