computer science

Krauss wildcard-matching algorithm

Krauss wildcard-matching algorithm is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Krauss wildcard-matching algorithm rather than just read about it. In short: In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.

Key takeaways

Krauss wildcard-matching algorithm belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Krauss wildcard-matching algorithm to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Krauss wildcard-matching algorithm from memory before moving on to harder problems.

Reference excerpt

In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.

History The algorithm is based on a history of development, correctness and performance testing, and programmer feedback that began with an unsuccessful search for a reliable non-recursive algorithm for matching wildcards. An initial algorithm, implemented in a single while loop, quickly prompted comments from software developers, leading to improvements. Ongoing comments and suggestions culminated in a revised algorithm still implemented in a single while loop but refined based on a collection of test cases and a performance profiler. The experience tuning the single while loop using the profiler prompted development of a two-loop strategy that achieved further performance gains, particularly in situations involving empty input strings or input containing no wildcard characters. The two-loop algorithm is available for use by the open-source software development community, under the terms of the Apache License v. 2.0, and is accompanied by test case code.

Usage The algorithm made available under the Apache license is implemented in both pointer-based C++ and portable C++ (implemented without pointers). The test case code, also available under the Apache license, can be applied to any algorithm that provides the pattern matching operations below. The implementation as coded is unable to handle multibyte character sets and poses problems when the text being searched may contain multiple incompatible character sets.

Pattern matching operations The algorithm supports three pattern matching operations:

A one-to-one match is performed between the pattern and the source to be checked for a match, with the exception of asterisk (*) or question mark (?) characters in the pattern. An asterisk (*) character matches any sequence of zero or more characters. A question mark (?) character matches any single character.

Examples *foo* matches any string containing "foo". mini* matches any string that begins with "mini" (including the string "mini" itself). ???* matches any string of three or more letters.

Applications The original algorithm has been ported to the DataFlex programming language by Larry Heiges for use with Data Access Worldwide code library. It has been posted on GitHub in modified form as part of a log file reader. The 2014 algorithm is part of the Unreal Model Viewer built into the Epic Games Unreal Engine game engine.

See also pattern matching glob (programming) wildmat

References

Worked examples

Example 1 — a first encounter with Krauss wildcard-matching algorithm

Start with the simplest possible case. Write down what Krauss wildcard-matching algorithm claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Krauss wildcard-matching algorithm before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Krauss wildcard-matching algorithm ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Krauss wildcard-matching algorithm

In research: Krauss wildcard-matching algorithm appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Krauss wildcard-matching algorithm in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Krauss wildcard-matching algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Pattern matching, so understanding it makes those chapters shorter.
In everyday life: Look for Krauss wildcard-matching algorithm outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Krauss wildcard-matching algorithm in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Krauss wildcard-matching algorithm means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Krauss wildcard-matching algorithm out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Krauss wildcard-matching algorithm in simple terms?

Why does Krauss wildcard-matching algorithm matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Krauss wildcard-matching algorithm?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Krauss wildcard-matching algorithm.