We formalize and examine the online Dictionary Recognition with One Gap problem (DROG) which is the following. Preprocess a dictionary D of d patterns each containing a special gap symbol that matches any string, so that given a text arriving online a character at a time, all patterns from D which are suffixes of the text that has arrived so far and have not been reported yet, are reported before the next character arrives. The gap symbols are associated with bounds determining possible lengths of matching strings. Online DROG captures the difficulty in a bottleneck procedure for cyber-security, as many digital signatures of viruses manifest themselves as patterns with a single gap.

Following the work on the closely related online Dictionary Matching with One Gap problem (DMOG), we provide algorithms whose time cost depends linearly on $\mathit{\delta }(G_D)$, where $G_D$ is a bipartite graph that captures the structure of D and $\mathit{\delta }(G_D)$ is the degeneracy of this graph. These algorithms are of practical interest since although $\mathit{\delta }(G_D)$ can be as large as $\sqrt{d}$, and even larger if $G_D$ is a multi-graph, it is typically a small constant in practice.

我们对以下的在线字典识别进行正式化和检查，其中包括“一字不漏”问题（DROG）。预处理d个模式的字典D，每个模式包含一个与任何字符串匹配的特殊间隙符号，以便给定一次在线到达文本的字符，来自D的所有模式都是迄今尚未到达的文本后缀已报告，在下一个字符到达之前报告。间隙符号与确定匹配字符串可能长度的界限相关联。在线DROG捕获了网络安全瓶颈过程中的困难，因为许多病毒的数字签名将自身表现为具有单个间隙的模式。

在密切相关的在线字典匹配与一缺口问题（DMOG）方面的工作之后，我们提供了算法，其时间成本线性依赖于 $\mathit{\delta }（G_d）$，在哪里 $G_d$是捕获D和D的结构的二部图$\mathit{\delta }（G_d）$是该图的简并性。这些算法具有实际意义，因为尽管$\mathit{\delta }（G_d）$ 可以大到 $\sqrt{d}$，甚至更大 $G_d$ 是一个多图，实际上它通常是一个小的常数。