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2013 IEEE 13th International Conference on Data Mining Workshops (2007)
Omaha, Nebraska, USA
Oct. 28, 2007 to Oct. 31, 2007
ISBN: 0-7695-3033-8
pp: 501-506
ABSTRACT
The convergence of embedded sensor systems and stream query processing suggests an important role for database techniques, in managing data that only partially ? and of- ten inaccurately ? capture the state of the world. Reasoning about uncertainty as a first class citizen, inside a database system, becomes an increasingly important operation for processing non deterministic data. An essential step for such an approach lies in the choice of the appropriate un- certainty model, that captures the probabilistic information in the data, both accurately and at the right semantic de- tail level. This paper introduces Hierarchical First-Order Graphical Models (HFGMs), an intuitive and economical representation of the data correlations stored in a Proba- bilistic Data Management system, in a hierarchical setting. HFGM semantics allow for an efficient summarization of the probabilistic model that can be induced from a dataset at various levels of granularity, effectively controlling the trade-off of the model's complexity vs its accuracy.
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CITATION
Eirinaios Michelakis, Daisy Zhe Wang, Minos Garofalakis, Joseph M. Hellerstein, "Granularity Conscious Modeling for Probabilistic Databases", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 501-506, 2007, doi:10.1109/ICDMW.2007.63
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