trie

简明释义

英[traɪ]美[traɪ]

n. 特里结构；单词查找树

英英释义

单词用法

同义词

反义词

例句

1.Why does each node in this Trie contain a linked list?

为什么每个节点在这种树包含一个链接列表？

2.The efficient method for collecting trie information to be measured is one of the main factors that ensure the measuring system with high precision and efficiency.

在检测系统中，有效的被测信息提取方法是保证检测具有高精度和高效率的主要因素之一。

3.Trie application offers more efficient means to measure flux of well and well with injection of polymer.

该仪器的应用为油水井及注聚合物井的流量测试提供了更为有效的手段和方法。

4.A trie (the name comes from the word retrieval) is made up of a series of nodes where the descendants of a given node have the same prefix of the string associated with that node.

trie(此名源于单词retrieval)由一系列的节点组成，其中，一个给定节点的后代具有与该节点关联的相同的字符串前缀。

5.A tdouble is a double represented internally using a Trie structure.

一个tdouble就是在内部使用Trie结构表示的一个double。

6.We implement a program for lexicon management base on the improved Double-Array Trie and compare it with other index mechanisms.

我们基于该优化算法实现了一个词典管理程序，并与利用其他索引机制的词典进行了实验对比。

7.When building a dictionary application, using a trie allows for quick lookups.

在构建字典应用程序时，使用字典树可以实现快速查找。

8.A trie can be used to implement a spell checker by storing valid words.

可以使用字典树实现拼写检查器，通过存储有效单词。

9.In autocomplete features, a trie helps suggest words based on the user's input.

在自动完成特性中，字典树根据用户的输入帮助建议单词。

10.The search algorithm uses a trie to efficiently store and retrieve strings.

搜索算法使用字典树来高效存储和检索字符串。

11.The trie structure is particularly useful for prefix-based searches.

字典树结构对于基于前缀的搜索特别有用。

作文

In the world of computer science, data structures play a crucial role in optimizing the efficiency of algorithms. One such data structure that has gained significant attention is the trie. A trie, pronounced as 'try', is a specialized tree-like structure used primarily for storing strings or sequences of characters. Its unique design allows for fast retrieval and insertion of words, making it an invaluable tool for applications such as autocomplete systems and spell checkers.The fundamental concept behind a trie is that it organizes data in a way that each node represents a character of a string. Unlike traditional binary trees, where each node has at most two children, a trie can have multiple children. This characteristic enables it to efficiently store a large set of strings by sharing common prefixes. For example, if we want to store the words 'cat', 'car', and 'cart', instead of storing them separately, a trie would allow us to share the common prefix 'ca' among these words. The structure of a trie consists of nodes, where each node contains links to its children and a boolean value indicating whether it marks the end of a valid word. When inserting a word into a trie, we start from the root node and traverse down the tree according to the characters of the word. If a character's corresponding child node does not exist, we create a new node. This process continues until we have inserted all characters of the word, marking the final node as a valid word.One of the most significant advantages of using a trie is its ability to perform prefix-based searches efficiently. This feature is particularly useful in applications like search engines and predictive text input, where users may want suggestions based on the initial letters they type. For instance, if a user types 'ca', the trie can quickly return all words that start with this prefix, such as 'cat', 'car', and 'cart'. This capability is achieved without needing to scan through all the stored words, which significantly reduces search time.Moreover, a trie can also be utilized for tasks beyond simple word storage. It can be employed in algorithms for finding the longest common prefix or even in solving problems related to string matching. The efficiency of a trie lies in its time complexity; both insertion and search operations can be performed in O(m) time, where m is the length of the word being inserted or searched. This performance is particularly beneficial when dealing with large datasets.However, it is essential to note that while a trie offers numerous advantages, it also comes with some drawbacks. The primary concern is the memory usage, as a trie can consume more memory than other data structures due to the overhead of storing multiple pointers for each node. This aspect should be considered when deciding whether to implement a trie for a specific application.In conclusion, the trie is a powerful data structure that excels in storing and retrieving strings efficiently. Its ability to handle prefix searches makes it ideal for various applications in computer science, particularly in areas involving text processing. Understanding the workings of a trie not only enhances our knowledge of data structures but also equips us with the tools to tackle complex problems in software development. As technology continues to evolve, the relevance of the trie will undoubtedly remain significant, making it a topic worth exploring further in the field of computer science.

在计算机科学的世界中，数据结构在优化算法的效率方面起着至关重要的作用。其中一种备受关注的数据结构是trie。trie（发音为“try”）是一种专门的树状结构，主要用于存储字符串或字符序列。它独特的设计使得快速检索和插入单词成为可能，这使其成为自动补全系统和拼写检查器等应用中不可或缺的工具。trie的基本概念是以一种方式组织数据，使每个节点代表字符串的一个字符。与传统的二叉树不同，二叉树每个节点最多有两个子节点，而trie可以有多个子节点。这一特性使其能够通过共享公共前缀来高效地存储大量字符串。例如，如果我们想存储单词“cat”、“car”和“cart”，那么在trie中，我们可以共享这些单词之间的公共前缀“ca”。trie的结构由节点组成，每个节点包含指向其子节点的链接和一个布尔值，指示是否标记为有效单词的结尾。在将单词插入trie时，我们从根节点开始，根据单词的字符遍历树。如果某个字符对应的子节点不存在，我们就创建一个新节点。这个过程一直持续到我们插入了单词的所有字符，并将最终节点标记为有效单词。使用trie的一个显著优点是它能够高效地执行基于前缀的搜索。这一特性在搜索引擎和预测文本输入等应用中尤为有用，因为用户可能希望根据他们输入的首字母获得建议。例如，如果用户输入“ca”，trie可以快速返回所有以该前缀开头的单词，例如“cat”、“car”和“cart”。这一能力的实现无需扫描所有存储的单词，从而显著减少了搜索时间。此外，trie还可以用于超出简单单词存储的任务。它可以用于查找最长公共前缀的算法，甚至解决与字符串匹配相关的问题。trie的效率体现在其时间复杂度上；插入和搜索操作都可以在O(m)时间内完成，其中m是被插入或搜索的单词的长度。当处理大型数据集时，这种性能尤其有益。然而，需要注意的是，虽然trie提供了许多优点，但也存在一些缺点。主要问题是内存使用，因为由于每个节点存储多个指针的开销，trie可能消耗比其他数据结构更多的内存。在决定是否为特定应用实现trie时，应考虑这一点。总之，trie是一种强大的数据结构，在高效存储和检索字符串方面表现出色。它处理前缀搜索的能力使其成为计算机科学中各种应用的理想选择，特别是在涉及文本处理的领域。理解trie的工作原理不仅增强了我们对数据结构的知识，还使我们具备了在软件开发中解决复杂问题的工具。随着技术的不断发展，trie的相关性无疑将继续保持重要性，使其在计算机科学领域值得进一步探索的话题。