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Evaluating probabilistic queries over imprecise data
TLDR
This paper addresses the important issue of measuring the quality of the answers to query evaluation based upon uncertain data, and provides algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve thequality of the executing queries. Expand
Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions
TLDR
The U-tree is proposed, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data and is fully dynamic, and does not place any constraints on the data pdfs. Expand
Preserving User Location Privacy in Mobile Data Management Infrastructures
TLDR
A data model to augment uncertainty to location data is suggested, and imprecise queries that hide the location of the query issuer and yields probabilistic results are proposed that investigate the evaluation and quality aspects for a range query. Expand
Querying imprecise data in moving object environments
TLDR
This work studies the execution of probabilistic nearest-neighbor queries, where the imprecision in answers to the queries is an inherent property of these applications due to uncertainty in the data, unlike the techniques for approximate nearest-NEighbor processing that trade accuracy for performance. Expand
Uncertain Data Mining: An Example in Clustering Location Data
TLDR
Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results and be enhanced by the K-means algorithm. Expand
Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data
TLDR
This paper develops two index structures and associated algorithms to efficiently answer Probabilistic Threshold Queries (PTQs), and establishes the difficulty of this problem by mapping one-dimensional intervals to a two-dimensional space, and shows that the problem of intervals indexing with probabilities is significantly harder than interval indexing which is considered a well-studied problem. Expand
Range search on multidimensional uncertain data
TLDR
The core of the methodology is a novel concept of “probabilistically constrained rectangle”, which permits effective pruning/validation of nonqualifying/qualifying data and a new index structure called the U-tree for minimizing the query overhead. Expand
Querying imprecise data in moving object environments
TLDR
Algorithms for computing these queries are presented for a generic object movement model and detailed solutions are discussed for two common models of uncertainty in moving object databases. Expand
Truth Inference in Crowdsourcing: Is the Problem Solved?
TLDR
It is believed that the truth inference problem is not fully solved, and the limitations of existing algorithms are identified and point out promising research directions. Expand
Mining uncertain data with probabilistic guarantees
TLDR
This paper proposes two effcient algorithms, which discover frequent patterns in bottom-up and top-down manners and explains how to use these patterns to generate association rules. Expand
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