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ICCAD
2001
IEEE
192views Hardware» more  ICCAD 2001»
14 years 8 months ago
BOOM - A Heuristic Boolean Minimizer
We present a two-level Boolean minimization tool (BOOM) based on a new implicant generation paradigm. In contrast to all previous minimization methods, where the implicants are ge...
Jan Hlavicka, Petr Fiser
WEA
2005
Springer
138views Algorithms» more  WEA 2005»
14 years 4 months ago
A Practical Minimal Perfect Hashing Method
We propose a novel algorithm based on random graphs to construct minimal perfect hash functions h. For a set of n keys, our algorithm outputs h in expected time O(n). The evaluatio...
Fabiano C. Botelho, Yoshiharu Kohayakawa, Nivio Zi...
WWW
2007
ACM
14 years 11 months ago
GigaHash: scalable minimal perfect hashing for billions of urls
A minimal perfect function maps a static set of keys on to the range of integers {0,1,2, ... , - 1}. We present a scalable high performance algorithm based on random graphs for ...
Kumar Chellapilla, Anton Mityagin, Denis Xavier Ch...
PVLDB
2010
91views more  PVLDB 2010»
13 years 9 months ago
Regret-Minimizing Representative Databases
We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making. Like top-k, the k-regret query assumes that users h...
Danupon Nanongkai, Atish Das Sarma, Ashwin Lall, R...
ICALP
2009
Springer
14 years 11 months ago
Correlation Clustering Revisited: The "True" Cost of Error Minimization Problems
Correlation Clustering was defined by Bansal, Blum, and Chawla as the problem of clustering a set of elements based on a possibly inconsistent binary similarity function between e...
Nir Ailon, Edo Liberty