MOIST：一个可扩展的并行移动对象索引器
MOIST: A Scalable and Parallel Moving Object Indexer with School Tracking
摘要：
基于位置的服务（LBS）正在迅速成为广泛的移动应用的下一个无处不在的技术。为了支持需要最近邻和历史查询的应用程序，LBS空间索引器必须能够有效地更新，查询，存档和挖掘位置记录，这些记录可能会相互竞争。在这项工作中，我们提出了MOIST，其基线是一个递归的空间分区索引BigTable上建立。为了减少更新和查询争用，MOIST将附近具有相似轨迹的对象分组到同一个学校，并仅跟踪学校领导者的历史。这种动态聚类方案可以消除冗余更新，从而减少更新延迟。为了改进历史查询处理，MOIST在内存中保留一些历史数据，同时以本地保留的方式将过时的数据刷新到并行磁盘上。通过实验研究，我们表明，MOIST可以支持高效的最近邻居和历史查询，并可以扩展以及随着用户数量和更新频率的增加。
英文摘要：
Location-Based Service (LBS) is rapidly becoming the next ubiquitous technology for a wide range of mobile applications. To support applications that demand nearest-neighbor and history queries, an LBS spatial indexer must be able to efficiently update, query, archive and mine location records, which can be in contention with each other. In this work, we propose MOIST, whose baseline is a recursive spatial partitioning indexer built upon BigTable. To reduce update and query contention, MOIST groups nearby objects of similar trajectory into the same school, and keeps track of only the history of school leaders. This dynamic clustering scheme can eliminate redundant updates and hence reduce update latency. To improve history query processing, MOIST keeps some history data in memory, while it flushes aged data onto parallel disks in a locality-preserving way. Through experimental studies, we show that MOIST can support highly efficient nearest-neighbor and history queries and can scale well with an increasing number of users and update frequency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
