Abstract
The development of the Internet and cloud computing has set up a matured environment for developing and deploying big data services. The main objective of requirements engineering for big data is to capture big data service users’ needs and provider’s capabilities, and to identify value added service use cases for big data technology in a given organizational context. Major objectives may include: collect real-time data about the world, search for useful information in large data sets, gain insights about given problems by data analytics, predict possible trend of interesting subjects, and make decisions for the next immediate actions. In this paper, we propose a big data service requirements analysis framework, which aims to provide useful guidelines for eliciting service requirements, selecting the right services architectures and evaluate the available technological services implementations. For services under operation, we suggest data analysis to service logs to elicit user’s changing needs, to evaluate the run-time service performance and to check compliance to general standards and domain-specific regulations. Example cases from eHealth and industry 4.0 are discussed to illustrate the proposed service requirements framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alves de Medeiros, A., van der Aalst, W.M., Weijters, A.: Quantifying process equivalence based on observed behavior. Data Knowl. Eng. 64, 55–74 (2008)
van der Aalst, W.M., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19
Beatty, J., Wiegers, K.: Forward thinking for tomorrow’s projects requirements for business analytics. Seilevel Whitepaper (2015)
Bretthauer, M., Aabakken, L., Dekker, E., et al.: Reporting systems in gastrointestinal endoscopy: requirements and standards facilitating quality improvement: European society of gastrointestinal endoscopy position statement. United European Gastroenterol. J. (2016). https://doi.org/10.1177/2050640616629079
Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)
Chung, L., et al.: NonFunctional Requirements in Software Engineering. Springer, New York (2012). https://doi.org/10.1007/978-1-4615-5269-7
Computing Community Consortium, Computing Research Association. Challenges and Opportunities with Big Data: A community white paper developed by leading researchers across the United States. White Paper, February 2012
Cysneiros, L.M.: Requirements engineering in health care domain. In: Proceedings of the IEEE Joint 10th International Requirements Engineering Conference, pp. 760–773, September 2002
Deutch, D., Milo, T.: A quest for beauty and wealth (or, business processes for database researchers). In: Proceedings of the Thirtieth ACM Sigmod-Sigact-Sigart Symposium on Principles of Database Systems, pp. 1–12 (2011)
David, R., Dong, F., Braun, Y., et al.: MyHealthAvatar survey: scenario based user needs and requirements. In: 2014 6th International Advanced Research Workshop on Silico Oncology and Cancer Investigation (IARWISOCI), pp. 1–5. IEEE (2014)
Dalrymple, P.W., Rogers, M., An, Y.: Effect of early requirements analysis and participative design on staff in an urban health clinic: civic engagement through collaboration. In: iConference 2009, Chapel Hill, NC (2009)
Fabian, B., Ermakova, T., Junghanns, P.: Collaborative and secure sharing of healthcare data in multi-clouds. Inf. Syst. 48, 132–150 (2015)
Ghasemi, M., Amyot, D.: Process mining in healthcare: a systematised literature review. IJEH 9(1), 60–88 (2016)
Rodrigues, J.P.C., de la Torre, I., Fernández, G., López-Coronado, M.: Analysis of the security and privacy requirements of cloud- based electronic health records systems. J. Med. Internet Res. 15(8), e186 (2013)
Grigori, D., Corrales, J.C., Bouzeghoub, M., Gater, A.: Ranking BPEL processes for service discovery. IEEE Trans. Serv. Comput. 3, 178–192 (2010)
Hua, L., Gong, Y.: Usability evaluation of a voluntary patient safety reporting system: understanding the difference between predicted and observed time values by retrospective think-aloud protocols. In: Kurosu, M. (ed.) HCI 2013. LNCS, vol. 8005, pp. 94–100. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39262-7_11
Hu, H., Wen, Y., Chua, T.S., et al.: Toward scalable systems for data analytics: a technology tutorial. IEEE Access 2, 652–687 (2014)
IBM: Data Driven Healthcare Organizations Use Data analytics for Big Gains (2013)
Kushniruk, A.: Evaluation in the design of health information systems: application of approaches emerging from usability engineering. Comput. Biol. Med. 32(3), 141–149 (2002)
Kambatla, K., Kollias, G., Kumar, V., et al.: Trends in data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)
Liu, L., Feng, L., Cao, Z., Li, J.: Requirements engineering for health data analytics: challenges and possible directions. In: RE 2016, pp. 266–275 (2016)
Liu, L., Zhou, Q., Liu, J., Cao, Z.: Requirements cybernetics: elicitation based on user behavioral data. J. Syst. Softw. 124, 187–194 (2017)
Llewellynn, T., Koller, S., Goumas, G., Leitner, P., Dasika, G., Wang, L., Tutschku, K., Fernández-Carrobles, M., Deniz, O., Fricker, S., Storkey, A., Pazos, N., Velikic, G., Leufgen, K., Dahyot, R.: BONSEYES: platform for open development of systems of artificial intelligence. In: Conference Computing Frontiers, pp. 299–304 (2017). Invited paper
Middleton, B., Bloomrosen, M., Dente, M.A., et al.: Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J. Am. Med. Inform. Assoc. 20(e1), e2–e8 (2013)
Raghupathi, W., Raghupathi, V.: Data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)
Stonebraker, M., et al.: The 8 requirements of real-time stream processing. ACM SIGMOD Rec. 34(4), 42–47 (2005)
Teixeira, L., Ferreira, C., Santos, B.S.: User-centered requirements engineering in health information systems: a study in the hemophilia field. Comput. Methods Programs Biomed. 106(3), 160–174 (2012)
Weidlich, M., Mendling, J., Weske, M.: Efficient consistency measurement based on behavioral profiles of process models. IEEE Trans. Softw. Eng. 37, 410–429 (2011)
Yu, E.S.K.: Towards modelling and reasoning support for early-phase requirements engineering. In: Proceedings of the Third IEEE International Symposium on Requirements Engineering, pp. 226–235, 6–10 Jan 1997
Acknowledgement
Partial financial support by National Science and Technology Support Program (No. 2015BAH14F02) and the National Natural Science Foundation of China (No. 61432020) are acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yasin, A., Liu, L., Cao, Z., Wang, J., Liu, Y., Ling, T.S. (2018). Big Data Services Requirements Analysis. In: Kamalrudin, M., Ahmad, S., Ikram, N. (eds) Requirements Engineering for Internet of Things. APRES 2017. Communications in Computer and Information Science, vol 809. Springer, Singapore. https://doi.org/10.1007/978-981-10-7796-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-7796-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7795-1
Online ISBN: 978-981-10-7796-8
eBook Packages: Computer ScienceComputer Science (R0)