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ICDE
2009
IEEE
170views Database» more  ICDE 2009»
10 years 4 months ago
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
Abstract-- When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring func...
Graham Cormode, Feifei Li, Ke Yi
ICDE
2010
IEEE
254views Database» more  ICDE 2010»
10 years 2 months ago
Efficient Rank Based KNN Query Processing Over Uncertain Data
Uncertain data are inherent in many applications such as environmental surveillance and quantitative economics research. As an important problem in many applications, KNN query has...
Ying Zhang, Xuemin Lin, Gaoping ZHU, Wenjie Zhang,...
ICML
2009
IEEE
10 years 3 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
SIGMOD
2009
ACM
175views Database» more  SIGMOD 2009»
10 years 2 months ago
Ranking distributed probabilistic data
Ranking queries are essential tools to process large amounts of probabilistic data that encode exponentially many possible deterministic instances. In many applications where unce...
Feifei Li, Ke Yi, Jeffrey Jestes
VLDB
2004
ACM
129views Database» more  VLDB 2004»
9 years 7 months ago
Probabilistic Ranking of Database Query Results
We investigate the problem of ranking answers to a database query when many tuples are returned. We adapt and apply principles of probabilistic models from Information Retrieval f...
Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, ...
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