1/29/2024 0 Comments Nifty synonym![]() Let’s apply this algorithm to our trie data structure example and see how it goes:Īs we can see, the system successfully finds the two matching phrases, “same family” and “separate existence”. In this case the text indicator is not moved forward, since the last word could be the beginning of a new branch. after a series of matches a non-match is encountered before the branch ends. One exception to the movement of the text indicator is when a partial match is found, i.e. The trie indicator returns to start in two cases: When it reaches the end of a branch, which means a search phrase has been found, or when it encounters a non-matching word, in which case no match has been found. The text indicator simply moves forward, while the trie indicator traverses the trie depth-wise, following a trail of matching words. The two indicators move along together, word by word. Our algorithm will have two indicators (pointers, if you like), one starting at the root, or “start” node in our trie structure, and the other at the first word in the text body. The European languages are members of the same family. Later, the user of our software presents it with a file containing the following text: So, we start by building an index, in the form of a trie: Remember that we know our search phrases beforehand. Text Search AlgorithmĪs a simple example, let’s assume the following search phrases: In a trie, however, the number of child nodes is dictated by the sequence it is representing, and in the case of readable/meaningful text, the number of children is usually high. As such, a ternary tree has the traversal complexity of O(log 3n). Traversing a binary tree has the complexity of O(log 2n), since each node branches into two, cutting the remaining traversal in half. To make it easier to grasp for the purposes of this trie tutorial, let’s imagine a binary tree. The advantage of a trie is that it significantly cuts search time. So, if we can combine all the search phrases into one trie, where each node contains a word, we will have the phrases laid out in a structure where simply tracing from the root downwards, via any path, yields a valid search phrase. In short, a trie is a special tree, capable of storing a sequence of values in such a way that tracing the path from the root to any node yields a valid subset of that sequence. Toptal’s previous tutorial on tries provides an excellent introduction to how they are structured and used. This versatile data structure is usually overlooked and not as famous as other tree-related structures when it comes to search problems. This requires search phrases to be indexed in a structure that can be transversed linearly, in parallel with the text body, in one pass, achieving a final complexity of O(n).Ī data structure that is especially well-suited for just this scenario is the trie. ![]() The best possible solution would be one that traverses the text body only once. But the reduction in runtime achieved by this approach (total runtime of 20 minutes assuming perfect division over 4 processors/cores) does not justify the added complexity to coding/debugging. For example, the search process can be partitioned and parallelized on multiple processors/cores. ![]() The previous scenario can be enhanced in a couple of ways. Running this code on my development machine against a test sample, I got a runtime of 1 hour 14 minutes – far beyond the time you need to grab a cup of coffee, get up and stretch, or any other excuse developers use to skip work. String text_body = File.ReadAllText("body.txt") įoreach (String phrase in search_phrases) The (likely only) upside of a direct approach that it is simple to implement, as apparent in the following C# snippet: String search_phrases = File.ReadAllLines ("terms.txt") Repeating that for m search phrases leads to the awful O(m * n). Searching for a string inside another has the complexity O(n). The most basic approach is to loop through the search phrases, and search through the text each phrase, one by one. Another example is traversing a large collection of judicial precedents and extracting the laws they reference. A real world application for this scenario is matching a number of medical theses against a list of medical conditions and finding out which theses discuss which conditions.
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