@conference {13364,
	title = {Ef?cient Query Evaluation over Temporally Correlated Probabilistic Streams},
	booktitle = {IEEE 25th International Conference on Data Engineering, 2009. ICDE {\textquoteright}09},
	year = {2009},
	month = {2009/04/29/March},
	pages = {1315 - 1318},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we address the problem of efficient query evaluation over highly correlated probabilistic streams. We observe that although probabilistic streams tend to be strongly correlated in space and time, the correlations are usually quite structured (i.e., the same set of dependencies and independences repeat across time) and Markovian (i.e., the state at time "t+1" is independent of the states at previous times given the state at time "t"). We exploit this observation to compactly encode probabilistic streams by decoupling the correlation structure (the set of dependencies) from the actual probability values. We develop novel stream processing operators that can efficiently and incrementally process new data items; our operators are based on the previously proposed framework of viewing probabilistic query evaluation as inference over probabilistic graphical models (PGMs) [P. Sen and A. Deshpande, 2007]. We develop a query planning algorithm that constructs efficient query plans that are executable in polynomial-time whenever possible, and we characterize queries for which such plans are not possible. Finally we conduct an extensive experimental evaluation that illustrates the advantages of exploiting the structured nature of correlations in probabilistic streams.},
	keywords = {Birds, Computerized monitoring, correlated probabilistic streams, correlation structure, Data engineering, data mining, Databases, Event detection, Graphical models, inference mechanisms, Markov processes, polynomial time, probabilistic database, probabilistic graphical model, probabilistic query evaluation, query evaluation, query planning algorithm, query plans, Query processing, Random variables, stream processing operator, Streaming media},
	isbn = {978-1-4244-3422-0},
	doi = {10.1109/ICDE.2009.229},
	author = {Kanagal,B. and Deshpande, Amol}
}
@conference {13402,
	title = {Representing Tuple and Attribute Uncertainty in Probabilistic Databases},
	booktitle = {Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007},
	year = {2007},
	month = {2007/10/28/31},
	pages = {507 - 512},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources-sensor data, experimental data, data from uncurated sources, and many others. There is a growing need to be able to flexibly represent the uncertainties in the data, and to efficiently query the data. Building on existing probabilistic database work, we present a unifying framework which allows a flexible representation of correlated tuple and attribute level uncertainties. An important capability of our representation is the ability to represent shared correlation structures in the data. We provide motivating examples to illustrate when such shared correlation structures are likely to exist. Representing shared correlations structures allows the use of sophisticated inference techniques based on lifted probabilistic inference that, in turn, allows us to achieve significant speedups while computing probabilities for results of user-submitted queries.},
	keywords = {attribute uncertainty, Computer science, Conferences, correlation structures, data mining, Data models, database management systems, Educational institutions, inference mechanisms, noisy data sources, probabilistic database, probabilistic inference, Probability distribution, Query processing, Relational databases, Sensor phenomena and characterization, tuple representation, Uncertainty, uncertainty handling},
	isbn = {978-0-7695-3019-2},
	doi = {10.1109/ICDMW.2007.11},
	author = {Sen,P. and Deshpande, Amol and Getoor, Lise}
}
