Load-shedding strategy for data stream frequent item mining
一种面向流数据频繁项挖掘的降载策略*

With the unpredictability of data stream generation rate, when the rate exceeds system capacity, some of the data elements cannot be real-time processing. Load shedding techniques is one of the key technologies to deal with this issue. The deficiencies of current load shedding techniques are analyzed and a new load-shedding strategy for data stream frequent data item mining is proposed in this paper. This strategy adopts the semantics of tuple deletion based on data item frequency to delete tuples with relatively low frequency as possible, thus it can effectively solve the problems when mining the frequent data item while the system is overloaded. Moreover, starting and stopping load shedding strategy is controlled automatically based on the data stream rate, so it is effectively address the problem of load shedding adaptability. According to our experiments and analysis, the proposed strategy has the effectiveness of mining frequent items in data stream.