时空数据挖掘是数据挖掘中的重要研究内容，其中时空预测的应用领域最为广泛。
Spatio-temporal data mining is an important research topic in data mining, and in which spatiotemporal forecasting is the most widely used.
交通流量的时空数据挖掘需要完整的数据，因此必须处理交通流量数据中的缺失值。
Missing values in traffic flow data should be imputed because complete data are needed for space-time data mining.
传统面向静态数据集的算法无法直接用于挖掘数据流，而现有数据流挖掘算法存在时空效率不高的缺陷。
Traditional data mining algorithms aiming at static datasets can't be used to mine data streams directly, neither do they have the time and space efficiency.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
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