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Applied Computing and Intelligence

2023, Volume 3, Issue 1: 44-78. doi: 10.3934/aci.2023004
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From basic approaches to novel challenges and applications in Sequential Pattern Mining

  • 1.
    Dept. of Information Engineering, University of Pisa, Italy; alessio.bechini@unipi.it, pietro.delloglio@unifi.it, francesco.marcelloni@unipi.it
  • 2.
    Dept. of Computer Science, University of Pisa, Italy; alessandro.bondielli@unipi.it
  • 3.
    Dept. of Philology, Literature and Lingustics, University of Pisa, Italy; alessandro.bondielli@unipi.it
  • Received: 14 September 2022 Revised: 05 January 2023 Accepted: 11 January 2023 Published: 06 February 2023

  • Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.

    Citation: Alessio Bechini, Alessandro Bondielli, Pietro Dell'Oglio, Francesco Marcelloni. From basic approaches to novel challenges and applications in Sequential Pattern Mining[J]. Applied Computing and Intelligence, 2023, 3(1): 44-78. doi: 10.3934/aci.2023004

    Related Papers:

  • Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.


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    [1] R. Agrawal, R. Srikant, Mining sequential patterns, Proceedings of the eleventh international conference on data engineering, (1995), 3-14.
    [2] R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, (1993), 207-216. https://dx.doi.org/10.1145/170035.170072
    [3] M. Amiri, L. Mohammad-Khanli, R. Mirandola, An online learning model based on episode mining for workload prediction in cloud, Future Generation Computer Systems, 87 (2018), 83-101. https://doi.org/10.1016/j.future.2018.04.044 doi: 10.1016/j.future.2018.04.044
    [4] M. Amiri, L. Mohammad-Khanli, R. Mirandola, A sequential pattern mining model for application workload prediction in cloud environment, J. Netw. Comput. Appl., 105 (2018), 21-62. https://dx.doi.org/10.1016/j.jnca.2017.12.015 doi: 10.1016/j.jnca.2017.12.015
    [5] X. Ao, H. Shi, J. Wang, L. Zuo, H. Li, Q. He, Large-scale frequent episode mining from complex event sequences with hierarchies, ACM T. Intel. Syst. Tec. (TIST), 10 (2019), 1-26. https://doi.org/10.1145/3326163 doi: 10.1145/3326163
    [6] J. Ayres, J. Flannick, J. Gehrke, T. Yiu, Sequential pattern mining using a bitmap representation, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, (2002), 429-435. https://dx.doi.org/10.1145/775047.775109
    [7] M. Barsacchi, A. Bechini, F. Marcelloni, Implicitly distributed fuzzy random forests, Proc. of the 36th Annual ACM Symposium on Applied Computing, (2021), 392-399. https://dx.doi.org/10.1145/3412841.3442082
    [8] K. Beedkar, R. Gemulla, Lash: Large-scale sequence mining with hierarchies, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, (2015), 491-503. https://dx.doi.org/10.1145/2723372.2723724
    [9] M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, et al., KNIME: The Konstanz Information Miner. In: Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007), Springer. https://doi.org/10.1007/978-3-540-78246-9_38
    [10] S. Biswas, M. Wardat, H. Rajan, (2022) The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large, Proc. of the 44th International Conference on Software Engineering, (2022), 2091-2103, https://dx.doi.org/10.1145/3510003.3510057
    [11] P. Braun, A. Cuzzocrea, C. K. Leung, A. G. M. Pazdor, J. Souza, S. K. Tanbeer, Pattern mining from big iot data with fog computing: Models, issues, and research perspectives, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), (2019), 584-591, https://dx.doi.org/10.1109/CCGRID.2019.00075
    [12] J. H. Chang, W. S. Lee, Efficient mining method for retrieving sequential patterns over online data streams, J. Inf. Sci., 31 (2005), 420-432. https://dx.doi.org/10.1177/0165551505055405 doi: 10.1177/0165551505055405
    [13] L. Chang, T. Wang, D. Yang, H. Luan, Seqstream: Mining closed sequential patterns over stream sliding windows, 2008 Eighth IEEE International Conference on Data Mining, (2008), 83-92. https://dx.doi.org/10.1109/ICDM.2008.36
    [14] C. C. Chen, C. Y. Tseng, M. S. Chen, Highly scalable sequential pattern mining based on MapReduce model on the cloud, 2013 IEEE International Congress on Big Data, (2013), 310-317. https://dx.doi.org/10.1109/bigdata.congress.2013.48
    [15] C. C. Chen, H. H. Shuai, M. S. Chen, Distributed and scalable sequential pattern mining through stream processing, Knowl. Inf. Syst., 53 (2017), 365-390. https://dx.doi.org/10.1007/s10115-017-1037-1 doi: 10.1007/s10115-017-1037-1
    [16] J. Chen, An updown directed acyclic graph approach for sequential pattern mining, IEEE T. Knowl. Data En., 22 (2009), 913-928. https://dx.doi.org/10.1109/TKDE.2009.135 doi: 10.1109/TKDE.2009.135
    [17] D. Y. Chiu, Y. H. Wu, A. L. Chen, An efficient algorithm for mining frequent sequences by a new strategy without support counting, Proceedings. 20th International Conference on Data Engineering, (2004), 375-386, https://dx.doi.org/10.1109/ICDE.2004.1320012
    [18] D. Choi, H. R'bigui, C. Cho, Candidate digital tasks selection methodology for automation with robotic process automation, Sustainability, 13 (2021), 8980. https://dx.doi.org/10.3390/su13168980 doi: 10.3390/su13168980
    [19] S. Cong, J. Han, D. Padua, Parallel mining of closed sequential patterns, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, (2005), 562-567. https://dx.doi.org/10.1145/1081870.1081937
    [20] T. J. Czubryt, C. K. Leung, A. G. M. Pazdor, Q-VIPER: Quantitative vertical bitwise algorithm to mine frequent patterns. In: Wrembel R, Gamper J, Kotsis G, Tjoa AM, Khalil I (eds) Big Data Analytics and Knowledge Discovery, Springer International Publishing, (2022), 219-233. https://dx.doi.org/10.1007/978-3-031-12670-3_19
    [21] P. Dell'Oglio, A. Bondielli, A. Bechini, F. Marcelloni, Leveraging sequence mining for robot process automation. In: Abraham A, Pllana S, Casalino G, Ma K, Bajaj A (eds) Intelligent Systems Design and Applications - 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) held December 12-14, 2022, Springer Nature Switzerland AG, in press.
    [22] A. Demiriz, webSPADE: a parallel sequence mining algorithm to analyze web log data, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., (2002), 755-758. IEEE. https://dx.doi.org/10.1109/icdm.2002.1184046
    [23] M. El-Sayed, C. Ruiz, E. Rundensteiner, Fs-miner: Efficient and incremental mining of frequent sequence patterns in web logs, Proc. of the International Workshop on Web Information and Data Management, (2004), 128-135. https://dx.doi.org/10.1145/1031453.1031477
    [24] C. I. Ezeife, Y. Lu, Y. Liu, Plwap sequential mining: open source code, Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, (2005), 26-35. https://dx.doi.org/10.1145/1133905.1133910
    [25] Y. Fan, Y. Ye, L. Chen, Malicious sequential pattern mining for automatic malware detection, Expert Systems with Applications, 52 (2016), 16-25. https://dx.doi.org/10.1016/j.eswa.2016.01.002 doi: 10.1016/j.eswa.2016.01.002
    [26] X. Fei, S. Zheng, Yan Lj, Fan C (2016) A improved sequential pattern mining algorithm based on PrefixSpan, 2016 World Automation Congress (WAC), (2016), 1-4. IEEE. https://dx.doi.org/10.1109/wac.2016.7583059
    [27] L. Feremans, B. Cule, B. Goethals, Mining top-k quantile-based cohesive sequential patterns, Proceedings of the 2018 SIAM international conference on data mining, (2018), 90-98.
    [28] P. Fournier-Viger, C. W. Wu, V. S. Tseng, Mining maximal sequential patterns without candidate maintenance, International Conference on Advanced Data Mining and Applications, (2013), 169-180. Springer Berlin Heidelberg. https://dx.doi.org/10.1007/978-3-642-53914-5_15
    [29] P. Fournier-Viger, T. Gueniche, S. Zida, V. S. Tseng, Erminer: sequential rule mining using equivalence classes, International Symposium on Intelligent Data Analysis, (2014), 108-119. https://doi.org/10.1007/978-3-319-12571-8_10
    [30] P. Fournier-Viger, C. W. Wu, A. Gomariz, V. S. Tseng, VMSP: Efficient vertical mining of maximal sequential patterns, Canadian conference on artificial intelligence, (2014), 83-94. Springer International Publishing. https://dx.doi.org/10.1007/978-3-319-06483-3_8
    [31] P. Fournier-Viger, J. C. W. Lin, A. Gomariz, T. Gueniche, A. Soltani, Z. Deng, et al., The SPMF open-source data mining library version 2, Joint European conference on machine learning and knowledge discovery in databases, (2016), 36-40. Springer. https://dx.doi.org/10.1007/978-3-319-46131-1_8
    [32] P. Fournier-Viger, J. C. W. Lin, R. U. Kiran, Y. S. Koh, R. Thomas, A survey of sequential pattern mining, Data Science and Pattern Recognition, 1 (2017), 54-77.
    [33] P. Fournier-Viger, P. Yang, J. C. W. Lin, U. Yun, Hue-span: Fast high utility episode mining, International Conference on Advanced Data Mining and Applications, (2019), 169-184. https://dx.doi.org/10.1007/978-3-030-35231-8_12
    [34] P. Fournier-Viger, Y. Yang, P. Yang, J. C. W. Lin, U. Yun, TKE: Mining top-k frequent episodes, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, (2020), 832-845. https://dx.doi.org/10.1007/978-3-030-55789-8_71
    [35] J. Fowkes, C. Sutton, A subsequence interleaving model for sequential pattern mining, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, (2016), 835-844. https://dx.doi.org/10.1145/2939672.2939787
    [36] F. Fumarola, P. F. Lanotte, M. Ceci, D. Malerba, CloFAST: closed sequential pattern mining using sparse and vertical id-lists, Knowl. Inf. Syst., 48 (2016), 429-463. https://dx.doi.org/10.1007/s10115-015-0884-x doi: 10.1007/s10115-015-0884-x
    [37] W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, P. S. Yu, A survey of parallel sequential pattern mining, ACM Transactions on Knowledge Discovery from Data (TKDD), 13 (2019), 1-34. https://dx.doi.org/10.1145/3314107 doi: 10.1145/3314107
    [38] M. Garofalakis, R. Rastogi, K. Shim, Mining sequential patterns with regular expression constraints, IEEE T. Knowl. Data En., 14 (2002), 530-552. https://dx.doi.org/10.1109/TKDE.2002.1000341 doi: 10.1109/TKDE.2002.1000341
    [39] J. Ge, Y. Xia, Distributed sequential pattern mining in large scale uncertain databases, Pacific-Asia conference on knowledge discovery and data mining, (2016), 17-29. Springer. https://dx.doi.org/10.1007/978-3-319-31750-2_2
    [40] J. Ge, Y. Xia, J. Wang, C. H. Nadungodage, S. Prabhakar, Sequential pattern mining in databases with temporal uncertainty, Knowl. Inf. Syst., 51 (2017), 821-850. https://dx.doi.org/10.1007/s10115-016-0977-1 doi: 10.1007/s10115-016-0977-1
    [41] A. Gomariz, M. Campos, R. Marin, B. Goethals, Clasp: An efficient algorithm for mining frequent closed sequences, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2013), 50-61. Springer. https://dx.doi.org/10.1007/978-3-642-37453-1_5
    [42] T. Guyet, W. Zhang, A. Bifet, Incremental mining of frequent serial episodes considering multiple occurrence, Computational Science-ICCS 2022: 22nd International Conference, London, UK, June 21-23, 2022, Proceedings, Part I., Cham: Springer International Publishing. https://dx.doi.org/10.48550/arXiv.2201.11650
    [43] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, M. Hsu, Freespan: Frequent pattern-projected sequential pattern mining, Proc. of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2000), 355-359. https://dx.doi.org/10.1145/347090.347167
    [44] J. Han, J. Pei, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, et al., PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, proceedings of the 17th international conference on data engineering, (2001), 215-224. https://dx.doi.org/10.1109/icde.2001.914830
    [45] J. Han, H. Cheng, D. Xin, X. Yan, Frequent pattern mining: Current status and future directions, Data Min. Knowl. Disc., 15 (2007), 55-86. https://dx.doi.org/10.1007/s10618-006-0059-1 doi: 10.1007/s10618-006-0059-1
    [46] C. C. Ho, H. F. Li, F. F. Kuo, S. Y. Lee, Incremental mining of sequential patterns over a stream sliding window, Sixth IEEE International Conference on Data Mining-Workshops (ICDMW'06), (2006), 677-681. IEEE. https://dx.doi.org/10.1109/ICDMW.2006.98
    [47] A. Hosseininasab, W. J. van Hoeve, A. A. Cire, Constraint-based sequential pattern mining with decision diagrams, Proc of the AAAI Conference on Artificial Intelligence, 33 (2019), 1495-1502. https://dx.doi.org/10.1609/aaai.v33i01.33011495 doi: 10.1609/aaai.v33i01.33011495
    [48] Y. H. Hsieh, C. C. Chen, H. H. Shuai, M. S. Chen, Highly parallel sequential pattern mining on a heterogeneous platform, 2018 IEEE International Conference on Data Mining (ICDM), (2018), 1037-1042. https://dx.doi.org/10.1109/ICDM.2018.00131
    [49] J. W. Huang, S. C. Lin, M. S. Chen, DPSP: Distributed progressive sequential pattern mining on the cloud, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2010), 27-34. https://dx.doi.org/10.1007/978-3-642-13672-6_3
    [50] K. Y. Huang, C. H. Chang, Efficient mining of frequent episodes from complex sequences, Inform. Syst., 33 (2008), 96-114. https://dx.doi.org/10.1016/j.is.2007.07.003 doi: 10.1016/j.is.2007.07.003
    [51] C. Jiang, F. Coenen, M. Zito, A survey of frequent subgraph mining algorithms, The Knowledge Engineering Review, 28 (2013), 75-105.
    [52] C. Kim, J. H. Lim, R. T. Ng, K. Shim, SQUIRE: Sequential pattern mining with quantities, J. Syst. Software, 80 (2007), 1726-1745. https://dx.doi.org/10.1016/j.jss.2006.12.562 doi: 10.1016/j.jss.2006.12.562
    [53] R. U. Kiran, M. Kitsuregawa, P. K. Reddy, Efficient discovery of periodic-frequent patterns in very large databases, J. Syst. Software, 112 (2016), 110-121.
    [54] B. Le, H. Duong, T. Truong, P. Fournier-Viger, Fclosm, fgensm: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy, Knowl. Inf. Syst., 53 (2017), 71-107. https://dx.doi.org/10.1007/s10115-017-1032-6 doi: 10.1007/s10115-017-1032-6
    [55] P. Lenca, B. Vaillant, P. Meyer, S. Lallich, Association rule interestingness measures: Experimental and theoretical studies, Quality Measures in Data Mining, (2007), 51-76.
    [56] Y. Liang, S. Wu, Sequence-growth: A scalable and effective frequent itemset mining algorithm for big data based on MapReduce framework, 2015 IEEE International Congress on Big Data, (2015), 393-400. IEEE. https://dx.doi.org/10.1109/BigDataCongress.2015.65
    [57] V. C. C. Liao, M. S. Chen, DFSP: a Depth-First SPelling algorithm for sequential pattern mining of biological sequences, Knowl. Inf. Syst., 38 (2014), 623-639. https://dx.doi.org/10.1007/s10115-012-0602-x doi: 10.1007/s10115-012-0602-x
    [58] J. C. W. Lin, T. Li, M. Pirouz, J. Zhang, P. Fournier-Viger, High average-utility sequential pattern mining based on uncertain databases, Knowl. Inf. Syst., 62 (2020), 1199-1228. https://doi.org/10.1007/s10115-019-01385-8 doi: 10.1007/s10115-019-01385-8
    [59] J. C. W. Lin, Y. Djenouri, G. Srivastava, Y. Li, P. S. Yu, Scalable mining of high-utility sequential patterns with three-tier MapReduce model, ACM Transactions on Knowledge Discovery from Data (TKDD), 16 (2021), 1-26. https://dx.doi.org/10.1145/3487046 doi: 10.1145/3487046
    [60] D. Lo, S. C. Khoo, J. Li, Mining and ranking generators of sequential patterns, Proceedings of the 2008 SIAM International Conference on Data Mining (SDM), (2008), 553-564. https://dx.doi.org/10.1137/1.9781611972788.51
    [61] J. M. Luna, F. Padillo, M. Pechenizkiy, S. Ventura, Apriori versions based on MapReduce for mining frequent patterns on big data, IEEE T. Cybernetics, 48 (2018), 2851-2865. https://dx.doi.org/10.1109/TCYB.2017.2751081 doi: 10.1109/TCYB.2017.2751081
    [62] J. M. Luna, P. Fournier-Viger, S. Ventura, Frequent itemset mining: A 25 years review, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9 (2019), e1329. https://doi.org/10.1002/widm.1329 doi: 10.1002/widm.1329
    [63] C. Luo, S. M. Chung, Efficient mining of maximal sequential patterns using multiple samples, Proceedings of the 2005 SIAM International Conference on Data Mining, (2005), 415-426. SIAM. https://dx.doi.org/10.1137/1.9781611972757.37
    [64] N. R. Mabroukeh, C. I. Ezeife, A taxonomy of sequential pattern mining algorithms, ACM Comput. Surv. (CSUR), 43 (2010), 1-41. https://dx.doi.org/10.1145/1824795.1824798 doi: 10.1145/1824795.1824798
    [65] R. Manikandan, S. B. V. J. Sara, N. Yuvaraj, A. Chaturvedi, S. S. Priscila, M. Ramkumar, Sequential pattern mining on chemical bonding database in the bioinformatics field, AIP Conference Proceedings, 2393 (2022), 020050. https://dx.doi.org/10.1063/5.0074405 doi: 10.1063/5.0074405
    [66] H. Mannila, H. Toivonen, A. Inkeri Verkamo, Discovery of frequent episodes in event sequences, Data min. knowl. disc., 1 (1997), 259-289. https://dx.doi.org/10.1023/A:1009748302351 doi: 10.1023/A:1009748302351
    [67] H. M. Marin-Castro, E. Tello-Leal, Event log preprocessing for process mining: A review, Applied Sciences, 11 (2021), 10556. https://dx.doi.org/10.3390/app112210556 doi: 10.3390/app112210556
    [68] F. Masseglia, P. Poncelet, R. Cicchetti, An efficient algorithm for web usage mining, Networking and Information Systems Journal, 2 (2000), 571-604.
    [69] I. Miliaraki, K. Berberich, R. Gemulla, S. Zoupanos, Mind the gap: Large-scale frequent sequence mining, Proceedings of the 2013 ACM SIGMOD international conference on management of data, (2013), 797-808. https://dx.doi.org/10.1145/2463676.2465285
    [70] C. H. Mooney, J. F. Roddick, Sequential pattern mining - approaches and algorithms, ACM Comput. Surv., 45 (2013), 1-39. https://dx.doi.org/10.1145/2431211.2431218 doi: 10.1145/2431211.2431218
    [71] M. Muzammal, R. Raman, Mining sequential patterns from probabilistic databases, Knowl. Inf. Syst., 44 (2015), 325-358. https://dx.doi.org/10.1007/s10115-014-0766-7 doi: 10.1007/s10115-014-0766-7
    [72] S. Nuruddin, M. Islam, M. Alam, J. Ovi, M. A. Islam, An efficient approach for sequential pattern mining on GPU using CUDA platform, 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), (2020), 1-9. https://dx.doi.org/10.1109/ISMSIT50672.2020.9255161
    [73] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 12 (2011), 2825-2830.
    [74] J. Pei, J. Han, B. Mortazavi-Asl, H. Zhu, Mining access patterns efficiently from web logs, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2000), 396-407. https://dx.doi.org/10.1007/3-540-45571-x_47
    [75] J. Pei, J. Han, W. Wang, Mining sequential patterns with constraints in large databases, Proc. of the 11th Int'l Conf. on Information and Knowledge Management, (2002), 18-25. https://dx.doi.org/10.1145/584792.584799
    [76] J. Pei, J. Han, W. Wang, Constraint-based sequential pattern mining: The pattern-growth methods, J. Intel. Inf. Syst., 28 (2007), 133-160. https://dx.doi.org/10.1007/s10844-006-0006-z doi: 10.1007/s10844-006-0006-z
    [77] T. T. Pham, Efficiently mining sequential generator patterns using prefix trees, Fund. Inform., 138 (2015), 373-386. https://dx.doi.org/10.3233/FI-2015-1217 doi: 10.3233/FI-2015-1217
    [78] H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, U. Dayal, Multi-dimensional sequential pattern mining, Proceedings of the tenth international conference on Information and knowledge management, (2001), 81-88. https://dx.doi.org/10.1145/502585.502600
    [79] K. Poongodi, D. Kumar, Mining frequent serial positioning episode rules with forward and backward search technique from event sequences, The Computer Journal, (2022). https://dx.doi.org/10.1093/comjnl/bxac031, bxac031
    [80] S. Qiao, C. Tang, S. Dai, M. Zhu, J. Peng, H. Li, Y. Ku, Partspan: Parallel sequence mining of trajectory patterns, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 5 (2008), 363-367. IEEE. https://dx.doi.org/10.1109/fskd.2008.33
    [81] C. Raissi, P. Poncelet, M. Teisseire, SPEED: mining maximal sequential patterns over data streams, 2006 3rd International IEEE Conference Intelligent Systems, (2006), 546-552. IEEE. https://dx.doi.org/10.1109/IS.2006.348478
    [82] S. Rathore, S. Dawar, V. Goyal, D. Patel, Top-k high utility episode mining from a complex event sequence, Proceedings of the 21st international conference on management of data, computer society of India, (2016).
    [83] P. Ravikumar, P. Likhitha, B. Venus Vikranth Raj, R. Uday Kiran, Y. Watanobe, K. Zettsu, Efficient discovery of periodic-frequent patterns in columnar temporal databases, Electronics, 10 (2021), 1478. https://doi.org/10.3390/electronics10121478 doi: 10.3390/electronics10121478
    [84] Ritika, S. K. Gupta, Mining transactional databases for frequent and high-utility fuzzy sequential patterns with time intervals, IEEE Access, 10 (2022), 71107-71119. https://dx.doi.org/10.1109/ACCESS.2022.3188307 doi: 10.1109/ACCESS.2022.3188307
    [85] K. K. Roy, M. H. H. Moon, M. M. Rahman, C. F. Ahmed, C. K. Leung, Mining sequential patterns in uncertain databases using hierarchical index structure, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2021), 29-41. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-75765-6_3
    [86] M. Sahli, E. Mansour, P. Kalnis, Parallel motif extraction from very long sequences, Proceedings of the 22nd ACM international conference on Information & Knowledge Management, (2013), 549-558. https://dx.doi.org/10.1145/2505515.2505575
    [87] A. Sallaberry, N. Pecheur, S. Bringay, M. Roche, M. Teisseire, Sequential patterns mining and gene sequence visualization to discover novelty from microarray data, J. Biomed. Inform., 44 (2011), 760-774. https://dx.doi.org/10.1016/j.jbi.2011.04.002 doi: 10.1016/j.jbi.2011.04.002
    [88] A. Segatori, A. Bechini, P. Ducange, F. Marcelloni, A distributed fuzzy associative classifier for big data, IEEE T. Cybernetics, 48 (2018), 2656-2669. https://dx.doi.org/10.1109/TCYB.2017.2748225 doi: 10.1109/TCYB.2017.2748225
    [89] S. Song, H. Hu, S. Jin, HVSM: a new sequential pattern mining algorithm using bitmap representation, International conference on advanced data mining and applications, (2005), 455-463. Springer. https://dx.doi.org/10.1007/11527503_55
    [90] W. Song, W. Ye, P. Fournier-Viger, Mining sequential patterns with flexible constraints from mooc data, Appl. Intell., 52 (2022), 16458-16474. https://doi.org/10.1007/s10489-021-03122-7 doi: 10.1007/s10489-021-03122-7
    [91] P. Songram, V. Boonjing, S. Intakosum, Closed multidimensional sequential pattern mining, Third International Conference on Information Technology: New Generations (ITNG'06), (2006), 512-517.https://dx.doi.org/10.1109/ITNG.2006.41
    [92] H. K. Sowmya, N. V. Uma Reddy, C. Kavyashree, R. J. Anandhi, Discovery of frequent pagesets from weblog using Hadoop Mapreduce based parallel apriori algorithm, 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), (2022), 765-770. https://dx.doi.org/10.23919/INDIACom54597.2022.9763104
    [93] R. Srikant, R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology (EDBT '96), (1996), 1-17.
    [94] T. Truong, H. Duong, B. Le, P. Fournier-Viger, U. Yun, H. Fujita, Efficient algorithms for mining frequent high utility sequences with constraints, Inform. Sciences, 568 (2021), 239-264. https://dx.doi.org/10.1016/j.ins.2021.01.060 doi: 10.1016/j.ins.2021.01.060
    [95] T. Truong-Chi, P. Fournier-Viger, A survey of high utility sequential pattern mining. In: Fournier-Viger P, Lin JCW, Nkambou R, Vo B, Tseng VS (eds) High-Utility Pattern Mining: Theory, Algorithms and Applications, (2019), 97-129. Springer International Publishing, Cham. https://dx.doi.org/10.1007/978-3-030-04921-8_4
    [96] C. F. Tsai, W. C. Lin, S. W. Ke, Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies, J. Syst. Software, 122 (2016), 83-92. https://dx.doi.org/10.1016/j.jss.2016.09.007 doi: 10.1016/j.jss.2016.09.007
    [97] C. Y. Tsai, B. H. Lai, A location-item-time sequential pattern mining algorithm for route recommendation, Knowledge-Based Systems, 73 (2015), 97-110. https://dx.doi.org/10.1016/j.knosys.2014.09.012 doi: 10.1016/j.knosys.2014.09.012
    [98] W. Van Der Aalst, Process mining, Commun. ACM, 55 (2012), 76-83. https://dx.doi.org/10.1145/2240236.2240257
    [99] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems, Curran Associates, Inc., vol 30, 2017. Available from: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
    [100] J. Wang, J. Han, BIDE: efficient mining of frequent closed sequences, Proc. of the 20th International Conference on Data Engineering, (2004), 79-90. IEEE. https://dx.doi.org/10.1109/ICDE.2004.1319986
    [101] X. Wang, A. Hosseininasab, P. Colunga, S. Kadıoğlu, W. J. van Hoeve, Seq2Pat: Sequence-to-pattern generation for constraint-based sequential pattern mining, Proc of the AAAI Conference on Artificial Intelligence, 36 (2022), 12665-12671. https://dx.doi.org/10.1609/aaai.v36i11.21542 doi: 10.1609/aaai.v36i11.21542
    [102] I. H. Witten, E. Frank, M. A. Hall, C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Data Mining, 2 (2005).
    [103] C. H. Wu, C. C. Lai, Y. C. Lo, An empirical study on mining sequential patterns in a grid computing environment, Expert Syst. Appl., 39 (2012), 5748-5757. https://dx.doi.org/10.1016/j.eswa.2011.11.095 doi: 10.1016/j.eswa.2011.11.095
    [104] C. W. Wu, Y. F. Lin, P. S. Yu, V. S. Tseng, Mining high utility episodes in complex event sequences, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, (2013), 536-544. https://dx.doi.org/10.1145/2487575.2487654
    [105] Y. Wu, Y. Tong, X. Zhu, X. Wu, NOSEP: Nonoverlapping sequence pattern mining with gap constraints, IEEE T. Cybernetics, 48 (2017), 2809-2822. https://dx.doi.org/10.1109/TCYB.2017.2750691 doi: 10.1109/TCYB.2017.2750691
    [106] Y. Wu, C. Zhu, Y. Li, L. Guo, X. Wu, NetNCSP: Nonoverlapping closed sequential pattern mining, Knowledge-based systems, 196 (2020), 105812. https://dx.doi.org/10.1016/j.knosys.2020.105812 doi: 10.1016/j.knosys.2020.105812
    [107] Y. Wu, L. Luo, Y. Li, L. Guo, P. Fournier-Viger, X. Zhu, X. Wu, NTP-Miner: Nonoverlapping three-way sequential pattern mining, ACM T. Knowl. Discov. Data, 16 (2021), 1-21. https://dx.doi.org/10.1145/3480245 doi: 10.1145/3480245
    [108] Y. Wu, M. Chen, Y. Li, J. Liu, Z. Li, J. Li, X. Wu, ONP-Miner: One-off negative sequential pattern mining, ACM T. Knowl. Discov. Data, (2022). https://dx.doi.org/10.1145/3549940
    [109] Y. Wu, Q. Hu, Y. Li, L. Guo, X. Zhu, X. Wu, OPP-Miner: Order-preserving sequential pattern mining for time series, IEEE T. on Cybernetics, (2022). https://dx.doi.org/10.1109/TCYB.2022.3169327
    [110] X. Yan, J. Han, gspan: Graph-based substructure pattern mining, 2002 IEEE International Conference on Data Mining, (2002), 721-724.
    [111] X. Yan, J. Han, R. Afshar, CloSpan: Mining closed sequential patterns in large datasets, Proceedings of the 2003 SIAM international conference on data mining, (2003), 166-177. https://dx.doi.org/10.1137/1.9781611972733.15
    [112] Z. Yang, M. Kitsuregawa, LAPIN-SPAM: An improved algorithm for mining sequential pattern, 21st International Conference on Data Engineering Workshops (ICDEW'05), (2005), 1222-1222. IEEE. https://dx.doi.org/10.1109/icde.2005.235
    [113] Z. Yang, Y. Wang, M. Kitsuregawa, LAPIN: Effective sequential pattern mining algorithms by last position induction for dense databases. In: Kotagiri R, Krishna PR, Mohania M, Nantajeewarawat E (eds) Advances in Databases: Concepts, Systems and Applications - DASFAA 2007, Springer Berlin Heidelberg, Berlin, Heidelberg, Lecture Notes in Computer Sciences, 4443 (2007), 1020-1023. https://dx.doi.org/10.1007/978-3-540-71703-4_95
    [114] J. Yin, Z. Zheng, L. Cao, Uspan: An efficient algorithm for mining high utility sequential patterns, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, (2012), 660-668. https://dx.doi.org/10.1145/2339530.2339636
    [115] T. You, Y. Sun, Y. Zhang, J. Chen, P. Zhang, M. Yang, Accelerated frequent closed sequential pattern mining for uncertain data, Expert Syst. Appl., (2022), 117254. https://doi.org/10.1016/j.eswa.2022.117254
    [116] X. Yu, J. Liu, X. Liu, C. Ma, B. Li, A mapreduce reinforced distributed sequential pattern mining algorithm, International Conference on Algorithms and Architectures for Parallel Processing, (2015), 183-197. Springer. https://dx.doi.org/10.1007/978-3-319-27122-4_13
    [117] U. Yun, J. J. Leggett, WSpan: Weighted sequential pattern mining in large sequence databases, 2006 3rd international IEEE conference intelligent systems, (2006), 512-517. IEEE. https://dx.doi.org/10.1109/IS.2006.348472
    [118] F. Zabihi, M. Ramezan, M. M. Pedram, A. Memariani, Fuzzy sequential pattern mining with sliding window constraint, 2010 2nd International Conference on Education Technology and Computer, 5 (2010), V5-396-V5-400. https://dx.doi.org/10.1109/ICETC.2010.5530044
    [119] M. J. Zaki, Parallel sequence mining on shared-memory machines, J. Parallel Distr. Com., 61 (2001), 401-426. https://dx.doi.org/10.1007/3-540-46502-2_8 doi: 10.1007/3-540-46502-2_8
    [120] M. J. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Mach. learn., 42 (2001), 31-60. https://dx.doi.org/10.1023/A:1007652502315 doi: 10.1023/A:1007652502315
    [121] J. Zhang, Y. Wang, D. Yang, CCSpan: Mining closed contiguous sequential patterns, Knowledge-Based Systems, 89 (2015), 1-13. https://dx.doi.org/10.1016/j.knosys.2015.06.014 doi: 10.1016/j.knosys.2015.06.014
    [122] H. Zhu, P. Wang, X. He, Y. Li, W. Wang, B. Shi, Efficient episode mining with minimal and non-overlapping occurrences, 2010 IEEE International Conference on Data Mining, (2010), 1211-1216. IEEE. https://dx.doi.org/10.1109/icdm.2010.25
    [123] X. Zhu, H. Deng, Z. Chen, A brief review on frequent pattern mining, 2011 3rd International Workshop on Intelligent Systems and Applications, (2011), 1-4. https://dx.doi.org/10.1109/ISA.2011.5873451
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