Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Managing Probabilistic Data with MystiQ: The Can-Do, the Could-Do, and the Can't-Do
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Approximate lineage for probabilistic databases
Proceedings of the VLDB Endowment
Fast and Simple Relational Processing of Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Building ranked mashups of unstructured sources with uncertain information
Proceedings of the VLDB Endowment
A unifying probability measure for logic-based similarity conditions on uncertain relational data
Proceedings of the 1st Workshop on New Trends in Similarity Search
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic Ranking Techniques in Relational Databases
Probabilistic Ranking Techniques in Relational Databases
A probabilistic interpretation for a geometric similarity measure
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Probabilistic Databases
QSQL: incorporating logic-based retrieval conditions into SQL
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Ranking Query Answers in Probabilistic Databases: Complexity and Efficient Algorithms
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Applying weighted queries on probabilistic databases
Proceedings of the 21st ACM international conference on Information and knowledge management
ProQua: a system for evaluating logic-based scoring functions on uncertain relational data
Proceedings of the 16th International Conference on Extending Database Technology
| Hi-index | 0.00 |
Probabilistic databases have been established as a powerful technique for managing and analysing large uncertain data sets. A major challenge for probabilistic databases is query evaluation. There exist even simple relational queries for which the exact probability computation is $\#\mathcal{P}$-hard. Consequently, if we are only interested in the k highest ranked tuples, then an efficient pre-filtering can reduce the computation time significantly. In this work we present a top-k filter which computes a small candidate set for a top-k answer based on a complex relational query in polynomial time.