Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Making the Threshold Algorithm Access Cost Aware
IEEE Transactions on Knowledge and Data Engineering
Supporting spatial aggregation in sensor network databases
Proceedings of the 12th annual ACM international workshop on Geographic information systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Tributaries and deltas: efficient and robust aggregation in sensor network streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Prominent streak discovery in sequence data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Given d input time series, an aggregated series can be formed by aggregating the d values at each time position. It is often useful to find the time positions whose aggregated values are the greatest. Instead of looking for individual top-k time positions, this paper gives two algorithms for finding the time interval (called the plateau) in which the aggregated values are close to each other (within a given threshold) and are all no smaller than the aggregated values outside of the interval. The first algorithm is a centralized one assuming that all data are available at a central location, and the other is a distributed search algorithm that does not require such a central location. The centralized algorithm has a linear time complexity with respect to the length of the time series, and the distributed algorithm employs the Threshold Algorithm by Fagin et al. and is quite efficient in reducing the communication cost as shown by the experiments reported in the paper.