Abstract
Data Warehouse (DW) and OLAP systems are acknowledged as first citizens of Business Intelligence (BI) technologies, allowing the on-line analysis of huge volumes of data. However, traditional data-driven BI might not be enough to compete in the context of Industry 4.0, since the collection and analysis of data from the Internet of Things (IoT) requires a more responsive approach. Therefore, in this work, we present a new design methodology for Self-Service DW with On-Demand IoT Data, which is accompanied by a new UML profile for Stream Data Warehouses based on IoT data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Arantes, M., Bonnard, R., Mattei, A.P., De Saqui-Sannes, P.: General architecture for data analysis in industry 4.0 using SYSML and model based system engineering. In: 2018 annual IEEE international systems conference (SysCon), pp. 1–6. IEEE (2018)
Bimonte, S., Boussaid, O., Schneider, M., Ruelle, F.: Design and implementation of active stream data warehouses. Int. J. Data Warehouse. Min. (IJDWM) 15(2), 1–21 (2019)
Bimonte, S., Edoh-Alove, É., Nazih, H., Kang, M.A., Rizzi, S.: Protolap: rapid olap prototyping with on-demand data supply. In: Proceedings of the sixteenth international workshop on Data warehousing and OLAP, pp. 61–66 (2013)
Boulil, K., Bimonte, S., Pinet, F.: Conceptual model for spatial data cubes: a UML profile and its automatic implementation. Comput. Stand. Interfaces 38, 113–132 (2015)
Cai, H., Gu, Y., Vasilakos, A.V., Xu, B., Zhou, J.: Model-driven development patterns for mobile services in cloud of things. IEEE Trans. Cloud Comput. 6(3), 771–784 (2016)
Ciccozzi, F., Spalazzese, R.: MDE4IoT: supporting the internet of things with model-driven engineering. IDC 2016. SCI, vol. 678, pp. 67–76. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-48829-5_7
Cuzzocrea, A., Mazón, J.N., Trujillo, J., Zubcoff, J., et al.: Model-driven data mining engineering: from solution-driven implementations to ‘composable’ conceptual data mining models. Int. J. Data Min. Model. Manag. 3(3), 217–251 (2011)
Golfarelli, M., Rizzi, S., Turricchia, E.: Modern software engineering methodologies meet data warehouse design: 4WD. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 66–79. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23544-3_6
Lennerholt, C., van Laere, J., Söderström, E.: Implementation challenges of self service business intelligence: a literature review. In: 51st Hawaii International Conference on System Sciences, Hilton Waikoloa Village, Hawaii, USA, 3–6 January 2018, vol. 51, pp. 5055–5063. IEEE Computer Society (2018)
Marouane, H., Makni, A., Bouaziz, R., Duvallet, C., Sadeg, B.: Definition of design patterns for advanced driver assistance systems. In: Proceedings of the 10th Travelling Conference on Pattern Languages of Programs, p. 3. ACM (2016)
Mezghani, E., Exposito, E., Drira, K.: A model-driven methodology for the design of autonomic and cognitive iot-based systems: Application to healthcare. IEEE Trans. Emerg. Top. Comput. Intell. 1(3), 224–234 (2017)
Nguyen, X.T., Tran, H.T., Baraki, H., Geihs, K.: Frasad: a framework for model-driven IoT application development. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 387–392. IEEE (2015)
Patel, P., Cassou, D.: Enabling high-level application development for the internet of things. J. Syst. Softw. 103, 62–84 (2015)
Plazas, J.E., Bimonte, S., De Sousa, G., Corrales, J.C.: Data-centric UML profile for wireless sensors: application to smart farming. Int. J. Agri. Environ. Inf. Syst. (IJAEIS) 10(2), 21–48 (2019)
Saggi, M.K., Jain, S.: A survey towards an integration of big data analytics to big insights for value-creation. Inf. Process. Manag. 54(5), 758–790 (2018)
Taktak, S., Alshomrani, S., Feki, J., Zurfluh, G.: The power of a model-driven approach to handle evolving data warehouse requirements. In: 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017), pp. 169–181. SciTePress (2017)
Thramboulidis, K., Christoulakis, F.: Uml4iot–a UML-based approach to exploit iot in cyber-physical manufacturing systems. Comput. Ind. 82, 259–272 (2016)
Acknowledgements
This work was partially supported by French ANR projects VGI4Bio (ANR-17-CE04-0012) and “Investissements d’ Avenir” through the IDEX-ISITE initiative CAP 20–25 (ANR-16-IDEX-0001), and the Colombian project IoT-Agro of Universidad del Cauca (VRI ID4633). We also thank COLCIENCIAS (Colombia) for the PhD scholarship granted to Julián Eduardo Plazas.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Plazas, J.E., Bimonte, S., Schneider, M., de Vaulx, C., Corrales, J.C. (2020). Self-service Business Intelligence over On-Demand IoT Data: A New Design Methodology Based on Rapid Prototyping. In: Darmont, J., Novikov, B., Wrembel, R. (eds) New Trends in Databases and Information Systems. ADBIS 2020. Communications in Computer and Information Science, vol 1259. Springer, Cham. https://doi.org/10.1007/978-3-030-54623-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-54623-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-54622-9
Online ISBN: 978-3-030-54623-6
eBook Packages: Computer ScienceComputer Science (R0)