Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Indefinite and maybe information in relational databases
ACM Transactions on Database Systems (TODS)
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
Query evaluation in probabilistic relational databases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Avatar semantic search: a database approach to information retrieval
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The dichotomy of conjunctive queries on probabilistic structures
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maximally joining probabilistic data
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Database design and querying within the fuzzy semantic model
Information Sciences: an International Journal
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Materialized views in probabilistic databases: for information exchange and query optimization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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
Database Support for Probabilistic Attributes and Tuples
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Information Sciences: an International Journal
Bridging the gap between intensional and extensional query evaluation in probabilistic databases
Proceedings of the 13th International Conference on Extending Database Technology
Efficient processing of probabilistic set-containment queries on uncertain set-valued data
Information Sciences: an International Journal
MUD: Mapping-based query processing for high-dimensional uncertain data
Information Sciences: an International Journal
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In this paper, we prove that a query plan is safe in tuple independent probabilistic databases if and only if its every answer tuple is tree structured in probabilistic graphical models. We classify hierarchical queries into core and non-core hierarchical queries and show that the existing methods can only generate safe plans for core hierarchical queries. Inspired by the bucket elimination framework, we give the sufficient and necessary conditions for the answer relation of every candidate sub-query to be used as a base relation. Finally, the proposed algorithm generates safe plans for extensional query evaluation on non-boolean hierarchical queries and invokes the SPROUT algorithm [24] for intensional query evaluation on boolean queries. A case study on the TPC-H benchmark reveals that the safe plans of Q7 and Q8 can be evaluated efficiently. Furthermore, extensive experiments show that safe plans generated by the proposed algorithm scale well.