中文摘要：

提出了一种新的多项式模型K＊TDG解决复杂数据流的分解搜索问题，其边权值K表明了系统参数之间的紧密程度．对K＊TDG、紧密K＊TDG和松散K＊TDG等概念进行了定义；对K＊TDG模型的基本加法运算和乘法运算进行了讨论、在此基础上提出了一种复杂数据流的分解匹配算法．为了降低算法的复杂度，还提出了一种根据复杂元件多项式次数分组的策略、实验结果表明所提出的K＊TDG模型能有效地用于复杂数据流的分解和匹配，所提出的算法和策略能使元件的搜索空间平均减少了49％．

英文摘要：

A new polynomial model K ＊ TDG is proposed to solve the decomposition and search problems of complex data flow, wherein the edge weight K represents its closeness to system parameters. The concepts of K ＊ TDG, compact K ＊ TDG, loose K ＊ TDG etc. are defined and the fundamental addition and multiplication operations of K ＊ TDG are discussed. Then a new decomposition and search algorithm for complex data flow is given. In order to decrease the algorithms complexity, a grouping strategy based on the degree of polynomials of complex components is proposed. Experimental results indicate that the proposed algorithm and strategy in this paper can reduce the searching space of components by an average of 49 percent.