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Progressive skyline computation in database systems
The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in theExpand
Anatomy: simple and effective privacy preservation
TLDR
A linear-time algorithm is developed for computing anatomized tables that obey the l-diversity privacy requirement, and minimize the error of reconstructing the microdata. Expand
Query Processing in Spatial Network Databases
TLDR
A Euclidean restriction and a network expansion framework that take advantage of location and connectivity to efficiently prune the search space are developed and applied to the most popular spatial queries. Expand
An optimal and progressive algorithm for skyline queries
TLDR
BBS is a progressive algorithm also based on nearest neighbor search, which is IO optimal, i.e., it performs a single access only to those R-tree nodes that may contain skyline points and its space overhead is significantly smaller than that of NN. Expand
Personalized privacy preservation
TLDR
The authors' technique performs the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the microdata, and establishes the superiority of the proposed solutions. Expand
M-invariance: towards privacy preserving re-publication of dynamic datasets
TLDR
A new generalization principle m-invariance is developed that effectively limits the risk of privacy disclosure in re-publication and is accompanied with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Expand
Continuous Nearest Neighbor Search
TLDR
This paper proposes techniques that solve the problem by performing a single query for the whole input segment, and proposes analytical models for the expected size of the output, as well as the cost of query processing, and extend out techniques to several variations of the problem. Expand
The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries
TLDR
This paper proposes a new index structure called the TPR*- tree, which takes into account the unique features of dynamic objects through a set of improved construction algorithms and provides cost models that determine the optimal performance achievable by any data-partition spatio-temporal access method. 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
Reverse kNN Search in Arbitrary Dimensionality
TLDR
The proposed algorithms for exact processing of RkNN with arbitrary values of k on dynamic multidimensional datasets utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. Expand
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