情境相关的室内空间群组Top-k查询  

Context-dependent Group Top-k Query for Indoor Space

作　　者：李敬雯 卢明许 刘彬彬[1] LI Jing-wen;LU Ming-xu;LIU Bin-bin(The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing,Jiangsu 210007,China)

机构地区：[1]中国电子科技集团公司第二十八研究所

出　　处：《计算技术与自动化》2019年第4期97-104,共8页Computing Technology and Automation

摘　　要：在室内空间移动对象管理中,研究热点之一是如何整合和支持更加灵活的查询操作,如Top-k查询等。针对室内空间群组Top-k查询需要同时考虑室内空间结构的特殊性、室内空间中复杂而丰富的情境信息以及群组的整体情况的问题,提出了一个近似算法ICGTop-k(Indoor Context-dependent Group Topk)来计算情境相关的室内群组Top-k查询的结果集合,进行两次Top-k查询得到最终的查询结果,并采用聚集优化方法对算法进行优化。通过实验对ICGTop-k算法、KBest算法和GPM算法进行了对比分析。结果表明,ICGTop-k相比于KBest和GPM在查询执行时间和查询精度都有显著提高。In the management of moving objects for indoor space,one of the research hotspots is how to integrate and support more flexible query operations,such as Top-k query. In view of context-dependent group Top-k query for indoor space,it is necessary to consider the particularity of the indoor space structure,the complex and rich contextual information in the indoor space and the overall situation of the group,an approximate algorithm ICGTop-k(Indoor Context-dependent Group Top-k)is proposed to calculate the result set of context-dependent group Top-k query for indoor space. Top-k query dose twice to get the final query results and the algorithm is optimized by clustering optimization. ICGTop-k,KBest and GPM algorithms are compared and analyzed through experiments.The experimental results show that the query execution time and query precision of ICGTop-k are significantly improved compared with KBest and GPM.

关 键 词：情境 室内空间 移动对象 群组查询 TOP-K查询 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]