18,318 research outputs found
Average-Case Optimal Approximate Circular String Matching
Approximate string matching is the problem of finding all factors of a text t
of length n that are at a distance at most k from a pattern x of length m.
Approximate circular string matching is the problem of finding all factors of t
that are at a distance at most k from x or from any of its rotations. In this
article, we present a new algorithm for approximate circular string matching
under the edit distance model with optimal average-case search time O(n(k + log
m)/m). Optimal average-case search time can also be achieved by the algorithms
for multiple approximate string matching (Fredriksson and Navarro, 2004) using
x and its rotations as the set of multiple patterns. Here we reduce the
preprocessing time and space requirements compared to that approach
Searching by approximate personal-name matching
We discuss the design, building and evaluation of a method to access theinformation of a person, using his name as a search key, even if it has deformations. We present a similarity function, the DEA function, based
on the probabilities of the edit operations accordingly to the involved
letters and their position, and using a variable threshold. The efficacy
of DEA is quantitatively evaluated, without human relevance judgments,
very superior to the efficacy of known methods. A very efficient
approximate search technique for the DEA function is also presented
based on a compacted trie-tree structure.Postprint (published version
Improved Algorithms for Approximate String Matching (Extended Abstract)
The problem of approximate string matching is important in many different
areas such as computational biology, text processing and pattern recognition. A
great effort has been made to design efficient algorithms addressing several
variants of the problem, including comparison of two strings, approximate
pattern identification in a string or calculation of the longest common
subsequence that two strings share.
We designed an output sensitive algorithm solving the edit distance problem
between two strings of lengths n and m respectively in time
O((s-|n-m|)min(m,n,s)+m+n) and linear space, where s is the edit distance
between the two strings. This worst-case time bound sets the quadratic factor
of the algorithm independent of the longest string length and improves existing
theoretical bounds for this problem. The implementation of our algorithm excels
also in practice, especially in cases where the two strings compared differ
significantly in length. Source code of our algorithm is available at
http://www.cs.miami.edu/\~dimitris/edit_distanceComment: 10 page
Fast and Compact Regular Expression Matching
We study 4 problems in string matching, namely, regular expression matching,
approximate regular expression matching, string edit distance, and subsequence
indexing, on a standard word RAM model of computation that allows
logarithmic-sized words to be manipulated in constant time. We show how to
improve the space and/or remove a dependency on the alphabet size for each
problem using either an improved tabulation technique of an existing algorithm
or by combining known algorithms in a new way
siEDM: an efficient string index and search algorithm for edit distance with moves
Although several self-indexes for highly repetitive text collections exist,
developing an index and search algorithm with editing operations remains a
challenge. Edit distance with moves (EDM) is a string-to-string distance
measure that includes substring moves in addition to ordinal editing operations
to turn one string into another. Although the problem of computing EDM is
intractable, it has a wide range of potential applications, especially in
approximate string retrieval. Despite the importance of computing EDM, there
has been no efficient method for indexing and searching large text collections
based on the EDM measure. We propose the first algorithm, named string index
for edit distance with moves (siEDM), for indexing and searching strings with
EDM. The siEDM algorithm builds an index structure by leveraging the idea
behind the edit sensitive parsing (ESP), an efficient algorithm enabling
approximately computing EDM with guarantees of upper and lower bounds for the
exact EDM. siEDM efficiently prunes the space for searching query strings by
the proposed method, which enables fast query searches with the same guarantee
as ESP. We experimentally tested the ability of siEDM to index and search
strings on benchmark datasets, and we showed siEDM's efficiency.Comment: 23 page
Similarity Matching Techniques For Fault Diagnosis In Automotive Infotainment Electronics
Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed
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