On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
Learning Probabilistic Relational Models
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aggregate operators in probabilistic databases
Journal of the ACM (JACM)
Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge Engineering
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Probabilistic ranked queries in uncertain databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query answering techniques on uncertain and probabilistic data: tutorial summary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Probabilistic top-k and ranking-aggregate queries
ACM Transactions on Database Systems (TODS)
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Managing Uncertain Data: Probabilistic Approaches
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Generating efficient safe query plans for probabilistic databases
Data & Knowledge Engineering
Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations
IEEE Transactions on Knowledge and Data Engineering
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Computing all skyline probabilities for uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Top-k queries on uncertain data: on score distribution and typical answers
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Semantics and evaluation of top-k queries in probabilistic databases
Distributed and Parallel Databases
Data & Knowledge Engineering
Ranking queries on uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Robust ranking of uncertain data
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Shooting top-k stars in uncertain databases
The VLDB Journal — The International Journal on Very Large Data Bases
Top-k best probability queries on probabilistic data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
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There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalized services, and decision making. In probabilistic relational databases, the most common problem in answering top-k queries (ranking queries) is selecting the top-k result based on scores and top-k probabilities. In this paper, we firstly propose novel answers to top-k best probability queries by selecting the probabilistic tuples which have not only the best top-k scores but also the best top-k probabilities. An efficient algorithm for top-k best probability queries is introduced without requiring users to define a threshold. The top-k best probability approach is more efficient and effective than the probability threshold approach (PT-k) [1,2]. Second, we add the ''k-best ranking score'' into the set of semantic properties for ranking queries on uncertain data proposed by [3,4]. Then, our proposed method is analyzed, which meets the semantic ranking properties on uncertain data. In addition, it proves that the answers to the top-k best probability queries overcome drawbacks of previous definitions of the top-k queries on probabilistic data in terms of semantic ranking properties. Lastly, we conduct an extensive experimental study verifying the effectiveness of answers to the top-k best probability queries compared to PT-k queries on uncertain data and the efficiency of our algorithm against the state-of-the-art execution of the PT-k algorithm using both real and synthetic data sets.