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许建秋 教授

南京航空航天大学计算机科学与技术学院教授,博士生导师,计算机系主任,先进计算和新型数据系统(ACID,Advanced Computing and Innovative Data System)研究所所长,CCF数据库专委委员,ACM China SIGSPATIAL Chapter执委会成员。于2005年和2008年在南京航空航天大学信息学院获得学士和硕士学位,2008年9月-2012.10前往德国进行博士学习(国家留学基金委资助)。博士期间主要研究多种运动方式移动对象,博士论文获得"magna cum laude" (from Latin, meaning with great honor)。 主要研究方向为时空数据管理、智能数据管理和分析等,包括移动对象数据库、时态和空间数据库以及人工智能赋能的数据库,主持/承担国家级项目5项,...

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Continuous k nearest neighbor queries over large multi-attribute trajectories: a systematic approach

发布时间: 2020-01-13 点击次数:

  • 所属单位:计算机科学与技术学院/人工智能学院/软件学院
  • 发表刊物:GEOINFORMATICA
  • 关键字:Trajectories Multi-attribute Continuous queries Nearest neighbors Index structure Update
  • 摘要:We study multi-attribute trajectories by combining standard trajectories (i.e., a sequence of timestamped locations) and descriptive attributes. A new form of continuous k nearest neighbor queries is proposed by integrating attributes into the evaluation. To enhance the query performance, a hybrid and flexible index is developed to manage both spatio-temporal data and attribute values. The index includes a 3D R-tree and a composite structure which can be popularized to work together with any R-tree based index and Grid-based index. We establish an efficient mechanism to update the index and define a cost model to estimate the I/Os. Query algorithms are proposed, in particular, an efficient method to determine the subtrees containing query attributes. Using synthetic and real datasets, we carry out comprehensive experiments in a prototype database system to evaluate the efficiency, scalability and generality. Our approach gains more than an order of magnitude speedup compared to three alternative approaches by using 1.8 millions of trajectories and hundreds of attribute values. The update performance is evaluated and the cost model is validated.
  • ISSN号:1384-6175
  • 是否译文:
  • 合写作者:Gueting, Ralf Hartmut,Gao, Yunjun
  • 通讯作者:许建秋
  • 发表时间:2018-10-01