Data structures for range-aggregate extent queries

被引:8
|
作者
Gupta, Prosenjit [1 ,2 ]
Janardan, Ravi [3 ]
Kumar, Yokesh [3 ]
Smid, Michiel [4 ]
机构
[1] Mentor Graph Corp, Hyderabad 5000082, Andhra Pradesh, India
[2] Int Inst Informat Technol, Hyderabad 500032, Andhra Pradesh, India
[3] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
[4] Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada
来源
关键词
Computational geometry; Data structures; Closest pair; Diameter; Width;
D O I
10.1016/j.comgeo.2009.08.001
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A fundamental and well-studied problem in computational geometry is range searching, where the goal is to preprocess a set, S, of geometric objects (e.g., points in the plane) so that the subset S' c S that is contained in a query range (e.g., an axes-parallel rectangle) can be reported efficiently. However, in many situations, what is of interest is to generate a more informative "summary" of the output, obtained by applying a suitable aggregation function on S'. Examples of such aggregation functions include count, sum, min, max, mean, median, mode, and top-k that are usually computed on a set of weights defined suitably on the objects. Such range-aggregate query problems have been the subject of much recent research in both the database and the computational geometry communities. In this paper, we further generalize this line of work by considering aggregation functions on point-sets that measure the extent or "spread" of the objects in the retrieved set S'. The functions considered here include closest pair, diameter, and width. The challenge here is that these aggregation functions (unlike, say, count) are not efficiently decomposable in the sense that the answer to S' cannot be inferred easily from answers to subsets that induce a partition of S'. Nevertheless, we have been able to obtain space- and query-time-efficient solutions to several such problems including: closest pair queries with axes-parallel rectangles on point sets in the plane and on random point-sets in R-d (d >= 2), closest pair queries with disks on random point-sets in the plane, diameter queries on point-sets in the plane, and guaranteed-quality approximations for diameter and width queries in the plane. Our results are based on a combination of geometric techniques, including multilevel range trees, Voronoi Diagrams, Euclidean Minimum Spanning Trees, sparse representations of candidate outputs, and proofs of (expected) upper bounds on the sizes of such representations. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:329 / 347
页数:19
相关论文
共 50 条
  • [21] Efficient Data Structures for Range Shortest Unique Substring Queries
    Abedin, Paniz
    Ganguly, Arnab
    Pissis, Solon P.
    Thankachan, Sharma V.
    ALGORITHMS, 2020, 13 (11) : 1 - 9
  • [22] Range-Consistent Answers of Aggregate Queries under Aggregate Constraints
    Flesca, Sergio
    Furfaro, Filippo
    Parisi, Francesco
    SCALABLE UNCERTAINTY MANAGEMENT, SUM 2010, 2010, 6379 : 163 - 176
  • [23] Doquet: Differentially Oblivious Range and Join Queries with Private Data Structures
    Qiu, Lina
    Kellaris, Georgios
    Mamoulis, Nikos
    Nissim, Kobbi
    Kollios, George
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2023, 16 (13): : 4160 - 4173
  • [24] Range queries on uncertain data
    Li, Jian
    Wang, Haitao
    THEORETICAL COMPUTER SCIENCE, 2016, 609 : 32 - 48
  • [25] Range Queries on Uncertain Data
    Li, Jian
    Wang, Haitao
    ALGORITHMS AND COMPUTATION, ISAAC 2014, 2014, 8889 : 326 - 337
  • [26] Efficient Aggregate Queries on Location Data with Confidentiality
    Feng, Da
    Zhou, Fucai
    Wang, Qiang
    Wu, Qiyu
    Li, Bao
    SENSORS, 2022, 22 (13)
  • [27] The Semantics of Aggregate Queries in Data Exchange Revisited
    Kolaitis, Phokion G.
    Spezzano, Francesca
    SCALABLE UNCERTAINTY MANAGEMENT, SUM 2013, 2013, 8078 : 233 - 246
  • [28] Approximation algorithms for aggregate queries on uncertain data
    Chen D.
    Chen L.
    Wang J.
    Wu Y.
    Wang J.
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2018, 58 (03): : 231 - 236
  • [29] Interval Estimation for Aggregate Queries on Incomplete Data
    Zhang, An-Zhen
    Li, Jian-Zhong
    Gao, Hong
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2019, 34 (06) : 1203 - 1216
  • [30] Interval Estimation for Aggregate Queries on Incomplete Data
    An-Zhen Zhang
    Jian-Zhong Li
    Hong Gao
    Journal of Computer Science and Technology, 2019, 34 : 1203 - 1216