2,287 research outputs found
A Bloom filter based semi-index on -grams
We present a simple -gram based semi-index, which allows to look for a
pattern typically only in a small fraction of text blocks. Several space-time
tradeoffs are presented. Experiments on Pizza & Chili datasets show that our
solution is up to three orders of magnitude faster than the Claude et al.
\cite{CNPSTjda10} semi-index at a comparable space usage
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
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
ZASTOSOWANIE ALGORYTMU WYSZUKIWANIA WIELU WZORCÓW OPARTEGO O TECHNIKĘ Q-GRAMÓW DO WYSZUKIWANIA PRZYBLIŻONEGO
We consider the application of multiple pattern matching (Multi AOSO on q-Grams) algorithm for approximate pattern matching. We propose the on-line approach which translates the problem from approximate pattern matching into a multiple pattern one (called partitioning into exact search). Presented solution allows relatively fast search multiple patterns in text with given k-differences(or mismatches). This paper presents comparison of solution based on MAG algorithm, and [4]. Experiments on DNA, English, Proteins and XML texts with up to k errors show that the new proposed algorithm achieves relatively good results in practical use.Rozważamy zastosowanie algorytmu wyszukiwania wielu wzorców (Multi AOSO on q-Grams) do wyszukiwania przybliżonego. Proponujemy rozwiązanie on-line, upraszczające problem wyszukiwania przybliżonego do wyszukiwania wielu wzorców. Zaprezentowane rozwiązanie umożliwia relatywnie szybko wyszukiwać wiele wzorców dla odległości Levenshteina (lub Hamminga) z ograniczeniem do k. W artykule porównane jest rozwiązanie oparte na algorytmie MAG oraz [4]. Badania eksperymentalne przeprowadzone na zbiorach DNA, English, Proteins and XML z różnymi wartościami k wykazały, że zaproponowany algorytm osiąga relatywnie dobre wyniki w praktycznym zastosowaniu
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