Meng T, Jing X, Yan Z, Pedrycz W (2020) A survey on machine learning for data fusion. Inf Fusion 57:115–129
Zheng Y (2015) Methodologies for cross-domain data fusion: an overview. IEEE Trans Big Data 1(1):16–34
Liu J, Li T, Xie P, Du S, Teng F, Yang X (2020) Urban big data fusion based on deep learning: an overview. Inf Fusion 53:123–133
Khan S, Nazir S, García-Magariñob I, Hussain A (2021) Deep learning-based urban big data fusion in smart cities: towards traffic monitoring and flow-preserving fusion. Comput Electr Eng 89:106906
Soldi G, Gaglione D, Forti N, Millefiori LM, Braca P, Carniel S, Di Simone A, Iodice A, Riccio D, Daffin’a FC et al (2021) Space-based global maritime surveillance. part ii: artificial intelligence and data fusion techniques. IEEE Aerosp Electron Syst Mag 36(9):30–42
Karagiannopoulou A, Tsertou A, Tsimiklis G, Amditis A (2022) Data fusion in earth observation and the role of citizen as a sensor: a scoping review of applications, methods and future trends. Remote Sens 14(5):1263
Rowley J (2007) The wisdom hierarchy: representations of the dikw hierarchy. J Inf Commun Sci 33(2):163–180
Zhang L, Xie Y, Xidao L, Zhang X (2018) Multi-source heterogeneous data fusion. In: 2018 International conference on artificial intelligence and big data (ICAIBD). IEEE, pp 47–51
Raghuwanshi SK, Pateriya R (2019) Recommendation systems: techniques, challenges, application, and evaluation. In: Soft computing for problem solving. Springer, Berlin, pp 151–164
Eckhardt A (2009) Various aspects of user preference learning and recommender systems. Dateso 2009, pp. 56–67. ISBN 978-80-01-04323-3
Trahms C, Wölker Y, Handmann P, Visbeck M, Renz M (2022) Data fusion for connectivity analysis between ocean regions. In: Submitted to: 2022 IEEE 18th International Conference on eScience (eScience)