GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Probabilistic histograms for probabilistic data
Proceedings of the VLDB Endowment
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Approximating sliding windows by cyclic tree-like histograms for efficient range queries
Data & Knowledge Engineering
On wavelet decomposition of uncertain time series data sets
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
(Approximate) uncertain skylines
Proceedings of the 14th International Conference on Database Theory
Synopses for probabilistic data over large domains
Proceedings of the 14th International Conference on Extending Database Technology
Geometric computations on indecisive points
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Histograms as statistical estimators for aggregate queries
Information Systems
Range counting coresets for uncertain data
Proceedings of the twenty-ninth annual symposium on Computational geometry
Efficient and scalable monitoring and summarization of large probabilistic data
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Data & Knowledge Engineering
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There is a growing realization that uncertain information is a first-class citizen in modern database management. As such, we need techniques to correctly and efficiently process uncertain data in database systems. In particular, data reduction techniques that can produce concise, accurate synopses of large probabilistic relations are crucial. Similar to their deterministic relation counterparts, such compact probabilistic data synopses can form the foundation for human understanding and interactive data exploration, probabilistic query planning and optimization, and fast approximate query processing in probabilistic database systems. In this paper, we introduce definitions and algorithms for building histogram- and Haar wavelet-based synopses on probabilistic data. The core problem is to choose a set of histogram bucket boundaries or wavelet coefficients to optimize the accuracy of the approximate representation of a collection of probabilistic tuples under a given error metric. For a variety of different error metrics, we devise efficient algorithms that construct optimal or near optimal size B histogram and wavelet synopses. This requires careful analysis of the structure of the probability distributions, and novel extensions of known dynamic programming-based techniques for the deterministic domain. Our experiments show that this approach clearly outperforms simple ideas, such as building summaries for samples drawn from the data distribution, while taking equal or less time.