Efficient Threshold Monitoring for Distributed Probabilistic Data

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Provided by: The University of Tulsa
Topic: Big Data
Format: PDF
In distributed data management, a primary concern is monitoring the distributed data and generating an alarm when a user specified constraint is violated. A particular useful instance is the threshold based constraint, which is commonly known as the distributed threshold monitoring problem. This paper extends this useful and fundamental study to distributed probabilistic data that emerge in a lot of applications, where uncertainty naturally exists when massive amounts of data are produced at multiple sources in distributed, networked locations.
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