Framework for Horizontal Scaling of Map Matching: Using Map-Reduce

V. Tiwari, Arti Arya, Sudha Chaturvedi
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引用次数: 13

Abstract

Map Matching is a well-established problem which deals with mapping raw time stamped location traces to edges of road network graph. Location data traces may be from devices like GPS, Mobile Signals etc. It has applicability in mining travel patterns, route prediction, vehicle turn prediction and resource prediction in grid computing etc. Existing map matching algorithms are designed to run on vertical scalable frameworks (enhancing CPU, Disk storage, Network Resources etc.). Vertical scaling has known limitations and implementation difficulties. In this paper we present a framework for horizontal scaling of map-matching algorithm, which overcomes limitations of vertical scaling. This framework uses Hbase for data storage and map-reduce computation framework. Both of these technologies belong to big data technology stack. Proposed framework is evaluated by running ST-matching based map matching algorithm.
地图匹配的水平缩放框架:使用Map- reduce
地图匹配是将原始时间标记的位置轨迹映射到路网图边缘的一个成熟问题。位置数据跟踪可能来自GPS、移动信号等设备。在网格计算中的出行模式挖掘、路线预测、车辆转弯预测、资源预测等方面具有一定的适用性。现有的映射匹配算法被设计为在垂直可扩展框架上运行(增强CPU,磁盘存储,网络资源等)。垂直扩展具有已知的限制和实现困难。本文提出了一种地图匹配算法的水平缩放框架,克服了垂直缩放的局限性。该框架使用Hbase作为数据存储和map-reduce计算框架。这两种技术都属于大数据技术栈。通过运行基于st匹配的映射匹配算法对所提出的框架进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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