Evaluating probabilistic queries over imprecise data

By Reynold Cheng, Dmitri V. Kalashnikov, Sunil Prabhakar. ACM SIGMOD Conference, 2003

695 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/09669b6b0eb122ae96872400832b7d470c3eb375

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Citations in the corpus

Probabilistic Skylines on Uncertain Data (2007, methodology): “Cheng et al. [9] proposed a broad classiﬁcation of probabilistic queries over uncertain data, and developed novel techniques for evaluating probabilistic queries.”
Trio: A System for Integrated Management of Data, Accuracy, and Lineage (2004, neutral): “Examples of this work can be found in [BGMP92, BP82, BP93, CKP03 , DS04, Fag02, Fuh90, IL84, KOR96, Lee92, LLRS97, LM04, LS90, Mot94, OO93, OW00, Sad91, SM01, YLW+95, Zim97], and even this list is woefully incomplete.”
Efficient Top-k Query Evaluation on Probabilistic Data (2007, background): “Also, we do not handle continuous attribute values [8, 5].”
Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions (2005, background): “[4] are the first to formulate uncertain retrieva l in general domains.”
Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data (2004, methodology): “In [7], a general classification, evaluation and quality of different types of probabilistic queries for sensor data are presented.”
Preserving User Location Privacy in Mobile Data Management Infrastructures (2006, background): “, answers are augmented with probability to indicate the confidence of their presence [17].”
A Survey of Uncertain Data Algorithms and Applications (2009, background): “Another example of such a database is the Orion project [25], [28] which presents query processing and indexing techniques in order to manage uncertainty over continuous intervals.”
Querying imprecise data in moving object environments (2003, methodology): “Here we modify the metric for probabilistic query range queries described in [ 3 , 13].”
Top-k Query Processing in Uncertain Databases (2007, background): “Tuple attributes can also be defined probabilistically over discrete or continuous domains [20] [6], e.g., a set of possible customer names in a dirty database, or an interval of possible sensor readings.”
Model-based approximate querying in sensor networks (2005, background): “There has been some recent work on approximate, probabilistic querying in sensor networks and moving object databases [46].”
PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks (2006, methodology): “Cheng et al. [4] and Deshpande et al. [8] have shown that generative-model based approaches can significantly reduce the communication burden in sensor networks.”
Efficient query evaluation on probabilistic databases (2004, neutral): “An alternate model for uncertainty is considered by Cheng et al. [ 7 ] that is more suitable for sensor databases.”
Model-Driven Data Acquisition in Sensor Networks (2004, background): “There has been some recent work on approximate, probabilistic querying in sensor networks and moving object databases [3].”
Managing uncertainty in moving objects databases (2004, neutral): “A recent result [Cheng et al. 2003] gave probabilistic estimates of the answer to a few categories of queries over uncertain values of dynamic data.”
Representing and Querying Correlated Tuples in Probabilistic Databases (2007, background): “[7] associate (continuous) probability distributions with attributes, and propose several query evaluation and indexing techniques over such data.”
Approximate Data Collection in Sensor Networks using Probabilistic Models (2006, methodology): “Storing the interval-valued data that comes out of an approximation scheme such as ours is also a well-studied problem in database literature (e.g., Cheng et al [ 2 ]).”
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms (2008, neutral): “probabilistic query processing techniques [16] to rank the”