Approximate aggregation techniques for sensor databases
By Jeffrey Considine, Feifei Li, George Kollios, John W. Byers. Proceedings / International Conference on Data Engineering, 2004
639 citations recorded by the source corpus.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/fb6952ca14ca0ec881ac8eb43d63ea0c4cee140c
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In Computer Networks and Communications
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Citations in the corpus
- Synopsis diffusion for robust aggregation in sensor networks (2004, methodology): “Count Distinct can be done using a trivial adaption of Flajolet and Martin s algorithm (FM) [8].”
- A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “Distinct Value Estimation: Distinct value estimation has been an open problem in stream processing and database research [6,8,15,27].”
- Model-Driven Data Acquisition in Sensor Networks (2004, background): “have shown that sketch based approximation techniques can be applied in sensor networks [17].”
- A Survey of Fault Management in Wireless Sensor Networks (2007, neutral): “Different from TAG, SKETCH [30] uses a DAG instead of a tree for data delivery.”
- Medians and beyond: new aggregation techniques for sensor networks (2004, methodology): “[4] have discussed how to compute COUNT , SUM , AVERAGE in a robust fashion in the presence of failures such as lost and duplicate packets.”
- Tributaries and deltas: efficient and robust aggregation in sensor network streams (2005, background): “Tributary Region Delta Region (Tree) - (Multi path) Base Station Figure 1: Tributaries and Deltas ing sensor communication is a primary means for conserving battery power [1, 18].”
- Top-k Query Processing in Uncertain Databases (2007, background): “Efficient processing of uncertain (probabilistic) data is a crucial requirement in different domains including sensor networks [18] [22] [8], moving objects tracking [7] [9] and data cleaning [12] [3].”