A hybrid deep learning and ontology-driven approach to perform business process capability assessment

Journal of Industrial Information Integration - Tập 30 - Trang 100409 - 2022
Marcelo Romero1,2, Wided Guédria1,2, Hervé Panetto2, Béatrix Barafort1
1Luxembourg Institute of Science and Technology (LIST), 5, Avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
2Universitéde Lorraine, CNRS, CRAN, F-54000 Nancy, France

Tài liệu tham khảo

Purchase, 2011, Enterprise transformation: Why are we interested, what is it, and what are the challenges?, J. Enterp. Transform., 1, 14, 10.1080/19488289.2010.549289 Proper, 2013, Enterprise architecture: informed steering of enterprises in motion, 16 Aguilar-Saven, 2004, Business process modelling: Review and framework, Int. J. Prod. Econ., 90, 129, 10.1016/S0925-5273(03)00102-6 Rohloff, 2011, Advances in business process management implementation based on a maturity assessment and best practice exchange, Inf. Syst. E-Bus. Manage., 9, 383, 10.1007/s10257-010-0137-1 Tarhan, 2017, On the Use of Ontologies in Software Process Assessment: A Systematic Literature Review, 2 Looy, 2011, Defining business process maturity. A journey towards excellence, Total Qual. Manage. Bus. Excell., 22, 1119, 10.1080/14783363.2011.624779 Team, 2002 ISO Central Secretary, 2004 ISO Central Secretary, 2015 Guédria, 2015, Maturity model for enterprise interoperability, Enterp. Inf. Syst., 9, 1, 10.1080/17517575.2013.805246 Crawford, 2007 Schumacher, 2016, A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises, Procedia Cirp, 52, 161, 10.1016/j.procir.2016.07.040 Kim, 2021, Organizational process maturity model for IoT data quality management, J. Ind. Inf. Integr. Feilmayr, 2016, An analysis of ontologies and their success factors for application to business, Data Knowl. Eng., 101, 1, 10.1016/j.datak.2015.11.003 Ehrlinger, 2016, Towards a definition of knowledge graphs, 1695 Proença, 2018, Formalizing ISO/IEC 15504-5 and SEI CMMI v1. 3–Enabling automatic inference of maturity and capability levels, Comput. Stand. Interfaces, 60, 13, 10.1016/j.csi.2018.04.007 Aggarwal, 2012 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Sengupta, 2020, A review of deep learning with special emphasis on architectures, applications and recent trends, Knowl.-Based Syst., 10.1016/j.knosys.2020.105596 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Hassoun, 1995 ISO Central Secretary, 2015 Adali, 2017, Assessment of agility in software organizations with a web-based agility assessment tool, 88 Benjamin, 2017, Organizational Transparency Maturity Assessment Method, 477 Barafort, 2018, A software artefact to support standard-based process assessment: Evolution of the TIPA® framework in a design science research project, Comput. Standards Interfaces, 60, 37, 10.1016/j.csi.2018.04.009 O’Regan, 2011, SCAMPI Appraisals, 221 Tarhan, 2015, Business process maturity assessment: state of the art and key characteristics, 430 Oliva, 2016, A maturity model for enterprise risk management, Int. J. Prod. Econ., 173, 66, 10.1016/j.ijpe.2015.12.007 Proença, 2016, Maturity models for information systems-A state of the art, Procedia Comput. Sci., 100, 1042, 10.1016/j.procs.2016.09.279 Schumacher, 2016, A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises, Procedia Cirp, 52, 161, 10.1016/j.procir.2016.07.040 Cater-Steel, 2016, Decision support systems for IT service management, Int. J. Inf. Decis. Sci., 8, 284 Lacerda, 2018 Grambow, 2013, Automated Software Engineering Process Assessment: Supporting Diverse Models using an Ontology, Int. J. Adv. Softw., 6, 213 Krivograd, 2014, Development of an intelligent maturity model-tool for business process management, 3878 Van Looy, 2016, Business process performance measurement: a structured literature review of indicators, measures and metrics, SpringerPlus, 5, 1797, 10.1186/s40064-016-3498-1 Wen, 2008, A knowledge-based decision support system for measuring enterprise performance, Knowl.-Based Syst., 21, 148, 10.1016/j.knosys.2007.05.009 Giovannini, 2012, Ontology-based system for supporting manufacturing sustainability, Annu. Rev. Control, 36, 309, 10.1016/j.arcontrol.2012.09.012 Barafort, 2009 Sangaiah, 2018, Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm, Comput. Electr. Eng., 71, 833, 10.1016/j.compeleceng.2017.07.022 Zhang, 2013, PLM components selection based on a maturity assessment and AHP methodology, 439 Yudatama, 2015, Evaluation maturity index and risk management for it governance using fuzzy AHP and fuzzy TOPSIS (case study bank XYZ), 323 J. Pöppelbuß, M. Röglinger, What makes a useful maturity model? a framework of general design principles for maturity models and its demonstration in business process management, in: Ecis, 2011, p. 28. Becker, 2009, Developing maturity models for IT management, Bus. Inf. Syst. Eng., 1, 213, 10.1007/s12599-009-0044-5 A. Maier, J. Moultrie, P.J. Clarkson, Developing maturity grids for assessing organisational capabilities: Practitioner guidance, in: 4th International Conference on Management Consulting: Academy of Management, 2009. De Bruin, 2005, Understanding the main phases of developing a maturity assessment model Kohlegger, 2009 Paulk, 1993, Capability maturity model, version 1.1, IEEE Softw., 10, 18, 10.1109/52.219617 Team, 2010 S. Marshall, G. Mitchell, An e-learning maturity model, in: Proceedings of the 19th Annual Conference of the Australian Society for Computers in Learning in Tertiary Education, Auckland, New Zealand, 2002, pp. 8–11. De Carolis, 2017, A maturity model for assessing the digital readiness of manufacturing companies, 13 Anggoro, 2018, Information system interoperability maturity model, Bull. Soc. Inform. Theory Appl., 2, 22, 10.31763/businta.v2i1.103 Santos-Neto, 2019, Enterprise maturity models: a systematic literature review, Enterp. Inf. Syst., 13, 719, 10.1080/17517575.2019.1575986 Romero, 2021, A framework for assessing capability in organisations using enterprise models, J. Ind. Inf. Integr. ISO Central Secretary, 2015 Team, 2011 Barafort, 2014, How to design an innovative framework for process improvement? The TIPA for ITIL case, 48 Yue, 2019, Towards a smart manufacturing maturity assessment framework: a socio-technical perspective, vol. 1345 Smith, 2012, Ontology, 47 Gangemi, 2009, Ontology design patterns, 221 Guizzardi, 2007, On ontology, ontologies, conceptualizations, modeling languages M.C. Klein, D. Fensel, Ontology versioning on the Semantic Web, in: SWWS, 2001, pp. 75–91. Maedche, 2001, Ontology learning for the semantic web, IEEE Intell. Syst., 16, 72, 10.1109/5254.920602 Antoniou, 2004, Web ontology language: Owl, 67 Masri, 2019, Survey of rule-based systems, Int. J. Acad. Inf. Syst. Res., 3, 1 Horrocks, 2004, SWRL: A semantic web rule language combining OWL and RuleML, W3C Member Submiss., 21, 1 Sirin, 2007, Pellet: A practical OWL-DL reasoner, Web Semant., 5, 51, 10.1016/j.websem.2007.03.004 Glimm, 2014, HermiT: an OWL 2 reasoner, J. Automat. Reason., 53, 245, 10.1007/s10817-014-9305-1 Bishop, 1995 Kramer, 1991, Nonlinear principal component analysis using autoassociative neural networks, AIChE J., 37, 233, 10.1002/aic.690370209 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Goodfellow, 2014, Generative adversarial nets, 2672 Giles, 1994, Dynamic recurrent neural networks: Theory and applications, IEEE Trans. Neural Netw., 5, 153, 10.1109/TNN.1994.8753425 Hochreiter, 2001 Manning, 1999 Almeida, 2019 Y. Bengio, J.-S. Senécal, et al., Quick Training of Probabilistic Neural Nets by Importance Sampling, in: AISTATS, 2003, pp. 1–9. M. Baroni, G. Dinu, G. Kruszewski, Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors, in: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, pp. 238–247. Salton, 1975, A vector space model for automatic indexing, Commun. ACM, 18, 613, 10.1145/361219.361220 Landauer, 1998, An introduction to latent semantic analysis, Discourse Process., 25, 259, 10.1080/01638539809545028 Blei, 2003, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 993 Bengio, 2003, A neural probabilistic language model, J. Mach. Learn. Res., 3, 1137 Mikolov, 2013 Mikolov, 2013, Distributed representations of words and phrases and their compositionality, 3111 Bojanowski, 2017, Enriching word vectors with subword information, Trans. Assoc. Comput. Linguist., 5, 135, 10.1162/tacl_a_00051 Pennington, 2014, Glove: Global vectors for word representation, 1532 Lok, 1997, Automated tool support for an emerging international software process assessment standard, 25 Alalwan, 2013, An Ontology-based Approach to Assessing Records Management Systems, E-Service J., 8, 24, 10.2979/eservicej.8.3.24 Ghazanfari, 2011, A tool to evaluate the business intelligence of enterprise systems, Sci. Iran., 18, 1579, 10.1016/j.scient.2011.11.011 Almeida, 2018, An ontology-based model for itil process assessment using tipa for itil, 104 da Silva Serapião Leal, 2020, A semi-automated system for interoperability assessment: an ontology-based approach, Enterp. Inf. Syst., 14, 308, 10.1080/17517575.2019.1678767 da Silva Avanzi, 2017, A framework for interoperability assessment in crisis management, J. Ind. Inf. Integr., 5, 26 Oberhauser, 2010, Leveraging semantic web computing for context-aware software engineering environments Proença, 2019, Information governance maturity assessment using enterprise architecture model analysis and description logics, 265 Romero, 2020, Towards a characterisation of smart systems: A systematic literature review, Comput. Ind., 120, 10.1016/j.compind.2020.103224 Cambridge University Press, 2008 Peters, 2011, Fundamentals of agent perception and attention modelling, 293 Chavarría-Barrientos, 2017, Achieving the sensing, smart and sustainable “everything”, 575 Baader, 2003 Treveil, 2020 Kotsiantis, 2007, Supervised machine learning: A review of classification techniques, Emerg. Artif. Intell. Appl. Comput. Eng., 160, 3 Klatt, 2005, You don’t have to think twice if you carefully tokenize, 299 Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Ng, 2004, Feature selection, L1 vs. L2 regularization, and rotational invariance, 78 Von Alan, 2004, Design science in information systems research, MIS Q., 28, 75, 10.2307/25148625 Hevner, 2007, A three cycle view of design science research, Scand. J. Inf. Syst., 19, 4 Leal, 2019, An ontology for interoperability assessment: A systemic approach, J. Ind. Inf. Integr., 16 ISO Central Secretary, 2004 Institute, 2004 Brickley, 2014 Manola, 2004, RDF primer, W3C Recomm., 10, 6 Musen, 2015, The protégé project: a look back and a look forward, AI Matters, 1, 4, 10.1145/2757001.2757003 ISO Central Secretary, 2015, ISO 9001: Quality management systems - Requirements 2018, 1 Naudet, 2010, Towards a systemic formalisation of interoperability, Comput. Ind., 61, 176, 10.1016/j.compind.2009.10.014 Bertalanffy, 1968 Kingma, 2014 Hawkins, 2004, The problem of overfitting, J. Chem. Inf. Comput. Sci., 44, 1, 10.1021/ci0342472 Chollet, 2021 OMG, 2011