With the wide application of peer-to-peer (P2P) technologies in many fields such as E-commerce, it is increasingly necessary to do aggregation queries in P2P networks. However, due to the large scale and decentralization of P2P networks it is rather difficult to do this kind of operation. Aggregation queries will become even more difficult in case that the data in P2P networks are time-varying which is often occurs in practice. The existing aggregation methods for data in P2P networks all assume that the data are time-invariant. If these methods are directly applied to P2P networks with time-varying data, some problems will arise because the data used in aggregation processing would have changed owing to the long time of aggregation. So, this paper proposes an approximate aggregation method for time-varying data in P2P networks based on uniform sampling. The theoretical analysis and experimental results show that this aggregation method outperforms the existing methods and can effectively be applied to P2P networks with time-varying data.%随着P2P技术在电子商务等领域的广泛应用,对分布在P2P网络中的数据进行聚集操作的需求越来越迫切.但是,由于P2P网络的大规模及分散性,这种聚集操作的实现颇具挑战性.而且在很多应用中,P2P网络中的数据往往是随时间变化的,这进一步增加了聚集操作的难度.现有P2P网络中的聚集算法均假定网络中的数据是非时变的,如果将其直接应用在存在时变数据的P2P网络中,则会因为其聚集时间过长而导致聚集过程中数据已经发生变化的问题.为此,提出了一种P2P网络中基于均衡采样的时变数据近似聚集算法,理论分析和实验结果表明,该聚集算法在处理时变数据时优于已有的算法,可以有效地应用于存在时变数据的P2P网络中.
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