Special Issue "String Matching and Its Applications"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 31 October 2019.
With the rapid growth of available data in almost all fields, there is large demand for efficient pattern-matching algorithms. We invite you to submit your latest research in the area of string matching (single or multiple, on-line or off-line, exact or approximate, in uncompressed or compressed form) describing new data structures and/or new algorithms. High-quality papers are solicited to address both theoretical and practical issues of string matching including, but not restricted to, natural language processing, text mining, bioinformatics (DNA, RNA, protein sequences), chemoinformatics, intrusion detection, security, plagiarism detection, digital forensics, video retrieval, and music analysis.
Prof. Dr. Thierry Lecroq
Prof. Dr. Simone Faro
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- exact string matching
- approximate string matching
- multiple string matching
- development and analysis of algorithms
- text indexing
- data structures for string matching
- information retrieval
- searching for regularities
- string matching in natural language processing
- string matching in bioinformatics
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Muhammed Oguzhan Kulekci
Abstract: Ambiguous codes can be defined as the codes that cannot be uniquely decoded without additional disambiguation information. These are mainly the class of non-prex-free codes, where a codeword can be a prex of other(s), and thus, the codeword boundary information is essential for correct decoding. Although that ambiguity can be seen as a disadvantage in terms of compression, actually there are cases where that intrinsic property can make sense. We present first an efficient ambiguous coding/decoding system, which is publicly available at GitHub. Based on that implementation we provide several applications where such a representation helps us to provide privacy, particularly on massive high entropy volumes.
2. Title: Tandem Repeat Discovery Using A Heuristically Derived Threshold for A String Correspondence Metric
Authors: Bruce Watson and Derrick Courie
Abstract: It is challenging to select appropriate parameter settings that drive approximate tandem repeat detecting algorithms. This study shows how that challenge can be mitigated by deriving a threshold value of a string correspondence metric based on Levenshtein distances. It is shown how an algorithm can use such a threshold value to effectively identify repeat elements. The threshold value is empirically derived from a recall-precision analysis carried out on synthetic data. It is shown that the performance of an algorithm relying on this threshold value compares very favourably to other published tandem repeat discovery algorithms and sometimes outperforms them.