資料串流環境上作探勘的研究是近年來的熱門的題目，在相關的領域中也有多樣的研究已被發表出來，然後，大部分的研究都只著重在單一的資料串流上，而忽略多串流環境的限制，導致許多已被發表的方法無法被實際的運用，因此促成了這篇論文的誕生，我們注意到了在多串流環境上，一個良好的執行計畫是重要的，於是我們提出了plan graph的概念幫助我們列舉出所有可能的執行計畫，並進一步對這個graph被刪減的動作，最後我們提出一個直覺的方法能迅速的找到一個接近完美的執行計畫，在這篇論文的最後，我們以實驗的方式來驗証我們的理論。

Data stream management is a hot topic of recent interests. Mining frequent itemsets has been widely studied in this field. However, most of the past works focus on mining frequent itemsets over a single data stream. Unfortunately, the research can be impractical in real applications, such as network management, market analysis, etc. In the last few years, several researchers have noticed this problem and proposed several approaches to address it. Mining frequent itemsets over multiple data streams is one of the major objectives of our work. Furthermore, we consider the request for frequent itemsets over multiple data streams as a special type of queries. We propose a framework for processing multiple queries over multiple data streams. The major difficulty of the work is to handle a large amount of data from several data streams to efficiently produce the mining results. Thus, an efficient processing plan is essential. In this paper, we introduce the concept of the Plan Graph for enumerating all possible processing plans. By using the Plan Graph, we transform the problem of finding the optimal processing plan into the problem of finding the minimum-cost tree on a graph. Although the Plan Graph enumerates successfully all possible processing plans, finding the optimal processing plan can be time-consuming. Therefore, we propose a heuristic approach to efficiently find a plan close to the optimal one. Finally, we verify the efficiency of our approach by the experiments.

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