| 面向动态关联数据的高效稀疏图索引方法 |
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| 引用本文： | 朱庆,冯斌,李茂粟,陈媚特,徐肇文,谢潇,张叶廷,刘铭崴,黄志勤,冯义从.面向动态关联数据的高效稀疏图索引方法[J].测绘学报,2020,49(6):681-691. |
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| 作者姓名： | 朱庆 冯斌 李茂粟 陈媚特 徐肇文 谢潇 张叶廷 刘铭崴 黄志勤 冯义从 |
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| 作者单位： | 1. 西南交通大学地球科学与环境工程学院, 四川 成都 611756;2. 浙江中海达空间信息技术有限公司, 浙江 湖州 313299;3. 四川视慧智图空间信息技术有限公司, 四川 成都 610036;4. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;5. 四川省自然资源厅信息中心, 四川 成都 610072 |
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| 基金项目： | 国家重点研发计划（2018YFB0505404）；国家自然科学基金（41871314） |
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| 摘 要： | 为了高效组织管理日益增加的智能感知和关联关系数据,满足多层次任务对多模态场景数据多维特征计算和关联挖掘的需求,针对现有树结构外存索引方法存在的磁盘I/O密集、处理效率低、对关联关系支持弱的瓶颈问题,提出了一种时空关系稀疏图索引方法。设计了一种基于内存图模型的时空索引结构,将多模态场景数据抽象为图的节点和边,支持时间、空间以及关联关系的高效组织,并基于稀疏矩阵进行时空关系图索引的内存表达和存储;以多维树索引为例进行了索引构建以及多模式查询试验。试验结果表明,本文方法在索引生成、时空查询和复杂时空关系查询效率等方面均优于对比方法,支持动态关联的多模态场景数据实时高性能处理和低延迟访问。
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| 关 键 词： | 时空索引 内存图模型 稀疏矩阵 动态关联数据 场景数据组织 |
| 收稿时间： | 2019-07-08 |
| 修稿时间： | 2020-03-12 |
An efficient sparse graph index method for dynamic and associated data |
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| ZHU Qing,FENG Bin,LI Maosu,CHEN Meite,XU Zhaowen,XIE Xiao,ZHANG Yeting,LIU Mingwei,HUANG Zhiqin,FENG Yicong.An efficient sparse graph index method for dynamic and associated data[J].Acta Geodaetica et Cartographica Sinica,2020,49(6):681-691. |
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| Authors: | ZHU Qing FENG Bin LI Maosu CHEN Meite XU Zhaowen XIE Xiao ZHANG Yeting LIU Mingwei HUANG Zhiqin FENG Yicong |
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| Institution: | (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;Zhejiang Hi-Target Spatial Information Technology Co. Ltd., Huzhou 313299, China;Sichuan Smart Map Spatial Information Technology Co. Ltd., Chengdu 610036, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;Information Center of Department of Nature Resources of Sichuan Province, Chengdu 610072, China) |
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| Abstract: | In order to efficiently organize and manage the increasing real-time sensor data and associations, and satisfy the requirements of multi-level tasks for multi-dimensional feature calculation and association mining of multi-modal scene data, a spatiotemporal sparse graph index method is proposed for the bottleneck problems of disk I/O-intensive, low processing efficiency and weak support for associations existing in the tree structure based external indexing methods. Firstly, a spatiotemporal index structure based on in-memory graph model is designed, which abstracts multi-modal scene data into nodes and edges of graph and supports efficient organization of time, location and associations of multi-modal scene data. Then, a sparse matrix based method of in-memory representation and storage for spatiotemporal graph index is presented. Finally, taking the multi-dimensional tree index as an example, the index construction and multi-model query experiments are carried out. The experimental results show that the method is superior to the contrast method in several aspects, such as generation efficiency, query performance, and then supports real-time high-performance processing of dynamic and associated multi-modal scene data with low latency access. |
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| Keywords: | spatiotemporal index in-memory graph model sparse matrix dynamic and associated data scene data organization |
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