Episode matching

Das, Gautam; Fleischer, Rudolf; Gąsieniec, Leszek; Gunopulos, Dimitrios; Kärkkäinen, Juha

doi:10.1007/3-540-63220-4_46

Cited by 47 publications

(52 citation statements)

References 7 publications

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“…8a (the vertical axis is in logarithmic scale). 6 In all cases, SEQ(A) outperforms all other methods, indicating that its approximation technique is effective in reducing the Data Scan cost through the reduction of falsedrops.…”

Section: Resultsmentioning

confidence: 86%

“…Some examples of indexing methods developed for string matching are suffix trees [9], suffix array [19], Q-grams [10], and Qsamples [28]. Also related is the work on episode matching, with which only insertions in the text are permitted [6,36]. However, the unordered nature of sequence elements and the freedom to represent their order in the sequence makes the techniques developed for string and episode matching inappropriate for optimizing pattern queries.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient storage and querying of sequential patterns in database systems⋆

Νανόπουλος

Zakrzewicz

Morzy

et al. 2003

Information and Software Technology

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The number of patterns discovered by data mining can become tremendous, in some cases exceeding the size of the original database. Therefore, there is a requirement for querying previously generated mining results or for querying the database against discovered patters. In this paper, we focus on developing methods for the storage and querying of large collections of sequential patterns. We describe a family of algorithms, which address the problem of considering the ordering among elements, that is crucial when dealing with sequential patterns. Moreover, we take into account the fact that the distribution of elements within sequential patterns is highly skewed, to propose a novel approach for the effective encoding of patterns. Experimental results, which examine a variety of factors, illustrate the efficiency of the proposed method. q

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Section: Resultsmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Efficient storage and querying of sequential patterns in database systems⋆

Νανόπουλος

Zakrzewicz

Morzy

et al. 2003

Information and Software Technology

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show abstract

“…The LCS distance (also called the indel distance) [90,10], the Levenshtein distance (also called the indelsub distance) [78], and the episode distance [36] correspond respectively to…”

Section: Example 14mentioning

confidence: 99%

“…The local subsequence recognition problem is considered by Das et al [36] as the "episode matching problem". For both the minimal-window and fixed-window versions, they give an algorithm running in time O mn log 2 n for a constant-size alphabet, which can be modified to an algorithm running in time O mn(log log n) 2 log 2 n for an unbounded-size alphabet.…”

Section: Approximate Pattern Matchingmentioning

confidence: 99%

Semi-local String Comparison: Algorithmic Techniques and Applications

Tiskin

2008

Math.comput.sci.

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The longest common subsequence (LCS) problem is a classical problem in computer science. The semi-local LCS problem is a generalisation of the LCS problem, arising naturally in the context of string comparison. In this work, we present a number of algorithmic techniques related to the semi-local LCS problem, and give a number of algorithmic applications of these techniques. Summarising the presented results, we conclude that semilocal string comparison turns out to be a useful algorithmic plug-in, which unifies, and often improves on, a number of previous approaches to various substring-and subsequence-related problems.

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“…More recently, an efficient algorithm to discover the best episode patterns was proposed in [9]. An episode pattern p, k , where p is a string and k is an integer, is said to match a string w if p is a subsequence of a substring u of w with |u| ≤ k [14,6,20]. The problem to find the best episode patterns is also known to be NP-hard.…”

Section: Introductionmentioning

confidence: 99%

Discovering Best Variable-Length-Don’t-Care Patterns

et al. 2002

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Abstract. A variable-length-don't-care pattern (VLDC pattern)is an element of set Π = (Σ ∪ { }) * , where Σ is an alphabet and is a wildcard matching any string in Σ * . Given two sets of strings, we consider the problem of finding the VLDC pattern that is the most common to one, and the least common to the other. We present a practical algorithm to find such best VLDC patterns exactly, powerfully sped up by pruning heuristics. We introduce two versions of our algorithm: one employs a pattern matching machine (PMM) whereas the other does an index structure called the Wildcard Directed Acyclic Word Graph (WDAWG). In addition, we consider a more generalized problem of finding the best pair q, k , where k is the window size that specifies the length of an occurrence of the VLDC pattern q matching a string w. We present three algorithms solving this problem with pruning heuristics, using the dynamic programming (DP), PMMs and WDAWGs, respectively. Although the two problems are NP-hard, we experimentally show that our algorithms run remarkably fast.

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Episode matching

Cited by 47 publications

References 7 publications

Efficient storage and querying of sequential patterns in database systems⋆

Efficient storage and querying of sequential patterns in database systems⋆

Semi-local String Comparison: Algorithmic Techniques and Applications

Discovering Best Variable-Length-Don’t-Care Patterns

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