Method and apparatus for suggesting completions for a partially entered data item based on previously-entered, associated data items - US Patent 5845300 Description. Description The present invention relates generally to the field of computer-based applications requiring data entry and more particularly to the field of improving the data entry process by automatically completing a partially entered data item with a matching data item from a list of previously entered data items. In the field of data processing systems, data is entered into databases for the purposes of rapid searching, retrieving or processing.
Since its inception, the field of data processing has been bottle-necked by the time consuming and human-error prone process of data entry. Therefore, it is desirable to improve the efficiency and reliability of entering data into a database system. One method to achieve this objective is the use of automatic completion algorithms to assist in the data entry process. Automatic data entry completion algorithms have appeared in various types of applications. The Operating Environment. Weblog: What Makes a Good Autocomplete? Redesign We’ve been working on a problem for the past couple of weeks: an optimal autocomplete algorithm. Many of our users have said that while Enso is great, it requires a bit too much typing. We’re inclined to agree. Yet, figuring out the best solution is tricky: there are more autocomplete algorithms than bones in a school of lionfish.
The Problem Let’s start by defining the problem. Enso currently uses a slightly modified version of the “Obvious Solution”. Unfortunately, this solution has much more typing than necessary. So we come to the question: how can we implement an autocomplete solution that allows Enso users to use fewer keystrokes, without losing the memorability of semantic language that makes Enso powerful? The Rubric Like all interesting problems, the constraints put upon a good autocomplete algorithm are often opposing. Keystroke Efficient. Worst Case: 9% Unix-Style: 27% Current Enso Behavior: 38% Transparent The autocomplete mechanism should be transparent to the user.
Trie. K-ary search tree data structure Unlike a binary search tree, nodes in the trie do not store their associated key. Instead, a node's position in the trie defines the key with which it is associated. This distributes the value of each key across the data structure, and means that not every node necessarily has an associated value. All the children of a node have a common prefix of the string associated with that parent node, and the root is associated with the empty string.
This task of storing data accessible by its prefix can be accomplished in a memory-optimized way by employing a radix tree. Though tries can be keyed by character strings, they need not be. The same algorithms can be adapted for ordered lists of any underlying type, e.g. permutations of digits or shapes. History, etymology, and pronunciation [edit] The null links within the children of a node emphasize the following characteristics:[14]: 734 [5]: 336 A basic structure type of nodes in the trie is as follows; time, where and.
Levenshtein distance. Several definitions of edit distance exist, using different sets of string operations. One of the most common variants is called Levenshtein distance, named after the Soviet Russian computer scientist Vladimir Levenshtein. In this version, the allowed operations are the removal or insertion of a single character, or the substitution of one character for another. Levenshtein distance may also simply be called "edit distance", although several variants exist.:32 Formal definition and properties[edit] Given two strings a and b on an alphabet Σ (e.g. the set of ASCII characters, the set of bytes [0..255], etc.), the edit distance d(a, b) is the minimum-weight series of edit operations that transforms a into b.
Insertion of a single symbol. Deletion of a single symbol changes uxv to uv (x→ε). Substitution of a single symbol x for a symbol y ≠ x changes uxv to uyv (x→y). Additional primitive operations have been suggested. Example[edit] for a total cost/distance of 5 operations. Properties[edit] Autocomplete. Autocomplete, or word completion, is a feature provided by many web browsers, e-mail programs, search engine interfaces, source code editors, database query tools, word processors, and command line interpreters.
Autocomplete is also available for, or already integrated in, general text editors. Autocomplete involves the program predicting a word or phrase that the user wants to type in without the user actually typing it in completely. This feature is effective when it is easy to predict the word being typed based on those already typed, such as when there are a limited number of possible or commonly used words (as is the case with e-mail programs, web browsers, or command line interpreters), or when editing text written in a highly structured, easy-to-predict language (as in source code editors).
It can also be very useful in text editors, when the prediction is based on a list of words in one or more languages. Definition[edit] Original purpose[edit] Description[edit] Shorthand[edit] Damn Cool Algorithms: Levenshtein Automata. Posted by Nick Johnson | Filed under python, coding, tech, damn-cool-algorithms In a previous Damn Cool Algorithms post, I talked about BK-trees, a clever indexing structure that makes it possible to search for fuzzy matches on a text string based on Levenshtein distance - or any other metric that obeys the triangle inequality. Today, I'm going to describe an alternative approach, which makes it possible to do fuzzy text search in a regular index: Levenshtein automata.
Introduction The basic insight behind Levenshtein automata is that it's possible to construct a Finite state automaton that recognizes exactly the set of strings within a given Levenshtein distance of a target word. We can then feed in any word, and the automaton will accept or reject it based on whether the Levenshtein distance to the target word is at most the distance specified when we constructed the automaton. Of course, if that were the only benefit of Levenshtein automata, this would be a short article. Indexing. Damn Cool Algorithms, Part 1: BK-Trees. Posted by Nick Johnson | Filed under coding, tech, damn-cool-algorithms This is the first post in (hopefully) a series of posts on Damn Cool Algorithms - essentially, any algorithm I think is really Damn Cool, particularly if it's simple but non-obvious.
BK-Trees, or Burkhard-Keller Trees are a tree-based data structure engineered for quickly finding near-matches to a string, for example, as used by a spelling checker, or when doing a 'fuzzy' search for a term. The aim is to return, for example, "seek" and "peek" if I search for "aeek". What makes BK-Trees so cool is that they take a problem which has no obvious solution besides brute-force search, and present a simple and elegant method for speeding up searches substantially.
BK-Trees were first proposed by Burkhard and Keller in 1973, in their paper "Some approaches to best match file searching". The only copy of this online seems to be in the ACM archive, which is subscription only. Previous PostNext Post. Algorithms. Algorithm Tutorials. Disjoint-set Data Structures By vlad_DTopCoder Member Introduction Many times the efficiency of an algorithm depends on the data structures used in the algorithm. A wise choice in the structure you use in solving a problem can reduce the time of execution, the time to implement the algorithm and the amount of memory used.
During SRM competitions we are limited to a time limit of 2 seconds and 64 MB of memory, so the right data structure can help you remain in competition. While some Data Structures have been covered before, in this article we'll focus on data structures for disjoint sets. The problem Let’s consider the following problem: In a room are N persons, and we will define two persons are friends if they are directly or indirectly friends. If A is a friend with B, and B is a friend with C, then A is a friend of C too. In the end there are 2 groups of friends: one group is {1, 2, 4, 5}, the other is {3}. Let’s see how things will work with sets for the example of the problem.