Abstract
The purpose of this article is to investigate whether or not including context data (context-awareness) in a classical data mining process would enhance the overall results. For that, the efficiency of the predictions was analyzed and compared: in a classical data mining process versus a context-aware data mining process. The two processes were applied on existing data collected from more weather stations to predict the future soil moisture. The classic data mining process considers historical data on soil moisture and temperature in a time interval, while the context-aware process also adds collected context information on air temperature for that location. The obtained results show advantages of CADM over classical DM.
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
Notes
References
Chang, C.-C., Hsu, C.-W., Lin, C.-J.: The analysis of decomposition methods for support vector machines. IEEE Trans. Neural Networks 11(4), 1003–1008 (2000)
Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)
FAO: Water for sustainable food and agriculture. A report produced for the G20 presidency of Germany (2017)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. In: The Morgan Kaufmann Series in Data Management Systems, pp. 230–240 (2006)
Kotte, O., Elorriaga, A., Stokic, D., Scholze, S.: Context sensitive solution for collaborative decision making on quality assurance in software development processes. In: Intelligent Decision Technologies: Proceedings of the 5th KES International Conference on Intelligent Decision Technologies (KES-IDT 2013), vol. 255, p. 130. IOS Press (2013)
Lee, S., Chang, J., Lee, S.-G.: Survey and trend analysis of context-aware systems. Inf. An Int. Interdiscip. J. 14(2), 527–548 (2011)
Lino, G.S., Berry, M.J.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, New York (2011)
Matei, O., Anton, C., Bozga, A., Pop, P.: Multi-layered architecture for soil moisture prediction in agriculture 4.0. In: Proceedings of International Conference on Computers and Industrial Engineering, CIE, no. 2, p. 15 (2017)
Matei, O., Rusu, T., Bozga, A., Pop-Sitar, P., Anton, C.: Context-aware data mining: embedding external data sources in a machine learning process. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 415–426. Springer (2017)
Matei, O., Rusu, T., Petrovan, A., Mihuţ, G.: A data mining system for real time soil moisture prediction. Procedia Eng. 181, 837–844 (2017)
Scholze, S., Barata, J., Stokic, D.: Holistic context-sensitivity for run-time optimization of flexible manufacturing systems. Sensors 17(3), 455 (2017)
Scholze, S., Kotte, O., Stokic, D., Grama, C.: Context-sensitive decision support for improved sustainability of product lifecycle. In: Intelligent Decision Technologies: Proceedings of the 5th KES International Conference on Intelligent Decision Technologies (KES-IDT 2013), vol. 255, p. 140. Courier Corporation (2013)
Scholze, S., Stokic, D., Kotte, O., Barata, J., Di Orio, G., Candido, G.: Reliable self-learning production systems based on context aware services. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4872–4877. IEEE (2013)
Stokic, D., Scholze, S., Kotte, O.: Generic self-learning context sensitive solution for adaptive manufacturing and decision making systems. In: Proceedings of the ICONS14—The Ninth International Conference on Systems, Nice, pp. 23–27 (2014)
Vajirkar, P., Singh, S., Lee, Y.: Context-aware data mining framework for wireless medical application. In: International Conference on Database and Expert Systems Applications, pp. 381–391. Springer (2003)
Witten, I.H., Frank, E., Hall, M.A.: Data mining: practical machine learning tools and techniques. In: Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, vol. 104, p. 113 (2005)
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
Avram, A., Matei, O., Pintea, CM., Pop, P.C., Anton, C.A. (2020). Context-Aware Data Mining vs Classical Data Mining: Case Study on Predicting Soil Moisture. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-20055-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20054-1
Online ISBN: 978-3-030-20055-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)