Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Robust and efficient fuzzy match for online data cleaning
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
Optimizing Top-k Selection Queries over Multimedia Repositories
IEEE Transactions on Knowledge and Data Engineering
Efficient top-K query calculation in distributed networks
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The threshold join algorithm for top-k queries in distributed sensor networks
DMSN '05 Proceedings of the 2nd international workshop on Data management for sensor networks
KLEE: a framework for distributed top-k query algorithms
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A geometric approach to monitoring threshold functions over distributed data streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Sharing aggregate computation for distributed queries
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
From complete to incomplete information and back
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Top-k Monitoring in Wireless Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
MCDB: a monte carlo approach to managing uncertain data
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)
Conditioning probabilistic databases
Proceedings of the VLDB Endowment
Exploiting shared correlations in 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
Efficient Constraint Monitoring Using Adaptive Thresholds
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Top-k Spatial Joins of Probabilistic Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Probabilistic ranking over relations
Proceedings of the 13th International Conference on Extending Database Technology
Threshold query optimization for uncertain data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Ranking queries on uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic threshold join over distributed uncertain data
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Efficient fuzzy ranking queries in uncertain databases
Applied Intelligence
Entity resolution for distributed probabilistic data
Distributed and Parallel Databases
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Ranking queries are essential tools to process large amounts of probabilistic data that encode exponentially many possible deterministic instances. In many applications where uncertainty and fuzzy information arise, data are collected from multiple sources in distributed, networked locations, e.g., distributed sensor fields with imprecise measurements, multiple scientific institutes with inconsistency in their scientific data. Due to the network delay and the economic cost associated with communicating large amounts of data over a network, a fundamental problem in these scenarios is to retrieve the global top-k tuples from all distributed sites with minimum communication cost. Using the well founded notion of the expected rank of each tuple across all possible worlds as the basis of ranking, this work designs both communication- and computation-efficient algorithms for retrieving the top-k tuples with the smallest ranks from distributed sites. Extensive experiments using both synthetic and real data sets confirm the efficiency and superiority of our algorithms over the straightforward approach of forwarding all data to the server.