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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
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
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
TLDR
This work proposes transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. 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
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
TLDR
Con conceptual partitioning (CPM) is proposed, a comprehensive technique for the efficient monitoring of continuous NN queries and it is shown that it outperforms the current state-of-the-art algorithms for all problem settings. 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
Aggregate nearest neighbor queries in spatial databases
TLDR
If <i>Q</i> fits in memory and <i*P</i] is indexed by an R-tree, these algorithms for aggregate nearest neighbors that capture several versions of the problem, including weighted queries and incremental reporting of results are developed. Expand
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
TLDR
The MV3R-tree is proposed, a structure that utilizes the concepts of multi-version B-trees and 3D-Rtrees that compares favorably with specialized structures aimed at timestamp and interval window queries, both in terms of time and space requirements. Expand
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