Adaptive multi-stage distance join processing

被引:2
|
作者
Shin, HS [1 ]
Moon, B
Lee, S
机构
[1] Seoul Natl Univ, Sch Comp Engn, Seoul, South Korea
[2] Univ Arizona, Dept Comp Sci, Tucson, AZ 85721 USA
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A spatial distance join is a relatively new type of operation introduced for spatial and multimedia database applications. Additional requirements for ranking and stopping cardinality are often combined with the spatial distance join in on-line query processing or internet search environments. These requirements pose new challenges as well as opportunities for more efficient processing of spatial distance join queries. In this paper, we first present an efficient k-distance join algorithm that uses spatial indexes such as R-trees. Bidirectional node expansion and plane-sweeping techniques are used for fast pruning of distant pairs, and the plane-sweeping is further optimized by novel strategies for selecting a sweeping axis and direction. Furthermore, we propose adaptive multi-stage algorithms for Ic-distance join and incremental distance join operations. Our performance study shows that the proposed adaptive multi-stage algorithms outperform previous work by up to an order of magnitude for both Ic-distance join and incremental distance join queries, under various operational conditions.
引用
收藏
页码:343 / 354
页数:12
相关论文
共 50 条
  • [41] NQR Signal Processing Based on Multi-stage Wiener Filter
    Yang, Tao
    Su, Tao
    He, Xuehui
    [J]. 2010 SYMPOSIUM ON SECURITY DETECTION AND INFORMATION PROCESSING, 2010, 7 : 229 - 234
  • [42] METAL ORGANIC BASED SYNTAN FOR MULTI-STAGE LEATHER PROCESSING
    Jayakumar, G. C.
    Sangeetha, S.
    Sreeram, K. J.
    Rao, J. Raghava
    Nair, Balachandran Unni
    [J]. JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION, 2015, 110 (09): : 288 - 294
  • [43] Multi-stage stochastic optimization: the distance between stochastic scenario processes
    Timonina, Anna V.
    [J]. COMPUTATIONAL MANAGEMENT SCIENCE, 2015, 12 (01) : 171 - 195
  • [44] Adaptive multi-stage evolutionary search for constrained multi-objective optimization
    Li, Huiting
    Jin, Yaochu
    Cheng, Ran
    [J]. COMPLEX & INTELLIGENT SYSTEMS, 2024,
  • [45] Underwater Image Enhancement Based on Multi-Stage Collaborative Processing
    Yuan Hongchun
    Zhao Hualong
    Gao Kai
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2024, 61 (08)
  • [46] Multi-stage Multi-task feature learning via adaptive threshold
    Fan, Ya-Ru
    Wang, Yilun
    Huang, Ting-Zhu
    [J]. 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2016, : 1665 - 1670
  • [47] Multi-stage classification
    Senator, TE
    [J]. FIFTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2005, : 386 - 393
  • [48] Multi-stage programming
    Taha, W
    Sheard, T
    [J]. ACM SIGPLAN NOTICES, 1997, 32 (08) : 321 - 321
  • [49] The multi-stage railgun
    Musolino, A
    Raugi, M
    Rocco, R
    Tellini, A
    [J]. IEEE TRANSACTIONS ON MAGNETICS, 2001, 37 (01) : 445 - 449
  • [50] Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows
    Agarwal, Prerna
    Dave, Harshit
    Bandlamudi, Jayachandu
    Sindhgatta, Renuka
    Mukherjee, Kushal
    [J]. THIRTY-EIGTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 21, 2024, : 22842 - 22849