中文摘要：

挖掘多数据流的异步偶合模式是具有挑战性的工作．主要的研究工作包括：（1）研究Haar小波滤波技术在挖掘流数据的异步偶合模式中的应用；（2）引入小波系数序列来度量数据流的异步局域偶合度；证明了一系列定理，保证了度量方法的正确性；（3）设计了环形滑动窗口和挖掘异步偶合模式的抗噪声增量算法，其时间复杂性小于O（n^）；（4）使用真实数据进行模拟实验，验证了算法的有效性．

英文摘要：

Mining asynchronous coincidence pattern is a difficult task in multi-data streams. The main contributions of this work included： （1） The filter technique of Haar Wavelet is investigated and applied to mining asynchronous coincidence pattern in multi-streams; （2） The Wavelet coefficient series are applied to the measurement of asynchronous coincidence between data streams. A series of theorems are proved to ensure the validity of measuring asynchronous coincidence; （3） The anti-noise increment algorithms are designed on loop sliding windows to mine asynchronous coincidence pattern and implemented with complexity O（n^2）; （4） The extensive experiments on real data are given to validate algorithms.