在工業4.0和數位轉型時代，製造業的價值主張已從單純銷售產品轉變為透過整合IT服務直接為終端客戶提供價值。企業資源規劃（ERP）系統最初專注於製造流程，現已發展到涵蓋幾乎所有業務運營，成為數位轉型策略的支柱，確保企業在數位互聯世界中保持競爭力。成功的數位轉型（DT）不僅需要ERP系統中包含的大量數據，還需要它們與業務流程保持一致，即業務與IT對齊（BITA）。
本研究從三個角度提出了支持 BITA 的綜合解決方案。首先是引入物件導向本體（OOO）。OOO融合了物理和哲學的觀點，著眼於業務運作中所有可觀察和可理解的實體本質，形成了本研究的核心概念。以此基礎提出了物件導的本體企業架構（OOOEA），包括應用層、業務層、領域層、資料存取層四層，實體層、數位化企業架構、知識系統三個面向，形成全面的系統整合框架。其次是採用基於統一塑模語言（UML）的CAS模型作為領域知識本體，以統一語言作為溝通標準對於促進 BITA 至關重要。研究以三個階段評估企業領域知識本體的發展階段：建模現實、驗證模型和驗證現實，也提供了廣泛的OOO建置工作流程。第三，實際應用案例驗證了所提出框架的可行性和有效性。例如，接單後設計生產（ETO）的製造商可以使用此框架進行客製化產品的成本估算服務。透過整合MBOM、庫存、採購、製造、銷售數據等ERP系統模組，展現了OOOOEA的領域知識整合能力。此外，系統整合能力透過系統功能和聊天機器人提供的服務展示。
領域知識是DT的基石，它不僅指導企業的數位化策略和方向，也促進內部溝通與協作，帶來更好的業務成果和競爭優勢。迄今為止，還沒有文獻探討物件導向本體在企業領域知識整合的應用。本研究建立了一種新穎的通訊語言來闡明企業領域知識，從而解決DT背景下的 BITA 挑戰。強調整合過程的跨學科性質，包括哲學、電腦科學、知識工程和管理，呈現重大進步。

In the era of Industry 4.0 and digital transformation (DT), the value proposition of the manufacturing industry has shifted from merely selling products to providing value directly to end customers through integrated IT services. Enterprise resource planning (ERP) systems, which initially focused on manufacturing processes, have evolved to encompass almost all business operations, serving as a pillar of DT strategies that ensure companies remain competitive in a digitally connected world. Successful DT requires not only the extensive data contained in ERP systems but also their alignment with business processes, known as business & IT alignment (BITA).
This study proposes integrated solutions to support BITA from three perspectives. First is the introduction of Object-Oriented Ontology (OOO). This approach integrates both physical and philosophical viewpoints to focus on the essence of all observable and comprehensible entities in business operations, forming the core concept of this study. Based on this, the Object-Oriented Ontology Enterprise Architecture (OOOEA) framework is proposed, encompassing four layers—application, business, domain, and data access—and three aspects—physics, digital enterprise architecture, and knowledge-based systems. This forms a comprehensive system integration framework. Second is the adoption of the UML-based CAS Model, which serves as the domain knowledge ontology. Using a "unified language" as a communication standard is crucial for facilitating BITA. The study evaluates the development stages of enterprise domain knowledge ontology across three stages: modeling the reality, verifying the model, and verifying the reality. An extensive OOO construction workflow is also provided. Third, practical application cases validate the feasibility and effectiveness of the proposed framework. For example, a manufacturer producing customized products using an Engineer-to-Order (ETO) approach can use this framework for cost estimation services. By integrating ERP system modules such as MBOM, inventory, procurement, manufacturing, and sales data, the OOOEA's domain knowledge integration capability is demonstrated. Additionally, system integration capabilities are showcased through services provided by system functions and chatbots.
Domain knowledge is the cornerstone of DT. It not only guides the digital strategy and direction of enterprises but also fosters internal communication and collaboration, leading to better business outcomes and competitive advantages. To date, no existing literature has explored the application of object-oriented ontology in enterprise domain knowledge integration. This study establishes a novel communication language to elucidate enterprise domain knowledge, thereby addressing BITA challenges in the context of DT. The interdisciplinary nature of the integration process, encompassing philosophy, computer science, knowledge engineering, and management, is emphasized, presenting significant advancements.

References
[1] Chang, R. and K. Chen. Enterprise architecture, the strategic thinking you need in the digital age. Harvard Business Review Instructor Lecture 2020 2020/10/29 2021/8/1]; Available from: https://www.hbrtaiwan.com/article_content_AR0010035.html.
[2] Tett, G., The silo effect: The peril of expertise and the promise of breaking down barriers. 2015: Simon and Schuster.
[3] Holotiuk, F. and D. Beimborn, Critical success factors of digital business strategy. 2017.
[4] Hsu, C.-C., R.-H. Tsaih, and D.C. Yen, The evolving role of IT departments in digital transformation. Sustainability, 2018. 10(10): p. 3706.
[5] Tijan, E., M. Jović, S. Aksentijević, and A. Pucihar, Digital transformation in the maritime transport sector. Technological Forecasting and Social Change, 2021. 170: p. 120879.
[6] Zimmermann, A., R. Schmidt, K. Sandkuhl, D. Jugel, J. Bogner, and M. Möhring. Evolution of enterprise architecture for digital transformation. in 2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop (EDOCW). 2018. IEEE.
[7] Kotarba, M., Digital transformation of business models. Foundations of Management, 2018. 10(1): p. 123-142.
[8] Gaiardelli, P., G. Pezzotta, A. Rondini, D. Romero, F. Jarrahi, M. Bertoni, S. Wiesner, T. Wuest, T. Larsson, and M. Zaki, Product-service systems evolution in the era of Industry 4.0. Service Business, 2021. 15(1): p. 177-207.
[9] Munir, K. and M. Sheraz Anjum, The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 2018. 14(2): p. 116-126.
[10] Blazquez, D. and J. Domenech, Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 2018. 130: p. 99-113.
[11] Osmundsen, K., J. Iden, and B. Bygstad. Digital Transformation: Drivers, Success Factors, and Implications. in MCIS. 2018.
[12] Berghaus, S. and A. Back. Disentangling the fuzzy front end of digital transformation: Activities and approaches. 2017. Association for Information Systems.
[13] Bhaskar, H.L., Business process reengineering framework and methodology: a critical study. International Journal of Services and Operations Management, 2018. 29(4): p. 527-556.
[14] Gimpel, H., S. Hosseini, R.X.R. Huber, L. Probst, M. Röglinger, and U. Faisst, Structuring Digital Transformation: A Framework of Action Fields and its Application at ZEISS. J. Inf. Technol. Theory Appl., 2018. 19(1): p. 3.
[15] Russell, K.D., P. O'Raghallaigh, P. O'Reilly, and J. Hayes. Digital privacy GDPR: a proposed digital transformation framework. in AMCIS 2018-24th Americas Conference on Information Systems. 2018. Association for Information Systems.
[16] Ershova, T.V. and Y.E. Hohlov. Digital Transformation Framework Monitoring of Large-Scale Socio-Economic Processess. in 2018 Eleventh International Conference" Management of large-scale system development"(MLSD. 2018. IEEE.
[17] Van Veldhoven, Z. and J. Vanthienen. Designing a Comprehensive Understanding of Digital Transformation and its Impact. in Bled eConference. 2019.
[18] Ramesh, N., Digital Transformation: How to Beat the High Failure Rate. 2019, Oklahoma State University.
[19] Zaoui, F. and N. Souissi, Roadmap for digital transformation: A literature review. Procedia Computer Science, 2020. 175: p. 621-628.
[20] Vial, G., Understanding digital transformation: A review and a research agenda. The journal of strategic information systems, 2019. 28(2): p. 118-144.
[21] Lee, C.-H., C.-L. Liu, A.J. Trappey, J.P. Mo, and K.C. Desouza, Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis, topic modeling and research trend discovery. Advanced Engineering Informatics, 2021. 50: p. 101428.
[22] Chao, M.-H., A.J. Trappey, and C.-T. Wu, Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics. Complexity, 2021. 2021.
[23] Trappey, A.J.C., C.V. Trappey, M.-H. Chao, N.-J. Hong, and C.-T. Wu, A VR-Enabled Chatbot Supporting Design and Manufacturing of Large and Complex Power Transformers. Electronics, 2022. 11(1): p. 87.
[24] Trappey, A.J., C.V. Trappey, M.-H. Chao, and C.-T. Wu, VR-enabled engineering consultation chatbot for integrated and intelligent manufacturing services. Journal of Industrial Information Integration, 2022: p. 100331.
[25] Chao, M.-H. and A.J. Trappey, Class-Activity-Status model for object-oriented ontology construction supporting domain knowledge integration to achieve business-IT alignment. Product: Management and Development, 2023. 20(2): p. 0-0.
[26] Chao, M.-H. and A.J. Trappey, Design of product configuration systems supporting customised product cost estimation using object-oriented ontology framework. Journal of Engineering Design, 2024: p. 1-34.
[27] Verina, N. and J. Titko. Digital transformation: conceptual framework. in Proc. of the Int. Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering’2019”, Vilnius, Lithuania. 2019.
[28] Panetta, K. Hyperautomation, blockchain, AI security, distributed cloud and autonomous things drive disruption and create opportunities in this year's strategic technology trends. 2019 2019-10-21 2022-12-25]; Available from: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020.
[29] Kirchmer, M. and P. Franz. Process reference models: accelerator for digital transformation. in International Symposium on Business Modeling and Software Design. 2020. Springer.
[30] Haleem, A., M. Javaid, R.P. Singh, S. Rab, and R. Suman, Hyperautomation for the enhancement of automation in industries. Sensors International, 2021. 2: p. 100124.
[31] Schneider, S. and O. Kokshagina, Digital transformation: What we have learned (thus far) and what is next. Creativity and innovation management, 2021. 30(2): p. 384-411.
[32] Feio, I.C.L. and V.D. Dos Santos. A Strategic Model and Framework for Intelligent Process Automation. in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI). 2022. IEEE.
[33] Harman, G., Object-oriented ontology: A new theory of everything. 2018: Penguin UK.
[34] Recht, S. and N. Wiberg, QUANTIFIED MANUFACTURING, LITTLE DATA AND THE NEW ÆSTHETIC. CSPA Quarterly, 2018(21): p. 8-19.
[35] McCormack, D.P., Atmospheric things. 2018: Duke University Press.
[36] Ferro, F., Object-Oriented Ontology’s View of Relations: a Phenomenological Critique. Open Philosophy, 2019. 2(1): p. 566-581.
[37] Yoran, G., Applied metaphysics–objects in object-oriented ontology and object-oriented programming. Interface critique, 2018(1): p. 120-133.
[38] Tylman, W., Computer Science and Philosophy: Did Plato Foresee Object-Oriented Programming? Foundations of Science, 2018. 23(1): p. 159-172.
[39] Wegner, P. Classification in object-oriented systems. in Proceedings of the 1986 SIGPLAN workshop on Object-oriented programming. 1986.
[40] Appleton, B., Patterns and software: Essential concepts and terminology. Object Magazine Online, 1997. 3(5): p. 20-25.
[41] Lindley, J., H.A. Akmal, and P. Coulton, Design Research and Object-Oriented Ontology. Open Philosophy, 2020. 3(1): p. 11-41.
[42] Ahn, S., Bartleby, the IoT, and Flat Ontology: How Ontology is Written in the Age of Ubiquitous Computing. Postmodern Culture, 2019. 29(3).
[43] Lindley, J., P. Coulton, and H.A. Akmal, Turning Philosophy with a Speculative Lathe: object-oriented ontology, carpentry, and design fiction. 2018.
[44] Akmal, H.A. and P. Coulton, A Tarot of Things: a supernatural approach to designing for IoT. 2020.
[45] Lindley, J., P. Coulton, and R. Cooper. The IoT and unpacking the Heffalump’s Trunk. in Proceedings of the Future Technologies Conference. 2018. Springer.
[46] Schallmo, D.R. and C.A. Williams, History of digital transformation, in Digital Transformation Now! 2018, Springer. p. 3-8.
[47] Hammer, M. and J. Champy, Reengineering the Corporation: Manifesto for Business Revolution, A. 2009: Zondervan.
[48] Al Tal, S., S. al Salaimeh, and N. Hajiyev, Information Technology In Business Process Reengineering. Information Technology, 2020. 29(7): p. 3653-3657.
[49] Dinata, H., Business Process Reengineering: The Role of Information Technology as a Determinant of Success for Improving Performance. Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, 2020. 5(1): p. 25-31.
[50] Sebastian, I., J. Ross, C. Beath, M. Mocker, K. Moloney, and N. Fonstad, How big old companies navigate digital transformation. 2017.
[51] Reis, J., M. Amorim, N. Melão, and P. Matos. Digital transformation: a literature review and guidelines for future research. in World conference on information systems and technologies. 2018. Springer.
[52] Tabrizi, B., E. Lam, K. Girard, and V. Irvin, Digital transformation is not about technology. Harvard Business Review, 2019. 13: p. 1-6.
[53] Verhoef, P.C., T. Broekhuizen, Y. Bart, A. Bhattacharya, J.Q. Dong, N. Fabian, and M. Haenlein, Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 2021. 122: p. 889-901.
[54] Matt, C., T. Hess, and A. Benlian, Digital transformation strategies. Business & Information Systems Engineering, 2015. 57(5): p. 339-343.
[55] Wessel, L., A. Baiyere, R. Ologeanu-Taddei, J. Cha, and T. Blegind-Jensen, Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems, 2021. 22(1).
[56] Breivold, H.P. and L. Rizvanovic. Business modeling and design in the Internet-of-Things context. in 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). 2018. IEEE.
[57] Baiyere, A., H. Salmela, and T. Tapanainen, Digital transformation and the new logics of business process management. European Journal of Information Systems, 2020. 29(3): p. 238-259.
[58] Gong, Y. and M. Janssen, The value of and myths about enterprise architecture. International Journal of Information Management, 2019. 46: p. 1-9.
[59] Sessions, R., A comparison of the top four enterprise-architecture methodologies. Houston: ObjectWatch Inc, 2007.
[60] Kotusev, S. A comparison of the top four enterprise architecture frameworks. 2021 [cited 2021 2021-07-01]; Available from: https://www.bcs.org/articles-opinion-and-research/a-comparison-of-the-top-four-enterprise-architecture-frameworks/.
[61] Pilipchuk, R., S. Seifermann, and R. Heinrich. Aligning business process access control policies with enterprise architecture. in Proceedings of the Central European Cybersecurity Conference 2018. 2018.
[62] Hacks, S. and H. Lichter. A probabilistic enterprise architecture model evolution. in 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC). 2018. IEEE.
[63] Venkatesan, D. and S. Sridhar, A rationale for the choice of enterprise architecture method and software technology in a software driven enterprise. International Journal of Business Information Systems, 2019. 32(3): p. 272-311.
[64] Kobryn, C. Modeling enterprise software architectures using UML. in Proceedings Second International Enterprise Distributed Object Computing (Cat. No.98EX244). 1998.
[65] Ozkaya, M. and F. Erata, A survey on the practical use of UML for different software architecture viewpoints. Information and Software Technology, 2020. 121: p. 106275.
[66] Júnior, E., K. Farias, and B. Silva, A Survey on the Use of UML in the Brazilian Industry, in Brazilian Symposium on Software Engineering. 2021, Association for Computing Machinery. p. 275–284.
[67] Buriánek, J., Comparison of selected enterprise architecture modeling techniques from the perspective of IT services. 2021.
[68] Wohlrab, R., J. Horkoff, R. Kasauli, S. Maro, J.-P. Steghöfer, and E. Knauss. Modeling and Analysis of Boundary Objects and Methodological Islands in Large-Scale Systems Development. in International Conference on Conceptual Modeling. 2020. Springer.
[69] Miranda, G.M., C.H. Bernabé, L.A. Santos, and M.P. Barcellos. Where enterprise architecture and early software engineering meet: An approach to use cases definition. in Proceedings of the 17th Brazilian Symposium on Software Quality. 2018.
[70] Siau, K. and P.-P. Loo, Identifying difficulties in learning UML. Information Systems Management, 2006. 23(3): p. 43-51.
[71] Seet, C. Enterprise Architecture Challenges and How to Adapt. [cited 2021 2021/8/1]; Available from: https://www.jibility.com/enterprise-architecture-challenges/.
[72] Lien, L. What is digital transformation? Seize the key opportunity to turn around during the change. 2021 2021/7/5 [cited 2021 2021/8/1]; Available from: https://www.hububble.co/blog/digitaltransformation.
[73] Gama, J.A.P. and M.N. Aponte. University digital transformation intelligent architecture: A dual model, methods and applications. in 16thLACCEI International Multi-Conference for Engineering, Education, and Technology: Innovation in Education and Inclusion. doi. 2018.
[74] Walch, M. and D. Karagiannis, Design Thinking and Knowledge Engineering: A Machine Learning Case. International Journal of Machine Learning and Computing, 2020. 10(6).
[75] Shanks, G., E. Tansley, and R. Weber, Using ontology to validate conceptual models. Communications of the ACM, 2003. 46(10): p. 85-89.
[76] Al-Fedaghi, S., Existential ontology and thinging modeling in software engineering. Available at SSRN 3360382, 2019.
[77] El-Ghalayini, H., M. Odeh, R. McClatchey, and T. Solomonides, Reverse engineering ontology to conceptual data models. arXiv preprint cs/0412036, 2004.
[78] Benslimane, S.M., M. Malki, and D. Bouchiha, Deriving Conceptual Schema from Domain Ontology: A Web Application Reverse Engineering Approach. Int. Arab J. Inf. Technol., 2010. 7(2): p. 167-176.
[79] Dorodnykh, N., Web-based software for automating development of knowledge bases on the basis of transformation of conceptual models. Открытые семантические технологии проектирования интеллектуальных систем, 2017(7): p. 145-150.
[80] Delcambre, L.M., S.W. Liddle, O. Pastor, and V.C. Storey, A reference framework for conceptual modeling: focusing on conceptual modeling research. 2019, Technical report.
[81] Patel, H.H. and N.M. Sureja, MULTI-SITE SOFTWARE DEVELOPMENT WITH ONTOLOGY. Technology, 2020. 11(5): p. 29-38.
[82] Green, S., D. Southee, and J. Boult, Towards a design process ontology. The Design Journal, 2014. 17(4): p. 515-537.
[83] Murdock, J., C. Buckner, and C. Allen. Evaluating dynamic ontologies. in International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management. 2010. Springer.
[84] Martins, B.F. The OntoOO-Method: An Ontology-Driven Conceptual Modeling Approach for Evolving the OO-Method. in International Conference on Conceptual Modeling. 2019. Springer.
[85] Verdonck, M., F. Gailly, R. Pergl, G. Guizzardi, B. Martins, and O. Pastor, Comparing traditional conceptual modeling with ontology-driven conceptual modeling: An empirical study. Information Systems, 2019. 81: p. 92-103.
[86] Guizzardi, G. and G. Wagner. A Unified Foundational Ontology and some Applications of it in Business Modeling. in CAiSE Workshops (3). 2004.
[87] Guizzardi, G., Ontological foundations for structural conceptual models. 2005.
[88] Guizzardi, G., G. Wagner, J.P.A. Almeida, and R.S. Guizzardi, Towards ontological foundations for conceptual modeling: The unified foundational ontology (UFO) story. Applied ontology, 2015. 10(3-4): p. 259-271.
[89] Guizzardi, G., C.M. Fonseca, J.P.A. Almeida, T.P. Sales, A.B. Benevides, and D. Porello, Types and taxonomic structures in conceptual modeling: A novel ontological theory and engineering support. Data & Knowledge Engineering, 2021. 134: p. 101891.
[90] Delgoshaei, P., M. Heidarinejad, and M.A. Austin. Combined ontology-driven and machine learning approach to monitoring of building energy consumption. in 2018 Building Performance Modeling Conference and SimBuild, Chicago, IL. 2018.
[91] Sosunova, I., A. Zaslavsky, and P. Fedchenkov. Role-based ontology-driven knowledge representation for IoT-enabled waste management. in Proceedings of the Seventh International Conference on the Internet of Things. 2017.
[92] Amaral, G., F. Baião, and G. Guizzardi, Foundational ontologies, ontology‐driven conceptual modeling, and their multiple benefits to data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021: p. e1408.
[93] Shridevi, S., V. Viswanathan, and B. Saleena, Ontology-Driven Decision Support Systems for Health Care, in Knowledge Computing and its Applications. 2018, Springer. p. 65-86.
[94] del Mar Roldán-García, M., J. García-Nieto, A. Maté, J. Trujillo, and J.F. Aldana-Montes, Ontology-driven approach for KPI meta-modelling, selection and reasoning. International Journal of Information Management, 2021. 58: p. 102018.
[95] Agt-Rickauer, H., Supporting domain modeling with automated knowledge acquisition and modeling recommendations. 2020.
[96] Vo, M.H.L. and Q. Hoang, Transformation of UML class diagram into OWL Ontology. Journal of Information and Telecommunication, 2020. 4(1): p. 1-16.
[97] Schwichtenberg, S., C. Gerth, and G. Engels. From open API to semantic specifications and code adapters. in 2017 IEEE International Conference on Web Services (ICWS). 2017. IEEE.
[98] Mohseni, M., M.K. Sohrabi, and M. Dorrigiv, A model‐driven approach for semantic web service modeling using web service modeling languages. Journal of Software: Evolution and Process, 2021. 33(7): p. e2364.
[99] Niknejad, N., W. Ismail, I. Ghani, B. Nazari, and M. Bahari, Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation. Information Systems, 2020. 91: p. 101491.
[100] Mohammadi, M. and M. Mukhtar. Service-oriented architecture and process modeling. in 2018 International Conference on Information Technologies (InfoTech). 2018. IEEE.
[101] Niknejad, N. and I.S. Amiri, Literature review of service-oriented architecture (SOA) adoption researches and the related significant factors. The impact of service oriented architecture adoption on organizations, 2019: p. 9-41.
[102] Hustad, E. and D.H. Olsen, Creating a sustainable digital infrastructure: the role of service-oriented architecture. Procedia Computer Science, 2021. 181: p. 597-604.
[103] Rademacher, F., S. Sachweh, and A. Zündorf. Differences between model-driven development of service-oriented and microservice architecture. in 2017 IEEE International Conference on Software Architecture Workshops (ICSAW). 2017. IEEE.
[104] Mendoza-Pitti, L., H. Calderón-Gómez, M. Vargas-Lombardo, J.M. Gómez-Pulido, and J.L. Castillo-Sequera, Towards a service-oriented architecture for the energy efficiency of buildings: A Systematic Review. IEEE Access, 2021. 9: p. 26119-26137.
[105] Rademacher, F., S. Sachweh, and A. Zündorf, Analysis of service-oriented modeling approaches for viewpoint-specific model-driven development of microservice architecture. arXiv preprint arXiv:1804.09946, 2018.
[106] Joseph, C.T. and K. Chandrasekaran, Straddling the crevasse: A review of microservice software architecture foundations and recent advancements. Software: Practice and Experience, 2019. 49(10): p. 1448-1484.
[107] Newman, S., Monolith to microservices: evolutionary patterns to transform your monolith. 2019: O'Reilly Media.
[108] Rademacher, F., J. Sorgalla, and S. Sachweh, Challenges of domain-driven microservice design: a model-driven perspective. IEEE Software, 2018. 35(3): p. 36-43.
[109] Macero, M., Macero, and Anglin, Learn Microservices with Spring Boot. 2017: Springer.
[110] Steinegger, R.H., P. Giessler, B. Hippchen, and S. Abeck. Overview of a domain-driven design approach to build microservice-based applications. in The Thrid Int. Conf. on Advances and Trends in Software Engineering. 2017.
[111] Li, X., Y. Xi, H. Zhu, J. Ling, and Q. Zhang. Infrastructure Smart Service System Based on Microservice Architecture. in International Conference on Inforatmion technology in Geo-Engineering. 2019. Springer.
[112] Rademacher, F., J. Sorgalla, S. Sachweh, and A. Zündorf, Towards a Viewpoint-specific Metamodel for Model-driven Development of Microservice Architecture. arXiv preprint arXiv:1804.09948, 2018.
[113] Pinheiro, C., A. Vasconcelos, and S. Guerreiro. Microservice Architecture from Enterprise Architecture Management Perspective. in International Symposium on Business Modeling and Software Design. 2019. Springer.
[114] Malhotra, R., Developing Microservices with Java, in Rapid Java Persistence and Microservices. 2019, Springer. p. 9-25.
[115] Hvam, L., N.H. Mortensen, and J. Riis, Product customization. 2008: Springer Science & Business Media.
[116] Shafiee, S., E. Sandrin, C. Forza, K. Kristjansdottir, A. Haug, and L. Hvam, Framing business cases for the success of product configuration system projects. Computers in Industry, 2023. 146: p. 103839.
[117] Shafiee, S., Y. Wautelet, L. Hvam, E. Sandrin, and C. Forza, Scrum versus Rational Unified Process in facing the main challenges of product configuration systems development. Journal of Systems and Software, 2020. 170: p. 110732.
[118] Rasmussen, J.B., L. Hvam, K. Kristjansdottir, and N.H. Mortensen, Guidelines for Structuring Object-Oriented Product Configuration Models in Standard Configuration Software. J. Univers. Comput. Sci., 2020. 26(3): p. 374-401.
[119] Carr, R.I., Cost-estimating principles. Journal of Construction Engineering and Management, 1989. 115(4): p. 545-551.
[120] Cooper, R. and R.S. Kaplan, Activity-based systems: Measuring the costs of resource usage. Accounting horizons, 1992. 6(3): p. 1-13.
[121] Quesado, P. and R. Silva, Activity-based costing (ABC) and its implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 2021. 7(1): p. 41.
[122] Schaffer, J. and H. Schleich, Complexity cost management, in Build To Order: The Road to the 5-Day Car. 2008, Springer. p. 155-174.
[123] Stark, R. and R. Stark, Major Technology 5: Product Data Management and Bill of Materials—PDM/BOM. Virtual Product Creation in Industry: The Difficult Transformation from IT Enabler Technology to Core Engineering Competence, 2022: p. 223-272.
[124] Kadir, A.Z.A., Y. Yusof, and M.S. Wahab, Additive manufacturing cost estimation models—a classification review. The International Journal of Advanced Manufacturing Technology, 2020. 107: p. 4033-4053.
[125] Baboolal, K. and P. Hosein. Material and Cost estimation of a Customized Product based on the Customer’s description. in 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA). 2021. IEEE.
[126] Berwing, K., G. Schuh, and V. Stich. Generation of a Data Model For Quotation Costing Of Make To Order Manufacturers From Case Studies. in Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. 2022. Hannover: publish-Ing.
[127] Goasduff, L. 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020. 2020 2020-09-28 2021-02-05]; Available from: https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/.
[128] Wood, L. Global Chatbot Market Anticipated to Reach $9.4 Billion by 2024 - Robust Opportunities to Arise in Retail & eCommerce. 2019 2019-12-12 2021-02-05]; Available from: https://markets.businessinsider.com/news/stocks/global-chatbot-market-anticipated-to-reach-9-4-billion-by-2024-robust-opportunities-to-arise-in-retail-ecommerce-1028759508.
[129] Chopra, A. 21 Vital Chatbot Statistics for 2020. 2020 2021-02-05]; Available from: https://outgrow.co/blog/vital-chatbot-statistics.
[130] Bouziane, A., D. Bouchiha, N. Doumi, and M. Malki, Question answering systems: survey and trends. Procedia Computer Science, 2015. 73: p. 366-375.
[131] Jurafsky, D. and J.H. Martin, Speech and language processing. 3rd ed. draft. 2019, Stanford, CA, USA: Stanford University.
[132] Lokman, A.S. and M.A. Ameedeen. Modern chatbot systems: A technical review. in Proceedings of the future technologies conference. 2018. Springer.
[133] Mishra, A. and S.K. Jain, A survey on question answering systems with classification. Journal of King Saud University-Computer and Information Sciences, 2016. 28(3): p. 345-361.
[134] Wu, Y., W. Wu, C. Xing, M. Zhou, and Z. Li, Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. arXiv preprint arXiv:1612.01627, 2016.
[135] Quamar, A., F. Özcan, D. Miller, R.J. Moore, R. Niehus, and J. Kreulen, Conversational BI: an ontology-driven conversation system for business intelligence applications. Proceedings of the VLDB Endowment, 2020. 13(12): p. 3369-3381.
[136] Gu, J.-C., Z.-H. Ling, and Q. Liu, Utterance-to-utterance interactive matching network for multi-turn response selection in retrieval-based chatbots. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019. 28: p. 369-379.
[137] Bradeško, L., M. Witbrock, J. Starc, Z. Herga, M. Grobelnik, and D. Mladenić, Curious Cat--Mobile, Context-Aware Conversational Crowdsourcing Knowledge Acquisition. ACM Transactions on Information Systems (TOIS), 2017. 35(4): p. 1-46.
[138] Firdaus, M., N. Thakur, and A. Ekbal, Aspect-Aware Response Generation for Multimodal Dialogue System. ACM Transactions on Intelligent Systems and Technology (TIST), 2021. 12(2): p. 1-33.
[139] Ait-Mlouk, A. and L. Jiang, KBot: a Knowledge graph based chatBot for natural language understanding over linked data. IEEE Access, 2020. 8: p. 149220-149230.
[140] Varitimiadis, S., K. Kotis, A. Skamagis, A. Tzortzakakis, G. Tsekouras, and D. Spiliotopoulos. Towards implementing an AI chatbot platform for museums. in International Conference on Cultural Informatics, Communication & Media Studies. 2020.
[141] Park, J., Y. Cho, H. Lee, J. Choo, and E. Choi, Knowledge Graph-based Question Answering with Electronic Health Records. arXiv preprint arXiv:2010.09394, 2020.
[142] Li, F.-L., M. Qiu, H. Chen, X. Wang, X. Gao, J. Huang, J. Ren, Z. Zhao, W. Zhao, and L. Wang. Alime assist: An intelligent assistant for creating an innovative e-commerce experience. in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017.
[143] Shi, N., Q. Zeng, and R. Lee, The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning. arXiv preprint arXiv:2009.13984, 2020.
[144] Wang, D., Example-Driven Question Answering. 2019, Microsoft Research.
[145] Sreelakshmi, A., S. Abhinaya, A. Nair, and S.J. Nirmala. A Question Answering and Quiz Generation Chatbot for Education. in 2019 Grace Hopper Celebration India (GHCI). 2019. IEEE.
[146] Athreya, R.G., A.-C. Ngonga Ngomo, and R. Usbeck. Enhancing Community Interactions with Data-Driven Chatbots--The DBpedia Chatbot. in Companion Proceedings of the The Web Conference 2018. 2018.
[147] Yamaguchi, H., M. Mozgovoy, and A. Danielewicz-Betz. A Chatbot Based On AIML Rules Extracted From Twitter Dialogues. in FedCSIS (Communication Papers). 2018.
[148] Lokman, A., M. Ameedeen, and N. Ghani. A Conceptual IR Chatbot Framework with Automated Keywords-based Vector Representation Generation. in IOP Conference Series: Materials Science and Engineering. 2020. IOP Publishing.
[149] Gillis, A.S. Turing Test. 2019 2019-06 2021-02-05]; Available from: https://searchenterpriseai.techtarget.com/definition/Turing-test.
[150] Reeves, S., V. Williams, F.M. Costela, R. Palumbo, O. Umoren, M.M. Christopher, D. Blacker, and R.L. Woods, Narrative video scene description task discriminates between levels of cognitive impairment in Alzheimer’s disease. Neuropsychology, 2020. 34(4): p. 437.
[151] Dai, J. and Z. Ma, Automatic Identification of Bond Information Based on OCR and NLP. JCP, 2019. 14(6): p. 397-403.
[152] Jain, V.K. and S. Kumar, Predictive analysis of emotions for improving customer services, in Natural Language Processing: Concepts, Methodologies, Tools, and Applications. 2020, IGI Global: Hershey, PA, USA. p. 808-817.
[153] Baez, M., F. Daniel, F. Casati, and B. Benatallah, Chatbot integration in few patterns. IEEE Internet Computing, 2020.
[154] Keng, Y. Application of patent information to business planning of enterprises. 2019 2019-12-10 2021-02-05]; Available from: https://www.judicial.gov.tw/tw/cp-1429-66877-ae6e7-1.html.
[155] Govindarajan, U.H., A. Trappey, and C. Trappey, Immersive Technology for Human-Centric Cyberphysical Systems in Complex Manufacturing Processes: A Comprehensive Overview of the Global Patent Profile Using Collective Intelligence. Complexity, 2018. 2018.
[156] Trappey, A., C. Trappey, and A.-C. Chang, Intelligent Extraction of a Knowledge Ontology From Global Patents: The Case of Smart Retailing Technology Mining. International Journal on Semantic Web and Information Systems, 2020. 16: p. 61-80.
[157] Adiwardana, D., M.-T. Luong, D.R. So, J. Hall, N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha, G. Nemade, and Y. Lu, Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977, 2020.
[158] Roller, S., E. Dinan, N. Goyal, D. Ju, M. Williamson, Y. Liu, J. Xu, M. Ott, K. Shuster, and E.M. Smith, Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637, 2020.
[159] Bao, S., H. He, F. Wang, H. Wu, H. Wang, W. Wu, Z. Guo, Z. Liu, and X. Xu, Plato-2: Towards building an open-domain chatbot via curriculum learning. arXiv preprint arXiv:2006.16779, 2020.
[160] Abdullahi, S.S., S. Yiming, A. Abdullahi, and U. Aliyu. Open domain chatbot based on attentive end-to-end Seq2Seq mechanism. in Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. 2019.
[161] Hong, C.H., Y. Liang, S.S. Roy, A. Jain, V. Agarwal, R. Draves, Z. Zhou, W. Chen, Y. Liu, and M. Miracky, Audrey: A Personalized Open-Domain Conversational Bot. arXiv preprint arXiv:2011.05910, 2020.
[162] Rastogi, P., A. Gupta, T. Chen, and L. Mathias, Scaling multi-domain dialogue state tracking via query reformulation. arXiv preprint arXiv:1903.05164, 2019.
[163] Calvaresi, D., J.-P. Calbimonte, E. Siboni, S. Eggenschwiler, G. Manzo, R. Hilfiker, and M. Schumacher, EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots. Electronics, 2021. 10(6): p. 666.
[164] Li, C.-Y., D. Ortega, D. Väth, F. Lux, L. Vanderlyn, M. Schmidt, M. Neumann, M. Völkel, P. Denisov, and S. Jenne, ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents. arXiv preprint arXiv:2005.01777, 2020.
[165] Ahmad, N.A., M.H. Che, A. Zainal, M.F. Abd Rauf, and Z. Adnan, Review of chatbots design techniques. International Journal of Computer Applications, 2018. 181(8): p. 7-10.
[166] Tavanapour, N. and E.A. Bittner, Automated facilitation for idea platforms: design and evaluation of a Chatbot prototype, in Thirty Ninth International Conference on Information Systems. 2018: San Francisco.
[167] Vladova, G., J. Haase, L.S. Rüdian, and N. Pinkwart, Educational chatbot with learning avatar for personalization, in Twenty-fifth Americas Conference on Information Systems. 2019: Cancun.
[168] Gupta, S., K. Jagannath, N. Aggarwal, R. Sridar, S. Wilde, and Y. Chen. Artificially Intelligent (AI) Tutors in the Classroom: A Need Assessment Study of Designing Chatbots to Support Student Learning. in PACIS 2019 Proceedings. 2019.
[169] Mehfooz, F., S. Jha, S. Singh, S. Saini, and N. Sharma, Medical Chatbot for Novel COVID-19, in ICT Analysis and Applications. 2021, Springer. p. 423-430.
[170] Catania, F., N. Di Nardo, F. Garzotto, and D. Occhiuto. Emoty: an emotionally sensitive conversational agent for people with neurodevelopmental disorders. in Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019.
[171] Jaiswal, M., C.-P. Bara, Y. Luo, M. Burzo, R. Mihalcea, and E.M. Provost. Muse: a multimodal dataset of stressed emotion. in Proceedings of The 12th Language Resources and Evaluation Conference. 2020.
[172] Augello, A., M. Gentile, L. Weideveld, and F. Dignum, A model of a social chatbot, in Intelligent Interactive Multimedia Systems and Services 2016. 2016, Springer. p. 637-647.
[173] Xu, A., Z. Liu, Y. Guo, V. Sinha, and R. Akkiraju. A new chatbot for customer service on social media. in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017.
[174] Hu, T., A. Xu, Z. Liu, Q. You, Y. Guo, V. Sinha, J. Luo, and R. Akkiraju. Touch your heart: A tone-aware chatbot for customer care on social media. in Proceedings of the 2018 CHI conference on human factors in computing systems. 2018.
[175] Janssen, A., J. Passlick, D. Cordona, and M. Breitner, Virtual Assistance in Any Context: A Taxonomy of Desgin Elements for Domain-Specific Chatbots. Business & Information Systems Engineering, 2020.
[176] Brandtzaeg, P.B. and A. Følstad. Why people use chatbots. in International conference on internet science. 2017. Springer.
[177] van Gisbergen, M.S., I. Sensagir, and J. Relouw, How Real Do You See Yourself in VR? The Effect of User-Avatar Resemblance on Virtual Reality Experiences and Behaviour, in Augmented Reality and Virtual Reality. 2020, Springer. p. 401-409.
[178] Azuma, R.T., A Survey of Augmented Reality. Presence: Teleoperators and Virtual Environments, 1997. 6(4): p. 355-385.
[179] Berryman, D.R., Augmented Reality: A Review. Medical Reference Services Quarterly, 2012. 31(2): p. 212-218.
[180] Carmigniani, J., B. Furht, M. Anisetti, P. Ceravolo, E. Damiani, and M. Ivkovic, Augmented reality technologies, systems and applications. Multimedia Tools and Applications, 2011. 51(1): p. 341-377.
[181] Voida, S., M. Podlaseck, R. Kjeldsen, and C. Pinhanez. A study on the manipulation of 2D objects in a projector/camera-based augmented reality environment. in Proceedings of the SIGCHI conference on Human factors in computing systems. 2005.
[182] Pyae, A., L. Mika, and J. Smed. Understanding Players' Experiences in Location-based Augmented Reality Mobile Games: A Case of Pokémon Go. in Extended Abstracts Publication of the Annual Symposium on Computer-Human Interaction in Play. 2017.
[183] Lee, J., Y. Kim, M.-H. Heo, D. Kim, and B.-S. Shin, Real-time projection-based augmented reality system for dynamic objects in the performing arts. Symmetry, 2015. 7(1): p. 182-192.
[184] Wilson, A.D. and H. Benko. Projected Augmented Reality with the RoomAlive Toolkit. in Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces. 2016.
[185] Lee, J., S. Jung, J.W. Kim, and F. Biocca, Applying spatial augmented reality to anti-smoking message: Focusing on spatial presence, negative emotions, and threat appraisal. International Journal of Human–Computer Interaction, 2019. 35(9): p. 751-760.
[186] Waruwu, A.F., I.P.A. Bayupati, and I.K.G.D. Putra, Augmented reality mobile application of Balinese Hindu temples: DewataAR. International Journal of Computer Network and Information Security, 2015. 7(2): p. 59.
[187] Starner, T., S. Mann, B. Rhodes, J. Levine, J. Healey, D. Kirsch, R.W. Picard, and A. Pentland, Augmented reality through wearable computing. Presence: Teleoperators & Virtual Environments, 1997. 6(4): p. 386-398.
[188] Barfield, W. and T. Caudell, Basic concepts in wearable computers and augmented reality, in Fundamentals of wearable computers and augmented reality. 2001, CRC Press. p. 19-42.
[189] Tussyadiah, I.P., T.H. Jung, and M.C. tom Dieck, Embodiment of wearable augmented reality technology in tourism experiences. Journal of Travel research, 2018. 57(5): p. 597-611.
[190] What is 3D visualization? 2019 [cited 2021 2021/5/29]; Available from: https://www.dd3d-studio.com/frequently-asked-questions/general-dd3d-qa-2/what-is-3d-visualization/.
[191] Burdea, G.C. and P. Coiffet, Virtual reality technology. 2003: John Wiley & Sons.
[192] Koutek, C.D.M. and M. Koutek, Scientific visualization in virtual reality: Interaction techniques and application development. 2003.
[193] Speicher, M., B.D. Hall, and M. Nebeling. What is mixed reality? in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019.
[194] Lütjens, M., T.P. Kersten, B. Dorschel, and F. Tschirschwitz, Virtual Reality in Cartography: Immersive 3D Visualization of the Arctic Clyde Inlet (Canada) Using Digital Elevation Models and Bathymetric Data. Multimodal Technologies and Interaction, 2019. 3(1): p. 9.
[195] Kim, Y.M., I. Rhiu, M. Rhie, H.S. Choi, and M.H. Yun. Current State of User Experience Evaluation in Virtual Reality: A Systematic Review from an Ergonomic Perspective. in Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2019. SAGE Publications Sage CA: Los Angeles, CA.
[196] Hamid, N.S.S., F.A. Aziz, and A. Azizi. Virtual reality applications in manufacturing system. in 2014 Science and Information Conference. 2014. IEEE.
[197] Gonzalez-Badillo, G., H.I. Medellin-Castillo, and T. Lim, Development of a haptic virtual reality system for assembly planning and evaluation. Procedia Technology, 2013. 7: p. 265-272.
[198] Peng, G., G. Wang, W. Liu, and H. Yu, A desktop virtual reality-based interactive modular fixture configuration design system. Computer-Aided Design, 2010. 42(5): p. 432-444.
[199] Guo, Z., D. Zhou, Q. Zhou, S. Mei, S. Zeng, D. Yu, and J. Chen, A hybrid method for evaluation of maintainability towards a design process using virtual reality. Computers & Industrial Engineering, 2020. 140: p. 106227.
[200] Nee, A.Y. and S.-K. Ong, Virtual and augmented reality applications in manufacturing. IFAC proceedings volumes, 2013. 46(9): p. 15-26.
[201] Schuster, A., L. Larsen, F. Fischer, R. Glück, S. Schneyer, M. Kühnel, and M. Kupke, Smart manufacturing of thermoplastic cfrp skins. Procedia Manufacturing, 2018. 17: p. 935-943.
[202] Woo, J.H. and D. Oh, Development of simulation framework for shipbuilding. International Journal of Computer Integrated Manufacturing, 2018. 31(2): p. 210-227.
[203] Wu, W., X. Shao, and H. Liu, Automatic visibility evaluation method for application in virtual prototyping environment. International Journal of Computer Integrated Manufacturing, 2019. 32(10): p. 960-978.
[204] Lau, K.W., Organizational learning goes virtual? The Learning Organization, 2015.
[205] Wickramasinghe, W., P. De Saram, C. Liyanage, L. Rangika, and L. Ranathunga. Virtual reality markup framework for generating interactive indoor environment. in 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS). 2017. IEEE.
[206] Malik, A.A., T. Masood, and A. Bilberg, Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace. International Journal of Computer Integrated Manufacturing, 2020. 33(1): p. 22-37.
[207] Stanica, I., M.-I. Dascalu, C.N. Bodea, and A.D.B. Moldoveanu. VR job interview simulator: where virtual reality meets artificial intelligence for education. in 2018 Zooming innovation in consumer technologies conference (ZINC). 2018. IEEE.
[208] Wolf, M., A. Semm, and C. Erfurth. Digital transformation in companies–challenges and success factors. in International Conference on Innovations for Community Services. 2018. Springer.
[209] Fensel, D., Ontologies, in Ontologies. 2001, Springer. p. 11-18.
[210] Erjavec, J., A. Manfreda, J. Jaklič, P. Fehér, M. Indihar Štemberger, Z. Szabó, and A. Kő. Case studies of successful digital transformation in Slovenia And Hungary. in 5th Internafional Conference on Management and Organizafion. 2018.
[211] Jonathan, G.M. Digital transformation in the public sector: Identifying critical success factors. in European, Mediterranean, and Middle Eastern Conference on Information Systems. 2019. Springer.
[212] Elbashir, M.Z., S.G. Sutton, H. Mahama, and V. Arnold, Unravelling the integrated information systems and management control paradox: enhancing dynamic capability through business intelligence. Accounting & Finance, 2021. 61: p. 1775-1814.
[213] Marinescu, F. and A. Avram, Domain-driven design Quickly. 2007: Lulu. com.
[214] O'Neil, E.J. Object/relational mapping 2008: hibernate and the entity data model (edm). in Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 2008.
[215] Aguilar-Saven, R.S., Business process modelling: Review and framework. International Journal of production economics, 2004. 90(2): p. 129-149.
[216] Meier, H., R. Roy, and G. Seliger, Industrial product-service systems—IPS2. CIRP annals, 2010. 59(2): p. 607-627.
[217] Dawood, O.S., Toward Requirements and Design Traceability Using Natural Language Processing. European Journal of Engineering and Technology Research, 2018. 3(7): p. 42-49.
[218] Venkatraman, S. and R. Venkatraman, Process innovation and improvement using business object-oriented process modelling (BOOPM) framework. Applied System Innovation, 2019. 2(3): p. 23.
[219] da Silva, E.H.D.R., A.C. Shinohara, E.P. de Lima, J. Angelis, and C.G. Machado, Reviewing Digital Manufacturing concept in the Industry 4.0 paradigm. Procedia CIRP, 2019. 81: p. 240-245.
[220] Choi, S., K. Jung, B. Kulvatunyou, and K. Morris, An analysis of technologies and standards for designing smart manufacturing systems. Journal of research of the national institute of standards and technology, 2016. 121: p. 422.
[221] Bellman, M. and G. Göransson, Intelligent process automation: building the bridge between Robotic Process Automation and artificial intelligence. 2019.
[222] Geer, D., Eclipse becomes the dominant Java IDE. Computer, 2005. 38(7): p. 16-18.
[223] Murphy, G.C., M. Kersten, and L. Findlater, How are Java software developers using the Eclipse IDE? IEEE software, 2006. 23(4): p. 76-83.
[224] Chen, H. and A. Cheng, ZK: AJAX without the JavaScript framework. 2007: Apress.
[225] Amini, M. and A.M. Abukari, ERP Systems Architecture For The Modern Age: A Review of The State of The Art Technologies. Journal of Applied Intelligent Systems and Information Sciences, 2020. 1(2): p. 70-90.
[226] Mladenova, T. Open-source ERP systems: an overview. in 2020 International Conference Automatics and Informatics (ICAI). 2020. IEEE.
[227] Freeman, E., E. Robson, B. Bates, and K. Sierra, Head First Design Patterns: A Brain-Friendly Guide. 2004, Newton, MA: O'Reilly Media, Inc.
[228] Forman, I.R. and N. Forman, Java reflection in action (in action series). 2004: Manning Publications Co.
[229] Avgustinov, P., O. De Moor, M.P. Jones, and M. Schäfer. QL: Object-oriented queries on relational data. in 30th European Conference on Object-Oriented Programming (ECOOP 2016). 2016. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
[230] Marinov, M. and I. Valova, Component Interaction in Distributed Knowledge-Based Systems. TEM Journal, 2019. 8(3): p. 721.
[231] Tang, H., S. Guo, L. Huang, L. Li, and Y. Li, Research and development on key models and technology of PDM system. The International Journal of Advanced Manufacturing Technology, 2015. 78(9): p. 1865-1878.
[232] Ying, L. Enterprise Marketing Knowledge Acquisition Based on Network Computing. in International Conference on Frontier Computing. 2020. Springer.
[233] Saura, J.R., D. Ribeiro-Soriano, and D. Palacios-Marqués, From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets. International Journal of Information Management, 2021: p. 102331.
[234] Ribeiro-Navarrete, S., J.R. Saura, and D. Palacios-Marqués, Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy. Technological Forecasting and Social Change, 2021. 167: p. 120681.