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Author: search.filters.author.Khancome, ChouvalitHas files: No

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    A two-hashing table multiple string pattern matching algorithm
    (2013-11-04)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    ;
    Chanvarasuth, Pisit
    Multiple string pattern matching is one of many approaches to simultaneously search occurrences of a large number of patterns in a given text. In this paper, a new solution to this problem is presented by using two-hashing tables to minimize attempting times. This solution, called Two-Hashing Table Multiple String Patterns Matching Algorithm (Two-HT-MSPMA), is suitable for very long length of minimum pattern length. Its time complexity is more efficient than classic algorithms. Empirical results showed that its attempting times were less than of traditional algorithms. © 2013 IEEE.
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    New hashing-based multiple string pattern matching algorithms
    (2012-07-23)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    The paper presents three new algorithms for multiple string pattern matching using hashing tables: a suffix search (SS), a suffix-prefix search (SPS), and a suffix-middle-prefix search (SMPS). It takes O(|P|) time and space preprocessing where |P| is the sum of all pattern lengths. They search for a fixed-length pattern m in a text with length |t| takes O(|t| |P|) in the worst case, O(|t|) in an average case, and O(|t|/m) in the best case. Furthermore, their attempting times are less than of traditional algorithms. © 2012 IEEE.
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    Dynamic multiple pattern detection algorithm
    (2010-07-12)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    The multiple pattern matching has always been a significant principle for applying to a network security system. This principle is adapted to accommodate the target patterns to be detected in a pre-processing phase, and the objective text and the suspect patterns are simultaneously detected by comparing with the existing structure. This research article introduces a new algorithm of multi-string pattern matching by means of a new data structure called inverted lists. The inverted lists structure, inherited from the inverted index, is represented by the positions of characters which appear in the patterns. The new algorithm, which is more efficient time than the traditional algorithms, scans the given text in a single pass. More importantly, the structure of patterns is able to update the patterns over time. © 2010 IEEE.
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    Data structure for dynamic patterns
    (2010-01-01)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    String matching and dynamic dictionary matching are significant principles in computer science. These principles require an efficient data structure for accommodating the pattern or patterns to be searched for in a large given text. Moreover, in the dynamic dictionary matching, the structure is able to insert or delete the individual patterns over time. This research article introduces a new dynamic data structure named inverted lists for both principles. The inverted lists data structure, which is derived from the inverted index, is implemented by the perfect hashing idea. This structure focuses on the position of characters and provides a hashing table to store the string patterns. The new data structure is more time efficient than traditional structures. Also, this structure is faster to construct and consumes less memory than others.
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    Inverted lists string pattern matching
    (2009-11-12)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    This paper presents two algorithms of string pattern matching. These algorithms employ the inverted lists to accommodate the string pattern to be searched for. The first solution scans the text in a single pass for all occurrences of string pattern. The second solution, which improves the first one, takes the comparison times equal to the length of pattern plus the number of comparisons that lead to be mismatched. © 2009 IEEE.
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    Dynamic dictionary matching using inverted lists
    (2007-12-01)
    Khancome, Chouvalit
    ;
    Boonjing, Veera
    This paper proposes a new solution to the problem of dynamic dictionary matching. It employs inverted lists as data structures accommodating string patterns. The new solution takes (1) O(|P|) time for preprocessing, where |P| is a sum of the length of all patterns in set of pattern P; (2) O(|p|) time for insertion or deletion, where |p| is the length of pattern to be inserted or deleted; and (3) a search O(|t|+locc) time, where |t| is the length of input text and locc is the number of occurrences of matching between a character in the input text and in the inverted list.