Abstract
The maturity of a new generation of information technologies, including the internet of things (IoT), wearables, cloud computing, Artificial Intelligence (AI) and machine learning, has led to the advent of smart domains, such as smart manufacturing, smart logistics, and smart healthcare. Smart healthcare brings unlimited opportunities to solve many of the problems of traditional medical systems, with the ultimate goal of realizing 4P medicine (Predictive, Preventive, Personalized, Participative). However, to realize this ambitious vision in such a highly regulated multi-disciplinary and sensitive domain, a mine of challenges needs to be effectively and efficiently addressed. A smart health digital platform that integrates all relevant (semi-) structured and unstructured health-related data is fundamental. The platform should incorporate a variety of care data, including vital medical information from medical records, current medication, imaging studies, lifestyle, genetic, demographic, psychological & psychosocial and patient-provided health data from exercise or health monitoring applications and medical pathways. These will lead to improving post-operative planning, reduce medical risks and costs, and generate more accurate therapy and increased Quality of Life (QoL) for patients. The main contribution of this article is a reference architecture for a smart digital platform for personalized prevention and patient management that acts as a roadmap for further R&D in this domain.
This research is partially funded by the EC Horizon 2020 project QUALITOP, under contract number H2020 - SC1-DTH-01-2019 – 875171.
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Notes
- 1.
General Data Protection Regulation: https://gdpr.eu, last access: 03.08.2020.
- 2.
Monitoring multidimensional aspects of QUAlity of Life after cancer ImmunoTherapy - an Open smart digital Platform for personalized prevention and patient management: https://h2020qualitop.liris.cnrs.fr/wordpress/index.php/project/.
References
Tian, S., et al.: Smart healthcare: making medical care more intelligent. Global Health J. 3, 62–65 (2019)
Galetsi, P., Katsaliaki, K.: A review of the literature on big data analytics in healthcare. J. Oper. Res. Soc. 71, 1511–1529 (2020)
Flores, M., et al.: P4 medicine: how systems medicine will transform the healthcare sector and society. Pers. Med. 10, 565–576 (2013)
Catarinucci, L., et al.: An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J. 2, 515–526 (2015)
Amato, A., Coronato, A.: An IoT-aware architecture for smart healthcare coaching systems. In: 2017 IEEE 31st AINA, pp. 1027–1034 (2017)
Sallabi, F., Shuaib, K.: Internet of things network management system architecture for smart healthcare. In: 2016 6th DICTAP, pp. 165–170 (2016)
Ahad, A., et al.: 5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions. IEEE Access 7, 100747–100762 (2019)
Frost & Sullivan: Drowning in big data? Reducing information technology complexities and costs for healthcare organizations (2015)
Cafarella, M.J., Halevy, A., Khoussainova, N.: Data integration for the relational web. Proc. VLDB Endow. 2, 1090–1101 (2009)
Venetis, P., et al.: Recovering semantics of tables on the web. Proc. VLDB Endow. 4, 528–538 (2011)
Hassanzadeh, O., et al.: Discovering linkage points over web data. Proc. VLDB Endow. 6, 445–456 (2013)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)
Kalavri, V., et al.: m2r2: a framework for results materialization and reuse in high-level dataflow systems for big data. In: 2013 IEEE 16th CSE, pp. 894–901 (2013)
Chapelle, O., Li, L.: An empirical evaluation of Thompson sampling. Presented at the Proceedings of the 24th NIPS, Granada, Spain (2011)
Xindong, W., et al.: Knowledge engineering with big data. IEEE Intell. Syst. 30, 46–55 (2015)
De Capitani di Vimercati, S., Samarati, P., Jajodia, S.: Policies, models, and languages for access control. In: Bhalla, S. (ed.) DNIS 2005. LNCS, vol. 3433, pp. 225–237. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31970-2_18
Ferraiolo, D.F., et al.: Proposed NIST standard for role-based access control. ACM Trans. Inf. Syst. Secur. 4, 224–274 (2001)
Wang, L., Wijesekera, D., Jajodia, S.: A logic-based framework for attribute based access control. In: The 2004 ACM, FMSE, USA (2004)
Bertino, E., Catania, B., Damiani, M.L., Perlasca, P.: GEO-RBAC: a spatially aware RBAC. In: The 10th SACMAT, Sweden (2005)
Rajpoot, Q.M., Jensen, C.D., Krishnan, R.: Integrating Attributes into Role-Based Access Control. Cham, pp. 242–249 (2015)
Huey, P.: Using oracle virtual private database to control data access. In: Oracle Database Security Guide, Chapter 7 (2012)
Rosenthal, A., Sciore, E.: View security as the basis for data warehouse security. In: 2nd DMDW 2000, Sweden (2000)
Rosenthal, A., Sciore, E.: Administering permissions for distributed data: factoring and automated inference. In: IFIP TC11/WG11.3, Canada, pp. 91–104 (2001)
Haddad, M., Stevovic, J., Chiasera, A., Velegrakis, Y., Hacid, M.-S.: Access control for data integration in presence of data dependencies. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8422, pp. 203–217. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05813-9_14
Angelov, S., Trienekens, J., Kusters, R.: Software reference architectures - exploring their usage and design in practice. In: Drira, K. (eds.) Software Architecture, pp. 17–24. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39031-9_2
Campbell, C.: Top Five Differences between Data Warehouses and Data Lakes. Blue-Granite.com (2017)
Gidley, S.: Tips for managing metadata in a data lake (2017). https://www.oreilly.com/content/tips-for-managing-metadata-in-a-data-lake/
Sawadogo, P.N., Scholly, É., Favre, C., Ferey, É., Loudcher, S., Darmont, J.: Metadata systems for data lakes: models and features. In: Welzer, T., et al. (eds.) ADBIS 2019. CCIS, vol. 1064, pp. 440–451. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30278-8_43
Maroto, C.: Data Lake Security: Four Key Areas to Consider When Securing Your Data Lake. https://www.searchtechnologies.com/blog/data-lake-security
Acknowledgment
Mike Papazoglou is one of the pioneers in service-oriented computing (SOC) and cloud computing, after having contributed ground-breaking work on database systems. He has strongly influenced the continuously growing SOC community he helped create as a co-founder of the International Conference on Service-Oriented Computing (ICSOC), which will celebrate its 30th anniversary in 2022.
Mike was also instrumental in writing the successful Horizon 2020 proposal QUALITOP whose first results are presented in this paper. Finally, Mike is not only an admirable colleague but also a long-term friend.
Bernd got to know Mike in 1983 during an EU ESPRIT project meeting at the University of Patras. Soon after this meeting, Mike joined GMD - Forschungszentrum Informationstechnik GmbH, a major German research institution for applied mathematics and computer science Bernd was also affiliated with at that time. Amal was a Ph.D. student of Mike who looks back to a fruitful scientific apprenticeship in Mike’s lab from 2008 to 2012; He has always been the mentor; role model and continuous supporter and she is honored to have him as an empowering friend since then.
We are deeply honored for the opportunity to contribute to Mike’s Festschrift celebrating his 65th birthday and transition to a life with much freestyle and lesser compulsory. We wish him all the best, many more birthdays, and a state of health that does not require him to rely on our smart health platform.
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Elgammal, A., Krämer, B.J. (2021). A Reference Architecture for Smart Digital Platform for Personalized Prevention and Patient Management. In: Aiello, M., Bouguettaya, A., Tamburri, D.A., van den Heuvel, WJ. (eds) Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future. Lecture Notes in Computer Science(), vol 12521. Springer, Cham. https://doi.org/10.1007/978-3-030-73203-5_7
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