In computer science, string searching algorithms, sometimes called string matching algorithms, are an important class of string algorithms that try to find a place where one or several strings (also called patterns) are found within a larger string or text.
Let Σ be an alphabet (finite set). Formally, both the pattern and searched text are vectors of elements of Σ. The Σ may be a usual human alphabet (for example, the letters A through Z in the Latin alphabet). Other applications may use binary alphabet (Σ = {0,1}) or DNA alphabet (Σ = {A,C,G,T}) in bioinformatics.
In practice, how the string is encoded can affect the feasible string search algorithms. In particular if a variable width encoding is in use then it is slow (time proportional to N) to find the Nth character. This will significantly slow down many of the more advanced search algorithms. A possible solution is to search for the sequence of code units instead, but doing so may produce false matches unless the encoding is specifically designed to avoid it.
Basic classification
The various algorithms can be classified by the number of patterns each uses.
Single pattern algorithms
Let m be the length of the pattern and let n be the length of the searchable text.
| Algorithm | Preprocessing time | Matching time1 |
|---|---|---|
| Naïve string search algorithm | 0 (no preprocessing) | Θ((n−m) m) |
| Rabin–Karp string search algorithm | Θ(m) | average Θ(n+m), worst Θ((n−m) m) |
| Finite-state automaton based search | Θ(m |Σ|) | Θ(n) |
| Knuth–Morris–Pratt algorithm | Θ(m) | Θ(n) |
| Boyer–Moore string search algorithm | Θ(m + |Σ|) | Ω(n/m), O(nm) |
| Bitap algorithm (shift-or, shift-and, Baeza–Yates–Gonnet) | Θ(m + |Σ|) | O(mn) |
| Alpha Skip Search algorithm | Θ(mlogm) | Θ((n/m)logm) |
1Asymptotic times are expressed using O, Ω, and Θ notation
The Boyer–Moore string search algorithm has been the standard benchmark for the practical string search literature.[1]
The log base in Alpha Skip Search is the alphabet size.
Algorithms using a finite set of patterns
Algorithms using an infinite number of patterns
Naturally, the patterns can not be enumerated in this case. They are represented usually by a regular grammar or regular expression.
Other classification
Other classification approaches are possible. One of the most common uses preprocessing as main criteria.
| Text not preprocessed | Text preprocessed | |
|---|---|---|
| Patterns not preprocessed | Elementary algorithms | Index methods |
| Patterns preprocessed | Constructed search engines | Signature methods |
Naïve string search
A simple but inefficient way to see where one string occurs inside another is to check each place it could be, one by one, to see if it's there. So first we see if there's a copy of the needle in the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack; if not, we look starting at the third character, and so forth. In the normal case, we only have to look at one or two characters for each wrong position to see that it is a wrong position, so in the average case, this takes O(n + m) steps, where n is the length of the haystack and m is the length of the needle; but in the worst case, searching for a string like "aaaab" in a string like "aaaaaaaaab", it takes O(nm)
Finite state automaton based search
In this approach, we avoid backtracking by constructing a deterministic finite automaton (DFA) that recognizes stored search string. These are expensive to construct—they are usually created using the powerset construction—but are very quick to use. For example,
Stubs
Knuth–Morris–Pratt computes a DFA that recognizes inputs with the string to search for as a suffix, Boyer–Moore starts searching from the end of the needle, so it can usually jump ahead a whole needle-length at each step. Baeza–Yates keeps track of whether the previous j characters were a prefix of the search string, and is therefore adaptable to fuzzy string searching. The bitap algorithm is an application of Baeza–Yates' approach. Alpha Skip Search builds logM sized keys (log base is alphabet size), raising the effective alphabet size to M, for small alphabets or long patterns.
On average, Alpha Skip Search is the fastest of these algorithms on small alphabets. The Alpha Skip Search algorithm builds a position lookup table with logM sized keys of the pattern, in time MlogM, with log base alphabet size. With keys of size logM, the effective alphabet size is M, even for small alphabets. The main search then skips through the text by size M-logM+1, trying to match keys of size logM in the position lookup table. When a key is found at text position k, a naive pattern match is tried at text position k - key position. The expected run time is O( (N/M)logM ) even for alphabet size two, and so is much faster than Boyer-Moore on average, for small alphabets. See Combinatorial Pattern Matching 1998 for a more thorough discussion.
Index methods
Faster search algorithms are based on preprocessing of the text. After building a substring index, for example a suffix tree or suffix array, the occurrences of a pattern can be found quickly. As an example, a suffix tree can be built in time, and all occurrences of a pattern can be found in time under the assumption that the alphabet has a constant size and all inner nodes in the suffix tree knows what leafs are underneath them. The latter can be accomplished by running a DFS algorithm from the root of the suffix tree.
Other variants
Some search methods, for instance trigram search, are intended to find a "closeness" score between the search string and the text rather than a "match/non-match". These are sometimes called "fuzzy" searches.
See also
Academic conferences on text searching
- Combinatorial pattern matching (CPM), a conference on combinatorial algorithms for strings, sequences, and trees.
- String Processing and Information Retrieval (SPIRE), an annual symposium on string processing and information retrieval.
- Prague Stringology Conference (PSC), an annual conference on algorithms on strings and sequences.
- Competition on Applied Text Searching (CATS), an annual series of evaluations of text searching algorithms.
References
- ^ Hume; Sunday (1991). "Fast String Searching". Software: Practice and Experience. 21 (11): 1221–1248. doi:10.1002/spe.4380211105.
- ^ Melichar, Borivoj, Jan Holub, and J. Polcar. Text Searching Algorithms. Volume I: Forward String Matching. Vol. 1. 2 vols., 2005. http://stringology.org/athens/TextSearchingAlgorithms/.
- R. S. Boyer and J. S. Moore, A fast string searching algorithm, Carom. ACM 20, (10), 262–272(1977).
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Chapter 32: String Matching, pp.906–932.
External links
- Huge (maintained) list of pattern matching links Last updated:12/27/2008 20:18:38
- StringSearch – high-performance pattern matching algorithms in Java – Implementations of many String-Matching-Algorithms in Java (BNDM, Boyer-Moore-Horspool, Boyer-Moore-Horspool-Raita, Shift-Or)
- Exact String Matching Algorithms — Animation in Java, Detailed description and C implementation of many algorithms.
- Boyer-Moore-Raita-Thomas
- (PDF) Improved Single and Multiple Approximate String Matching
- Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features