Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Michelle Sweering"'
Autor:
Esteban Gabory, Moses Njagi Mwaniki, Nadia Pisanti, Solon P. Pissis, Jakub Radoszewski, Michelle Sweering, Wiktor Zuba
Publikováno v:
Frontiers in Bioinformatics, Vol 4 (2024)
IntroductionAn elastic-degenerate (ED) string is a sequence of sets of strings. It can also be seen as a directed acyclic graph whose edges are labeled by strings. The notion of ED strings was introduced as a simple alternative to variation and seque
Externí odkaz:
https://doaj.org/article/0fbb0868164048a0936e75501aa9841c
Autor:
Grigorios Loukides, Alessio Conte, Huiping Chen, Roberto Grossi, Michelle Sweering, Solon P. Pissis
Publikováno v:
KDD 2021-27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KDD 2021-27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2021, Virtual Event Singapore, Singapore. pp.117-126, ⟨10.1145/3447548.3467365⟩
KDD
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021, Virtual Event Singapore, Singapore. pp.117-126, ⟨10.1145/3447548.3467365⟩
KDD 2021-27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2021, Virtual Event Singapore, Singapore. pp.117-126, ⟨10.1145/3447548.3467365⟩
KDD
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021, Virtual Event Singapore, Singapore. pp.117-126, ⟨10.1145/3447548.3467365⟩
International audience; A-truss is a graph such that each edge is contained in at least − 2 triangles. This notion has attracted much attention, because it models meaningful cohesive subgraphs of a graph. We introduce the problem of identifying a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::104d6b08b1974e3f45c8b224fbfbf0ac
https://hal.inria.fr/hal-03498386/file/3447548.3467365.pdf
https://hal.inria.fr/hal-03498386/file/3447548.3467365.pdf
Autor:
Michelle Sweering, Giulia Bernardini, Solon P. Pissis, Huiping Chen, Roberto Grossi, Nadia Pisanti, Alessio Conte, Grigorios Loukides, Giovanna Rosone
Publikováno v:
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2020, 15 (1), pp.1-34. ⟨10.1145/3418683⟩
ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 15 (1), pp.1-34. ⟨10.1145/3418683⟩
ACM Transactions on Knowledge Discovery from Data, 15(1)
King's College London
ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2020, 15 (1), pp.1-34. ⟨10.1145/3418683⟩
ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 15 (1), pp.1-34. ⟨10.1145/3418683⟩
ACM Transactions on Knowledge Discovery from Data, 15(1)
King's College London
String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge. In this paper, we consider the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a297d7aea7b7efd9214cd4cd7b5d081a
http://hdl.handle.net/10281/302770
http://hdl.handle.net/10281/302770
Autor:
Giulia Bernardini, Alessio Conte, Garance Gourdel, Roberto Grossi, Grigorios Loukides, Nadia Pisanti, Solon Pissis, Giulia Punzi, Leen Stougie, Michelle Sweering
Publikováno v:
Bernardini, G, Conte, A, Gourdel, G, Grossi, R, Loukidis, G, Pisanti, N, Pissis, S, Punzi, G, Stougie, L & Sweering, M 2022, ' Hide and Mine in Strings : Hardness, Algorithms, and Experiments ', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING . https://doi.org/10.1109/TKDE.2022.3158063
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering, 2022, pp.1-6. ⟨10.1109/TKDE.2022.3158063⟩
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering, 2022, pp.1-6. ⟨10.1109/TKDE.2022.3158063⟩
International audience; Data sanitization and frequent pattern mining are two well-studied topics in data mining. Our work initiates a study on the fundamental relation between data sanitization and frequent pattern mining in the context of sequentia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ca42cec6c8465ce54c34479bf9d928c
https://doi.org/10.1109/tkde.2022.3158063
https://doi.org/10.1109/tkde.2022.3158063