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AGGREGATE QUERY METHOD AND SYSTEM FOR TRAFFIC DATA FLOWS

机译:流量数据流的聚集查询方法和系统

摘要

An aggregate query method and system for traffic data flows belong to the technical field of information processing. The method comprises obtaining spatial-temporal information of a moving object to generate a traffic data flow, partitioning a data space into sub-units, grouping the adjacent sub-units with similar frequencies into a small number of buckets, calculating Kalman gains of the buckets on the basis of the frequency of the buckets, indexing the buckets to form a BPT (binary partition tree) index of a current timestamp by means of a BPT, and forming a historic index by serializing the BPT after the current timestamp is over; performing an aggregate query, and when the frequency of the buckets changes too much, the aggregate query value is replaced by the optimal estimated value of the frequency of the buckets. The system comprises: an information collecting module, a data processing module, an index processing module, an application service module and an index storage module. The method enables effective suppression of the maximum relative error of outliers during traffic data flow query, thereby guaranteeing the availability of the aggregate query method.
机译:交通数据流的聚合查询方法和系统属于信息处理技术领域。该方法包括获得移动物体的时空信息以产生交通数据流,将数据空间划分为子单元,将具有相似频率的相邻子单元分组为少量的桶,计算桶的卡尔曼增益。根据存储桶的频率,对存储桶进行索引,以借助BPT对当前时间戳进行索引,形成当前时间戳的BPT(二进制分区树)索引,并在当前时间戳结束后对BPT进行序列化,从而形成历史索引;执行聚合查询,当存储桶的频率变化太大时,聚合查询值将被替换为存储桶的频率的最佳估计值。该系统包括:信息收集模块,数据处理模块,索引处理模块,应用服务模块和索引存储模块。该方法能够有效抑制交通数据流查询过程中离群值的最大相对误差,从而保证了聚合查询方法的可用性。

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