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» Ranking queries on uncertain data
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ICDE
2005
IEEE
133views Database» more  ICDE 2005»
14 years 9 months ago
Efficient Inverted Lists and Query Algorithms for Structured Value Ranking in Update-Intensive Relational Databases
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). SVR uses structured data values to score (rank) the results of keyword search que...
Lin Guo, Jayavel Shanmugasundaram, Kevin S. Beyer,...
BTW
2007
Springer
131views Database» more  BTW 2007»
13 years 11 months ago
TopX - Efficient and Versatile Top-k Query Process-ing for Text, Semistructured, and Structured Data
: This paper presents a comprehensive overview of the TopX search engine, an extensive framework for unified indexing and querying large collections of unstructured, semistructured...
Martin Theobald, Ralf Schenkel, Gerhard Weikum
ICML
2009
IEEE
14 years 8 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
VLDB
2001
ACM
82views Database» more  VLDB 2001»
13 years 12 months ago
Supporting Incremental Join Queries on Ranked Inputs
This paper investigates the problem of incremental joins of multiple ranked data sets when the join condition is a list of arbitrary user-defined predicates on the input tuples. ...
Apostol Natsev, Yuan-Chi Chang, John R. Smith, Chu...
KBSE
2000
IEEE
13 years 12 months ago
Practical Large Scale What-If Queries: Case Studies with Software Risk Assessment
When a lack of data inhibits decision making, large scale what-if queries can be conducted over the uncertain parameter ranges. Such what-if queries can generate an overwhelming a...
Tim Menzies, Erik Sinsel