Parameterized matching has proven to be an efficient and useful tool for detecting code duplications. This paper presents a technique for calculating parameterized Longest Common Prefix ($plcp$) in constant time based on the knowledge about the $plcp$ of the following suffixes. Using this technique, online p-suffix tree construction can be done in worst case time $O(\log n)$ per input symbol. Searching for a pattern of length m in the resulting suffix tree takes $O(\min \{m\log (|\mathrm{\Sigma }|+|\mathrm{\Pi }|),m+\log n\}+m{\tau }_{\mathrm{\Pi }}+t_{occ})$ time, where $t_{occ}$ is the number of occurrences of the pattern, and ${\tau }_{\mathrm{\Pi }}$ depends on Π. For constant-sized Π, ${\tau }_{\mathrm{\Pi }}=1$, for polynomial-sized Π, ${\tau }_{\mathrm{\Pi }}=\log \log |\mathrm{\Pi }|$, and for unbounded Π, ${\tau }_{\mathrm{\Pi }}=\log |\mathrm{\Pi }|$.

事实证明，参数化匹配是检测代码重复的有效工具。本文提出了一种计算参数化的最长公共前缀（$p升Cp$）基于有关$p升Cp$以下后缀。使用此技术，可以在最坏的情况下完成在线p后缀树的构建$Ø（\mathrm{日志}ñ）$每个输入符号。在结果后缀树中搜索长度为m的模式需要$Ø（\mathrm{分}\{米\mathrm{日志}（|\mathrm{\Sigma }|+|\mathrm{\Pi }|），米+\mathrm{日志}ñ\}+米{\tau }_{\mathrm{\Pi }}+{Ť}_{ØCC}）$ 时间，地点 ${Ť}_{ØCC}$ 是模式的出现次数，并且 ${\tau }_{\mathrm{\Pi }}$取决于Π。对于恒定大小的Π，${\tau }_{\mathrm{\Pi }}=\mathrm{1个}$，对于多项式大小的Π， ${\tau }_{\mathrm{\Pi }}=\mathrm{日志}\mathrm{日志}|\mathrm{\Pi }|$，对于无界Π， ${\tau }_{\mathrm{\Pi }}=\mathrm{日志}|\mathrm{\Pi }|$。