A Knowledge-based Framework for Developing Smart Interfaces for Smart Service Systems
Khung hỗ trợ dựa trên tri thức cho phép triển khai giao diện thông minh cho các hệ thống dịch vụ thông minh
Abstract
Nowadays, smart service systems are value co-creating configurations of people, technologies, organisations,
and information that are capable of in-dependent learning, adaptation, and decision-making. They are propelled by unprecedented advancements in connectivity, sensors, data storage, computation, and artificial intelligence. One of the key challenges faced by those systems is how to provide smart interfaces, which can assist business users with limited knowledge in business analytics in gaining business insights from business data. For this reason, this paper proposes a knowledge-based framework for developing smart interfaces for smart service systems, which will assist business users in exploring business data to gain business in-sights and subsequently make better business decisions to promote value co-creation. A prototype with simulation data has been developed and presented as a running example to illustrate how the proposed framework can be applied to create an effective smart interface for a typical smart service system: a customer intelligence system.
References
C. Lim and P. P. Maglio, “Clarifying the concept of smart service system,” Handbook of Service Science, Volume II, pp. 349–376, 2019.
T. Le Dinh, T. T. Pham Thi, C. Pham-Nguyen, and L. N. H. Nam, “A knowledge-based model for context-aware smart service systems,” Journal of Information and Telecommunication, vol. 6, no. 2, pp. 141–162, 2022.
D. Beverungen, C. F. Breidbach, J. Poeppelbuss, and V. K. Tuunainen, “Smart service systems: An interdisciplinary perspective.” Inf. Syst. J., vol. 29, no. 6, pp. 1201–1206, 2019.
E. M. Roth, J. T. Malin, and D. L. Schreckenghost, “Paradigms for intelligent interface design,” in Handbook of human-computer interaction. Elsevier, 1997, pp. 1177–1201.
C.-H. Lim, P. P. Maglio, K.-J. Kim, M.-J. Kim, and K.-H. Kim, “Toward smarter service systems through serviceoriented data analytics,” in 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE, 2016, pp. 936–941.
J. Hagel III and J. F. Rayport, “The coming battle for customer information,” The McKinsey Quarterly, no. 3, p. 64, 1997.
J. Yven and H. Wechsler, “Smart interfaces for humancomputer intelligent interaction,” in Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003., vol. 2. IEEE, 2003, pp. 1192–1197.
A. Braham, F. Buendía, M. Khemaja, and F. Gargouri, “User interface design patterns and ontology models for adaptive mobile applications,” Personal and Ubiquitous Computing, pp. 1–17, 2021.
M. W. Iqbal, N. A. Ch, S. K. Shahzad, M. R. Naqvi, B. A. Khan, and Z. Ali, “User context ontology for adaptive mobile-phone interfaces,” IEEE Access, vol. 9, pp. 96 751–96 762, 2021.
G. Santos, T. Pinto, Z. Vale, and J. M. Corchado, “Multiagent semantic interoperability in complex energy systems simulation and decision support,” in 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP), 2019, pp. 1–6.
N. A. K. Dam, T. Le Dinh, and W. Menvielle, “Towards a conceptual framework for customer intelligence in the era of big data,” International Journal of Intelligent Information Technologies (IJIIT), vol. 17, no. 4, pp. 64–80, 2021.
B. Kitchens, D. Dobolyi, J. Li, and A. Abbasi, “Advanced customer analytics: Strategic value through integration of relationship-oriented big data,” Journal of Management Information Systems, vol. 35, no. 2, pp. 540–574, 2018.
N. A. K. Dam, T. L. Dinh, and W. Menvielle, “The quest for customer intelligence to support marketing decisions: A knowledge-based framework,” Vietnam Journal of Computer Science, vol. 9, no. 03, pp. 349–368, 2022.
T. Le Dinh, T. M. H. Vu, N. A. K. Dam, and C. N. Nguyen, “Trivi: A conceptual framework for customer intelligence systems for small and medium-sized enterprises,” 2022.
A. Abdul, J. Vermeulen, D. Wang, B. Y. Lim, and M. Kankanhalli, “Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda,” in Proceedings of the 2018 CHI conference on human factors in computing systems, 2018, pp. 1–18.
S. A. Pednekar and S. Chandna, “Optimizing business sales and improving user experience by using intelligent user interface.”
T. Zubkova and L. Tagirova, “Intelligent user interface design of application programs,” in Journal of Physics: Conference Series, vol. 1278, no. 1. IOP Publishing, 2019, p. 012026.
S. V. Kolekar, R. M. Pai, and M. P. MM, “Rule based adaptive user interface for adaptive e-learning system,” Education and Information Technologies, vol. 24, pp. 613–641, 2019.
C. Martin, F. Kampfer, C. Herdin, and L. B. Yameni, “Situation analytics and model-based user interface development: a synergetic approach for building runtime-adaptive business applications,” Complex Systems Informatics and Modeling Quarterly, no. 20, pp. 1–19, 2019.
J. Rowley, “The wisdom hierarchy: representations of the dikw hierarchy,” Journal of information science, vol. 33, no. 2, pp. 163–180, 2007.
A. Dresch, D. P. Lacerda, J. A. V. Antunes Jr, A. Dresch, D. P. Lacerda, and J. A. V. Antunes, Design science research. Springer, 2015.
Y. Dankov, “Conceptual model of user interface design for general architectural framework for business visual analytics,” in Proceedings of the 20th International Conference on Computer Systems and Technologies, 2019, pp. 251–254.
V. Johnston, M. Black, J. Wallace, M. Mulvenna, and R. Bond, “A framework for the development of a dynamic adaptive intelligent user interface to enhance the user experience,” in Proceedings of the 31st European Conference on Cognitive Ergonomics, 2019, pp. 32–35.