Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Sketching streams through the net: distributed approximate query tracking
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Communication-efficient distributed monitoring of thresholded counts
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A geometric approach to monitoring threshold functions over distributed data streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Toward sophisticated detection with distributed triggers
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Streaming in a connected world: querying and tracking distributed data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A geometric approach to monitoring threshold functions over distributed data streams
ACM Transactions on Database Systems (TODS)
Distributed set-expression cardinality estimation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximate continuous querying over distributed streams
ACM Transactions on Database Systems (TODS)
Shape sensitive geometric monitoring
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate Clustering on Distributed Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Optimal tracking of distributed heavy hitters and quantiles
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Distributed threshold querying of general functions by a difference of monotonic representation
Proceedings of the VLDB Endowment
Algorithms for distributed functional monitoring
ACM Transactions on Algorithms (TALG)
The continuous distributed monitoring model
ACM SIGMOD Record
Report on the first workshop on innovative querying of streams
ACM SIGMOD Record
Sketch-based geometric monitoring of distributed stream queries
Proceedings of the VLDB Endowment
Data management research at the technical university of crete
ACM SIGMOD Record
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Many modern streaming applications, such as online analysis of financial, network, sensor and other forms of data are inherently distributed in nature. An important query type that is the focal point in such application scenarios regards actuation queries, where proper action is dictated based on a trigger condition placed upon the current value that a monitored function receives. Recent work studies the problem of (non-linear) sophisticated function tracking in a distributed manner. The main concept behind the geometric monitoring approach proposed there, is for each distributed site to perform the function monitoring over an appropriate subset of the input domain. In the current work, we examine whether the distributed monitoring mechanism can become more efficient, in terms of the number of communicated messages, by extending the geometric monitoring framework to utilize prediction models. We initially describe a number of local estimators (predictors) that are useful for the applications that we consider and which have already been shown particularly useful in past work. We then demonstrate the feasibility of incorporating predictors in the geometric monitoring framework and show that prediction-based geometric monitoring in fact generalizes the original geometric monitoring framework. We propose a large variety of different prediction-based monitoring models for the distributed threshold monitoring of complex functions. Our extensive experimentation with a variety of real data sets, functions and parameter settings indicates that our approaches can provide significant communication savings ranging between two times and up to three orders of magnitude, compared to the transmission cost of the original monitoring framework.