{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:33:56Z","timestamp":1777638836688,"version":"3.51.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100015321","name":"Universidade de Tr\u00e1s-os-Montes e Alto Douro","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100015321","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Bus Inf Syst Eng"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.<\/jats:p>","DOI":"10.1007\/s12599-023-00844-5","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T15:02:48Z","timestamp":1703170968000},"page":"777-798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Survey on Association Rule Mining for Enterprise Architecture Model Discovery"],"prefix":"10.1007","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8687-7027","authenticated-orcid":false,"given":"Carlos","family":"Pinheiro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-3338","authenticated-orcid":false,"given":"Sergio","family":"Guerreiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5383-9884","authenticated-orcid":false,"given":"Henrique S.","family":"Mamede","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"844_CR1","doi-asserted-by":"publisher","unstructured":"Agarwal RC, Aggarwal CC, Prasad VVV (2000) Depth first generation of long patterns. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, Boston. ACM, pp 108\u2013118. https:\/\/doi.org\/10.1145\/347090.347114","DOI":"10.1145\/347090.347114"},{"key":"844_CR2","doi-asserted-by":"publisher","unstructured":"Aggarwal A, Toshniwal D (2018) Spatio-temporal frequent itemset mining on web data. In: 2018 IEEE international conference on data mining workshops, pp 1160\u20131165. https:\/\/doi.org\/10.1109\/ICDMW.2018.00166","DOI":"10.1109\/ICDMW.2018.00166"},{"key":"844_CR3","doi-asserted-by":"publisher","unstructured":"Agrawal R, Imieli\u0144ski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data. ACM, New York, pp 207\u2013216. https:\/\/doi.org\/10.1145\/170035.170072","DOI":"10.1145\/170035.170072"},{"key":"844_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/69.553164","volume-title":"Parallel mining of association rules: design, implementation and experience","author":"R Agrawal","year":"1996","unstructured":"Agrawal R, Shafer J (1996) Parallel mining of association rules: design, implementation and experience. IBM Research Division, San Jose"},{"issue":"4","key":"844_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326163","volume":"10","author":"X Ao","year":"2019","unstructured":"Ao X, Shi H, Wang J, Zuo L, Li H, He Q (2019) Large-scale frequent episode mining from complex event sequences with hierarchies. ACM Trans Intell Syst Technol 10(4):1\u201326. https:\/\/doi.org\/10.1145\/3326163","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"1","key":"844_CR7","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s40537-018-0129-4","volume":"5","author":"M Barkhordari","year":"2018","unstructured":"Barkhordari M, Niamanesh M (2018) Kavosh: an effective map-reduce-based association rule mining method. J Big Data 5(1):25. https:\/\/doi.org\/10.1186\/s40537-018-0129-4","journal-title":"J Big Data"},{"issue":"2","key":"844_CR8","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TII.2021.3076513","volume":"18","author":"K Cai","year":"2022","unstructured":"Cai K, Chen H, Ai W, Miao X, Lin Q, Feng Q (2022) Feedback convolutional network for intelligent data fusion based on near-infrared collaborative IoT technology. IEEE Trans Ind Inform 18(2):1200\u20131209. https:\/\/doi.org\/10.1109\/TII.2021.3076513","journal-title":"IEEE Trans Ind Inform"},{"issue":"7","key":"844_CR9","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/TKDE.2009.135","volume":"22","author":"J Chen","year":"2010","unstructured":"Chen J (2010) An updown directed acyclic graph approach for sequential pattern mining. IEEE Trans Knowl Data Eng 22(7):913\u2013928. https:\/\/doi.org\/10.1109\/TKDE.2009.135","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"844_CR10","doi-asserted-by":"publisher","unstructured":"Cheung D, Han J, Ng V, Wong C (1996a) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings 1996 international conference on data engineering, New Orleans. https:\/\/doi.org\/10.1109\/ICDE.1996.492094","DOI":"10.1109\/ICDE.1996.492094"},{"key":"844_CR11","doi-asserted-by":"publisher","unstructured":"Cheung DW, Han J, Ng VT, Fu AW, Fu Y (1996b) A fast distributed algorithm for mining association rules. In: Fourth international conference on parallel and distributed information systems, pp 31\u201342. https:\/\/doi.org\/10.1109\/PDIS.1996.568665","DOI":"10.1109\/PDIS.1996.568665"},{"key":"844_CR12","doi-asserted-by":"publisher","unstructured":"Cheung DW, Lee SD, Kao B (1997) A general incremental technique for maintaining discovered association rules. In: Database systems for advanced applications \u201997, pp 185\u2013194. https:\/\/doi.org\/10.1142\/9789812819536_0020","DOI":"10.1142\/9789812819536_0020"},{"issue":"3","key":"844_CR13","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1007\/s10586-018-1812-0","volume":"21","author":"K-W Chon","year":"2018","unstructured":"Chon K-W, Kim M-S (2018) BIGMiner: a fast and scalable distributed frequent pattern miner for big data. Cluster Comput 21(3):1507\u20131520. https:\/\/doi.org\/10.1007\/s10586-018-1812-0","journal-title":"Cluster Comput"},{"key":"844_CR14","doi-asserted-by":"publisher","first-page":"e4676258","DOI":"10.1155\/2018\/4676258","volume":"2018","author":"DS da Cunha","year":"2018","unstructured":"da Cunha DS, Xavier RS, Ferrari DG, Vilasb\u00f4as FG, de Castro LN (2018) Bacterial colony algorithms for association rule mining in static and stream data. Math Probl Eng 2018:e4676258. https:\/\/doi.org\/10.1155\/2018\/4676258","journal-title":"Math Probl Eng"},{"key":"844_CR15","doi-asserted-by":"publisher","unstructured":"Datta S, Mali K (2021) Significant association rule mining with high associability. In: 5th international conference on intelligent computing and control systems, pp 1159\u20131164. https:\/\/doi.org\/10.1109\/ICICCS51141.2021.9432237","DOI":"10.1109\/ICICCS51141.2021.9432237"},{"key":"844_CR16","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.ins.2018.07.020","volume":"496","author":"Y Djenouri","year":"2019","unstructured":"Djenouri Y, Djenouri D, Belhadi A, Cano A (2019) Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Inform Sci 496:363\u2013377. https:\/\/doi.org\/10.1016\/j.ins.2018.07.020","journal-title":"Inform Sci"},{"issue":"2","key":"844_CR17","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s10270-014-0407-3","volume":"15","author":"M Farwick","year":"2016","unstructured":"Farwick M, Schweda CM, Breu R, Hanschke I (2016) A situational method for semi-automated enterprise architecture documentation. Softw Syst Model 15(2):397\u2013426. https:\/\/doi.org\/10.1007\/s10270-014-0407-3","journal-title":"Softw Syst Model"},{"issue":"3","key":"844_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3314107","volume":"13","author":"W Gan","year":"2019","unstructured":"Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Yu PS (2019) A survey of parallel sequential pattern mining. ACM Trans Knowl Discov Data 13(3):1\u201334. https:\/\/doi.org\/10.1145\/3314107","journal-title":"ACM Trans Knowl Discov Data"},{"key":"844_CR19","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/978-3-642-20279-7_2","volume-title":"Architecture principles: the cornerstones of enterprise architecture","author":"D Greefhorst","year":"2011","unstructured":"Greefhorst D, Proper E (2011) The role of enterprise architecture. In: Greefhorst D, Proper E (eds) Architecture principles: the cornerstones of enterprise architecture. Springer, Heidelberg, pp 7\u201329. https:\/\/doi.org\/10.1007\/978-3-642-20279-7_2"},{"key":"844_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.phpro.2015.02.005","volume":"62","author":"F Gullo","year":"2015","unstructured":"Gullo F (2015) From patterns in data to knowledge discovery: what data mining can do. Phys Proc 62:18\u201322. https:\/\/doi.org\/10.1016\/j.phpro.2015.02.005","journal-title":"Phys Proc"},{"key":"844_CR21","doi-asserted-by":"publisher","unstructured":"Gustavsson PM, Planstedt T (2005) The road towards multi-hypothesis intention simulation agents architecture\u2014fractal information fusion modeling. In: Proceedings of the winter simulation conference. https:\/\/doi.org\/10.1109\/WSC.2005.1574548","DOI":"10.1109\/WSC.2005.1574548"},{"issue":"2","key":"844_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/335191.335372","volume":"29","author":"JW Han","year":"2000","unstructured":"Han JW, Pei J, Yin YW (2000) Mining frequent patterns without candidate generation. SIGMOD Rec 29(2):1\u201312. https:\/\/doi.org\/10.1145\/335191.335372","journal-title":"SIGMOD Rec"},{"issue":"1","key":"844_CR23","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","volume":"8","author":"J Han","year":"2004","unstructured":"Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 8(1):53\u201387. https:\/\/doi.org\/10.1023\/B:DAMI.0000005258.31418.83","journal-title":"Data Min Knowl Discov"},{"key":"844_CR24","doi-asserted-by":"publisher","unstructured":"Karthik S, Medvidovic N (2019) Automatic detection of latent software component relationships from online Q&A sites. In: IEEE\/ACM 7th international workshop on realizing artificial intelligence synergies in software engineering, pp 15\u201321. https:\/\/doi.org\/10.1109\/RAISE.2019.00011","DOI":"10.1109\/RAISE.2019.00011"},{"key":"844_CR25","unstructured":"Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE technical report EBSE-2007\u201301. Keele, Staffs, and Durham. https:\/\/citeseerx.ist.psu.edu\/doc\/10.1.1.117.471. Accessed 6 Mar 2022"},{"key":"844_CR26","unstructured":"Kitchenham B (2004) Procedures for performing systematic reviews. Keele University. https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=29890a936639862f45cb9a987dd599dce9759bf5. Accessed 9 May 2022"},{"key":"844_CR27","doi-asserted-by":"publisher","unstructured":"Kiteley R, Stogdon C (2014) Literature reviews in social work. Sage, London. https:\/\/doi.org\/10.4135\/9781473957756","DOI":"10.4135\/9781473957756"},{"key":"844_CR28","unstructured":"Laudon K, Laudon JP (2021) Management information systems: managing the digital firm, global edition. Pearson. https:\/\/books.google.com.br\/books?id=AqJXzgEACAAJ. Accessed 14 Nov 2021"},{"issue":"19","key":"844_CR29","doi-asserted-by":"publisher","first-page":"6648","DOI":"10.1016\/j.eswa.2015.04.048","volume":"42","author":"T Le","year":"2015","unstructured":"Le T, Vo B (2015) An N-list-based algorithm for mining frequent closed patterns. Expert Syst Appl 42(19):6648\u20136657. https:\/\/doi.org\/10.1016\/j.eswa.2015.04.048","journal-title":"Expert Syst Appl"},{"key":"844_CR30","doi-asserted-by":"publisher","unstructured":"Li H, Wang Y, Zhang D, Zhang M, Chang EY (2008) Pfp: parallel fp-growth for query recommendation. In: Proceedings of the ACM conference on recommender systems, pp 107\u2013114. ACM, New York. https:\/\/doi.org\/10.1145\/1454008.1454027","DOI":"10.1145\/1454008.1454027"},{"key":"844_CR31","doi-asserted-by":"publisher","unstructured":"Liang Y-H, Wu S-Y (2015) Sequence-growth: a scalable and effective frequent itemset mining algorithm for big data based on MapReduce framework. In: IEEE international congress on big data, pp 393\u2013400. https:\/\/doi.org\/10.1109\/BigDataCongress.2015.65","DOI":"10.1109\/BigDataCongress.2015.65"},{"issue":"2","key":"844_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3392149","volume":"4","author":"F Lin","year":"2020","unstructured":"Lin F, Muzumdar K, Laptev NP, Curelea M-V, Lee S, Sankar S (2020) Fast dimensional analysis for root cause investigation in a large-scale service environment. Proc ACM Meas Anal Comput Syst 4(2):1\u201323. https:\/\/doi.org\/10.1145\/3392149","journal-title":"Proc ACM Meas Anal Comput Syst"},{"key":"844_CR33","doi-asserted-by":"publisher","unstructured":"Lin M-Y, Lee P-Y, Hsueh S-C (2012) Apriori-based frequent itemset mining algorithms on MapReduce. In: Proceedings of the 6th international conference on ubiquitous information management and communication. ACM, New York. https:\/\/doi.org\/10.1145\/2184751.2184842","DOI":"10.1145\/2184751.2184842"},{"key":"844_CR34","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-319-94289-6_27","volume-title":"Web Services\u2014ICWS 2018","author":"X Liu","year":"2018","unstructured":"Liu X, Zhang X, Wang Y, Zhou J, Helal S, Xu Z, Cao S (2018) PARMTRD: parallel association rules based multiple-topic relationships detection. In: Jin H et al (eds) Web Services\u2014ICWS 2018. Springer, Cham, pp 422\u2013436. https:\/\/doi.org\/10.1007\/978-3-319-94289-6_27"},{"issue":"4","key":"844_CR35","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1007\/s10489-020-01994-9","volume":"51","author":"X Liu","year":"2021","unstructured":"Liu X, Niu X, Fournier-Viger P (2021) Fast Top-K association rule mining using rule generation property pruning. Appl Intell 51(4):2077\u20132093. https:\/\/doi.org\/10.1007\/s10489-020-01994-9","journal-title":"Appl Intell"},{"issue":"3","key":"844_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3495213","volume":"13","author":"X Liu","year":"2022","unstructured":"Liu X, Zheng L, Zhang W, Zhou J, Cao S, Yu S (2022) An evolutive frequent pattern tree-based incremental knowledge discovery algorithm. ACM Trans Manag Inf Syst 13(3):1\u201320. https:\/\/doi.org\/10.1145\/3495213","journal-title":"ACM Trans Manag Inf Syst"},{"key":"844_CR37","doi-asserted-by":"publisher","unstructured":"Liu J, Wang K, Fung BCM (2012) Direct discovery of high utility itemsets without candidate generation. In: IEEE 12th international conference on data mining, pp 984\u2013989. https:\/\/doi.org\/10.1109\/ICDM.2012.20","DOI":"10.1109\/ICDM.2012.20"},{"issue":"10","key":"844_CR38","doi-asserted-by":"publisher","first-page":"2851","DOI":"10.1109\/TCYB.2017.2751081","volume":"48","author":"JM Luna","year":"2018","unstructured":"Luna JM, Padillo F, Pechenizkiy M, Ventura S (2018) Apriori versions based on MapReduce for mining frequent patterns on big data. IEEE Trans Cybern 48(10):2851\u20132865. https:\/\/doi.org\/10.1109\/TCYB.2017.2751081","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"844_CR39","doi-asserted-by":"publisher","first-page":"e1329","DOI":"10.1002\/widm.1329","volume":"9","author":"JM Luna","year":"2019","unstructured":"Luna JM, Fournier-Viger P, Ventura S (2019) Frequent itemset mining: a 25 years review. Wires Data Min Knowl Discov 9(6):e1329. https:\/\/doi.org\/10.1002\/widm.1329","journal-title":"Wires Data Min Knowl Discov"},{"key":"844_CR40","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.knosys.2018.04.037","volume":"153","author":"D Mart\u00edn","year":"2018","unstructured":"Mart\u00edn D, Mart\u00ednez-Ballesteros M, Garc\u00eda-Gil D, Alcal\u00e1-Fdez J, Herrera F, Riquelme-Santos JC (2018) MRQAR: a generic MapReduce framework to discover quantitative association rules in big data problems. Knowl-Based Syst 153:176\u2013192. https:\/\/doi.org\/10.1016\/j.knosys.2018.04.037","journal-title":"Knowl-Based Syst"},{"key":"844_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-021-00576-z","author":"D Menaga","year":"2021","unstructured":"Menaga D, Saravanan S (2021) GA-PPARM: CONSTRAINT-based objective function and genetic algorithm for privacy preserved association rule mining. Evolut Intell. https:\/\/doi.org\/10.1007\/s12065-021-00576-z","journal-title":"Evolut Intell"},{"key":"844_CR42","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.compbiomed.2019.01.019","volume":"106","author":"M Modaresnezhad","year":"2019","unstructured":"Modaresnezhad M, Vahdati A, Nemati H, Ardestani A, Sadri F (2019) A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: application to predicting bariatric surgery outcomes. Comput Biol Med 106:84\u201390. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.01.019","journal-title":"Comput Biol Med"},{"key":"844_CR43","doi-asserted-by":"publisher","unstructured":"Moens S, Aksehirli E, Goethals B (2013) Frequent itemset mining for big data. In: IEEE international conference on big data, pp 111\u2013118. https:\/\/doi.org\/10.1109\/BigData.2013.6691742","DOI":"10.1109\/BigData.2013.6691742"},{"issue":"6","key":"844_CR44","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1080\/00207540412331322939","volume":"43","author":"EI Neaga","year":"2005","unstructured":"Neaga EI, Harding JA (2005) An enterprise modeling and integration framework based on knowledge discovery and data mining. Int J Prod Res 43(6):1089\u20131108. https:\/\/doi.org\/10.1080\/00207540412331322939","journal-title":"Int J Prod Res"},{"key":"844_CR45","doi-asserted-by":"publisher","unstructured":"Niazmand E (2022) Enhancing query answer completeness with query expansion based on synonym predicates. In: Companion proceedings of the web conference, pp 354\u2013358. ACM, New York. https:\/\/doi.org\/10.1145\/3487553.3524198","DOI":"10.1145\/3487553.3524198"},{"issue":"2","key":"844_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3377882","volume":"16","author":"FM Noori","year":"2020","unstructured":"Noori FM, Riegler M, Uddin MZ, Torresen J (2020) Human activity recognition from multiple sensors data using multi-fusion representations and CNNs. ACM Trans Multimed Comput Commun Appl 16(2):1\u201319. https:\/\/doi.org\/10.1145\/3377882","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"844_CR47","doi-asserted-by":"publisher","first-page":"145614","DOI":"10.1109\/ACCESS.2019.2945911","volume":"7","author":"A Onan","year":"2019","unstructured":"Onan A (2019) Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access 7:145614\u2013145633. https:\/\/doi.org\/10.1109\/ACCESS.2019.2945911","journal-title":"IEEE Access"},{"issue":"1","key":"844_CR48","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3233\/ICA-170555","volume":"25","author":"F Padillo","year":"2018","unstructured":"Padillo F, Luna JM, Herrera F, Ventura S (2018) Mining association rules on big data through MapReduce genetic programming. Integr Comput-Aided Eng 25(1):31\u201348. https:\/\/doi.org\/10.3233\/ICA-170555","journal-title":"Integr Comput-Aided Eng"},{"issue":"5","key":"844_CR49","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/17517575.2019.1590859","volume":"13","author":"R Perez-Castillo","year":"2019","unstructured":"Perez-Castillo R, Ruiz-Gonzalez F, Genero M, Piattini M (2019) A systematic mapping study on enterprise architecture mining. Enterp Inform Syst 13(5):675\u2013718. https:\/\/doi.org\/10.1080\/17517575.2019.1590859","journal-title":"Enterp Inform Syst"},{"key":"844_CR50","doi-asserted-by":"publisher","first-page":"113249","DOI":"10.1016\/j.dss.2020.113249","volume":"131","author":"R P\u00e9rez-Castillo","year":"2020","unstructured":"P\u00e9rez-Castillo R, Ruiz F, Piattini M (2020) A decision-making support system for enterprise architecture modelling. Decis Support Syst 131:113249. https:\/\/doi.org\/10.1016\/j.dss.2020.113249","journal-title":"Decis Support Syst"},{"issue":"2","key":"844_CR51","doi-asserted-by":"publisher","first-page":"e2314","DOI":"10.1002\/smr.2314","volume":"33","author":"R P\u00e9rez-Castillo","year":"2021","unstructured":"P\u00e9rez-Castillo R, Caivano D, Ruiz F, Piattini M (2021) ArchiRev\u2014reverse engineering of information systems toward archimate models an industrial case study. J Softw Evol Proc 33(2):e2314. https:\/\/doi.org\/10.1002\/smr.2314","journal-title":"J Softw Evol Proc"},{"key":"844_CR52","doi-asserted-by":"publisher","unstructured":"Phan H (2018) NOV-CFI: a novel algorithm for closed frequent itemsets mining in transactional databases. In: Proceedings of the VII international conference on network, Communication and computing, pp 58\u201363. ACM, New York. https:\/\/doi.org\/10.1145\/3301326.3301363","DOI":"10.1145\/3301326.3301363"},{"key":"844_CR53","doi-asserted-by":"publisher","unstructured":"Pinheiro CR, Guerreiro S, Mamede HS (2021) Automation of enterprise architecture discovery based on event mining from API gateway logs: state of the art. In: IEEE 23rd conference on business informatics, pp 117\u2013124. https:\/\/doi.org\/10.1109\/CBI52690.2021.10062","DOI":"10.1109\/CBI52690.2021.10062"},{"key":"844_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.sysarc.2018.09.005","volume":"91","author":"S Sinaei","year":"2018","unstructured":"Sinaei S, Fatemi O (2018) Run-time mapping algorithm for dynamic workloads using association rule mining. J Syst Arch 91:1\u201310. https:\/\/doi.org\/10.1016\/j.sysarc.2018.09.005","journal-title":"J Syst Arch"},{"key":"844_CR55","doi-asserted-by":"publisher","unstructured":"De Stefano M, Pecorelli F, Tamburri DA, Palomba F, De Lucia A (2020) Splicing community patterns and smells: a preliminary study. In: Proceedings of the IEEE\/ACM 42nd international conference on software engineering workshops, pp 703\u2013710. ACM, New York. https:\/\/doi.org\/10.1145\/3387940.3392204","DOI":"10.1145\/3387940.3392204"},{"key":"844_CR56","doi-asserted-by":"publisher","unstructured":"Tax N, Sidorova N, Haakma R, van der Aalst WMP (2018) Mining local process models with constraints efficiently: applications to the analysis of smart home data. In: 14th international conference on intelligent environments, pp 56\u201363. https:\/\/doi.org\/10.1109\/IE.2018.00016","DOI":"10.1109\/IE.2018.00016"},{"key":"844_CR57","unstructured":"The Open Group (2018) The TOGAF\u00ae standard, version 9.2. https:\/\/publications.opengroup.org\/standards\/togaf\/c182. https:\/\/pubs.opengroup.org\/architecture\/togaf9-doc\/arch\/index.html. Accessed 28 Apr 2022"},{"key":"844_CR6","unstructured":"The Open Group (2019) ArchiMate\u00ae 3.1 Specification. https:\/\/pubs.opengroup.org\/architecture\/archimate3-doc\/. Accessed 15 Apr 2022"},{"key":"844_CR58","unstructured":"Uno T, Kiyomi M, Arimura H (2004) LCM ver. 2: efficient mining algorithms for frequent\/closed\/maximal itemsets. FIMI \u201904, p 126. https:\/\/ceur-ws.org\/Vol-126\/uno.pdf. Accessed 27 Feb 2022"},{"key":"844_CR59","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-642-28108-2_19","volume-title":"Business Process Management Workshops","author":"W van der Aalst","year":"2012","unstructured":"van der Aalst W, Adriansyah A, de Medeiros AKA, Arcieri F, Baier T, Blickle T, Wynn M (2012) Process mining manifesto. In: Daniel F et al (eds) Business Process Management Workshops. Springer, Heidelberg, pp 169\u2013194. https:\/\/doi.org\/10.1007\/978-3-642-28108-2_19"},{"issue":"6","key":"844_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3363571","volume":"13","author":"JM-T Wu","year":"2019","unstructured":"Wu JM-T, Lin JC-W, Tamrakar A (2019) High-utility itemset mining with effective pruning strategies. ACM Trans Knowl Discov Data 13(6):1\u201322. https:\/\/doi.org\/10.1145\/3363571","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"3","key":"844_CR61","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/TSMC.2015.2437327","volume":"46","author":"Y Xun","year":"2016","unstructured":"Xun Y, Zhang J, Qin X (2016) FiDoop: parallel mining of frequent itemsets using MapReduce. IEEE Trans Syst Man Cybern: Syst 46(3):313\u2013325. https:\/\/doi.org\/10.1109\/TSMC.2015.2437327","journal-title":"IEEE Trans Syst Man Cybern: Syst"},{"issue":"2","key":"844_CR62","doi-asserted-by":"publisher","first-page":"933","DOI":"10.3906\/elk-1905-88","volume":"28","author":"P Yildirim Ta\u015fer","year":"2020","unstructured":"Yildirim Ta\u015fer P, Birant KU, Birant D (2020) Multitask-based association rule mining. Turk J Elec Eng Comput Sci 28(2):933\u2013955. https:\/\/doi.org\/10.3906\/elk-1905-88","journal-title":"Turk J Elec Eng Comput Sci"},{"issue":"3","key":"844_CR63","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/69.846291","volume":"12","author":"MJ Zaki","year":"2000","unstructured":"Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372\u2013390. https:\/\/doi.org\/10.1109\/69.846291","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"844_CR64","doi-asserted-by":"publisher","unstructured":"Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 326\u2013335. ACM, New York. https:\/\/doi.org\/10.1145\/956750.956788","DOI":"10.1145\/956750.956788"},{"key":"844_CR65","doi-asserted-by":"publisher","unstructured":"Zaki MJ, Hsiao C-J (2002) CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the SIAM international conference on data mining, pp 457\u2013473. https:\/\/doi.org\/10.1137\/1.9781611972726.27","DOI":"10.1137\/1.9781611972726.27"},{"key":"844_CR66","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-319-27060-9_44","volume-title":"Advances in artificial intelligence and soft computing","author":"S Zida","year":"2015","unstructured":"Zida S, Fournier-Viger P, Lin JC-W, Wu C-W, Tseng VS (2015) EFIM: a highly efficient algorithm for high-utility itemset mining. In: Sidorov G, Galicia-Haro SN (eds) Advances in artificial intelligence and soft computing. Springer, Cham, pp 530\u2013546. https:\/\/doi.org\/10.1007\/978-3-319-27060-9_44"}],"container-title":["Business &amp; Information Systems Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-023-00844-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12599-023-00844-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-023-00844-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T10:09:09Z","timestamp":1734689349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12599-023-00844-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["844"],"URL":"https:\/\/doi.org\/10.1007\/s12599-023-00844-5","relation":{},"ISSN":["2363-7005","1867-0202"],"issn-type":[{"value":"2363-7005","type":"print"},{"value":"1867-0202","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,21]]},"assertion":[{"value":"4 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}