MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
BayesStore: managing large, uncertain data repositories with probabilistic graphical models
Proceedings of the VLDB Endowment
Cleaning uncertain data with quality guarantees
Proceedings of the VLDB Endowment
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient join processing on uncertain data streams
Proceedings of the 18th ACM conference on Information and knowledge management
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Real-world applications confront uncertain streams derived from unreliable data acquisition equipments and/or defective processing algorithms. However, application context covers specific cleaning rules to bring data close to the reality (i.e. data quality), and query features can filter data for the efficiency (i.e. data volume). In this paper, we propose a framework for cleaning uncertain data for query effectiveness and efficiency, which processes high-volume streams in parallel, and append new cleaning rules & queries seamlessly. We implement a prototype for video surveillance application over the architecture.