Convex hull of randomly chosen points from a polytope
Proc. of an international workshop on Parallel algorithms and architectures
Annals of Operations Research
A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra
Discrete & Computational Geometry - Special issue on ACM symposium on computational geometry, North Conway
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Combining fuzzy information: an overview
ACM SIGMOD Record
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Consensus answers for queries over probabilistic databases
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Supporting ranking queries on uncertain and incomplete data
The VLDB Journal — The International Journal on Very Large Data Bases
Ranking continuous probabilistic datasets
Proceedings of the VLDB Endowment
Processing a large number of continuous preference top-k queries
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Computing immutable regions for subspace top-k queries
Proceedings of the VLDB Endowment
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Ranking queries report the top-K results according to a user-defined scoring function. A widely used scoring function is the weighted summation of multiple scores. Often times, users cannot precisely specify the weights in such functions in order to produce the preferred order of results. Adopting uncertain/incomplete scoring functions (e.g., using weight ranges and partially-specified weight preferences) can better capture user's preferences in this scenario. In this paper, we study two aspects in uncertain scoring functions. The first aspect is the semantics of ranking queries, and the second aspect is the sensitivity of computed results to refinements made by the user. We formalize and solve multiple problems under both aspects, and present novel techniques that compute query results efficiently to comply with the interactive nature of these problems.