基于 HBase 的面向语义单元的室内移动对象索引

随着室内定位技术的广泛应用,传感器记录了大量室内移动对象的位置数据,而索引技术作为移动对象数据分析的基础工作也得到越来越多的研究。已有索引技术多是针对室外空间的移动对象,不能支持室内移动对象数据的三维立体性、轨迹的复杂性、随机性等特点,这些索引技术也仅仅关注了移动对象的位置信息,忽略了语义信息,不能有效地支持室内移动对象的管理和分析,并且当面对海量的移动对象数据时,这些架构在传统关系型数据库上的索引都存在性能瓶颈问题。因此,本文提出了面向语义单元的移动对象表达模型,利用语义单元将室内移动对象的位置语义化,设计了SCoII （Semantic Cell Oriented Indoor moving objects Index）索引结构对室内移动对象的历史数据进行索引,能够有效支持语义粒度上的时空范围查询、移动对象语义轨迹查询。索引基于HBase实现,能够适应大规模的并发更新与查询,具有良好的规模扩展性,规避了大数据给传统数据库带来的性能瓶颈问题,实验证明其具有良好的更新和查询性能。该索引的实现方便了基于语义的室内移动对象分析和数据挖掘工作,为今后的分析工作奠定了基础。

Semantic Cell Oriented Indoor Moving Objects Index based on HBase

With the development of indoor positioning technique, more and more position data of indoor moving objects are recorded by sensors. As the basic work of moving objects database, index technique has become a research hot-spot. Majority of existing moving objects index are for outdoor moving objects which are not suitable for indoor environment. Also, they only build index on geography coordinates of moving objects, lack of supporting of semantic information which can offer effective support for management and analysis of indoor moving objects. There will be a performance bottleneck when massive data are ingested and frequent querying are asked when implemented on traditional relational database. In this paper, we built a grid of indoor floor environment and create a map relation from grid to semantic cell. Then, we utilized this map to semanticize indoor moving objects’ location if it was contained in a semantic cell. After this work, we built an index called SCoII （Semantic Cell Oriented Indoor moving objects Index）. SCoII can answer not only semantic spatio-temporal range query but also indoor moving object’s semantic trajectory query, which can support for semantic-based analysis of indoor moving objects. SCoII is implemented on HBase, so it also avoided the performance degradation of traditional relational database when encounting massive data and have good performance of updating and querying without bottleneck. Experimental results also showed that it can be adapt to big data. Supporting for semantic information of indoor moving object is the most important feature of SCoII. More data mining jobs can be done on indoor moving object’s semantic location and semantic trajectory such as the simple example given out at the end. Management and analysis based on semantic of indoor moving objects will be convenient on SCoII, which lays a foundation of analysis work in the future.