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        <identifier>oai:drops-oai.dagstuhl.de:17975</identifier>
        <datestamp>2024-03-12T12:01:17Z</datestamp>
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          <dc:title>Suffix-Prefix Queries on a Dictionary</dc:title>
          <dc:creator>Loukides, Grigorios</dc:creator>
          <dc:creator>Pissis, Solon P.</dc:creator>
          <dc:creator>Thankachan, Sharma V.</dc:creator>
          <dc:creator>Zuba, Wiktor</dc:creator>
          <dc:subject>all-pairs suffix-prefix</dc:subject>
          <dc:subject>suffix-prefix queries</dc:subject>
          <dc:subject>internal pattern matching</dc:subject>
          <dc:description>In the all-pairs suffix-prefix (APSP) problem, we are given a dictionary R of k strings, S_1,…,S_k, of total length n, and we are asked to find the length SPL_{i,j} of the longest string that is both a suffix of S_i and a prefix of S_j, for all i,j ∈ [1,k]. APSP is a classic problem in string algorithms with many applications in bioinformatics. When all strings of the dictionary are over an integer alphabet of size σ ≤ n^𝒪(1), APSP can be solved in the optimal 𝒪(n+k²) time with the use of the generalized suffix tree of the dictionary [Gusfield et al., Inf. Process. Lett. 1992].&#13;
In many bioinformatics applications, such as in sequence assembly, the size k of dictionary R is very large. In particular, k² usually dominates n, and thus the k² factor is the bottleneck both in the time and in the space complexity of such applications. We thus initiate a holistic study on several data structure variants of APSP. In particular, we consider the following types of queries:  &#13;
- One-to-One(i,j): output SPL_{i,j}. &#13;
- One-to-All(i): output SPL_{i,j} for every j ∈ [1,k]. &#13;
- Report(i,𝓁): output all distinct j ∈ [1,k] such that SPL_{i,j} ≥ 𝓁, where 𝓁 ≥ 0 is an integer. &#13;
- Count(i,𝓁): output the number of distinct j ∈ [1,k] such that SPL_{i,j} ≥ 𝓁, where 𝓁 ≥ 0 is an integer. &#13;
- Top(i,K): output K distinct j ∈ [1,k] with the highest values of SPL_{i,j} breaking ties arbitrarily.&#13;
We assume the standard word RAM model of computation with word size w = Ω(log n) and an integer alphabet of size σ ≤ n^𝒪(1). We show the following upper bounds:&#13;
&#13;
Query           | Space (words) | Query time                | Note&#13;
One-to-One(i,j) | 𝒪(n)          | 𝒪(log log k)              | Theorem 11&#13;
One-to-All(i)   | 𝒪(n)          | 𝒪(k)                      | Theorem 14&#13;
Report(i,𝓁)     | 𝒪(n)          | 𝒪(log n/log log n+output) | Theorem 19(i)&#13;
Count(i,𝓁)      | 𝒪(n)          | 𝒪(log n/log log n)        | Theorem 19(ii)&#13;
Top(i,K)        | 𝒪(n)          | 𝒪(log² n/log log n+K)     | Theorem 22&#13;
&#13;
We also present efficient algorithms for constructing these data structures.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Grigorios Loukides and Solon P. Pissis and Sharma V. Thankachan and Wiktor Zuba</dc:contributor>
          <dc:date>2023</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 259, 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/LIPIcs.CPM.2023.21</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-179757</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2023.21</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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