# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 5 Discussion
### Limitations and Future Research  
Although the presented machine-learning models have good performance with respect to the ETA-pilot, the accuracies of the presented machine-learning models were not ideal and future research is needed to test more machine-learning algorithms. The trained SOM model yields the worst robustness,  
Figure 12: Fuel consumption in real time compared with historical consumption curves. compared to the MLP and to the existing tools, probably because SOMs require more data samples to converge into a state that yields a similar performance to the regression models. The collection of roughly 60 ship-months should be extended to cover a longer time period per ship. Still the evaluation showed that both MLP and SOM generalize well on different given data sets. This is largely because the appropriate hyper parameters enabled the models to be generalizable to some extent when trained on different ferries, meaning that the models' predictive performance is not bound to one ship. However, it should also note that the current hyperparameters might not work that well for ships that do not sail on the same routes, such as tankers and dry bulkers instead of ferries. Therefore, there is a huge unexploited potential to be trained on different ships and routes in the future. Hull fouling, maintenance costs could also be included in the model to predict real voyage costs (which is a composite of more factors than only fuel costs). Current data do not include critical data points such as trim or draught. The specific type of fuel that was used is also unknown but might have a big impact on the engines' performance. Ship sensors work at a higher time resolution than 10 min, but only a 10-min average is stored on hard disks. An increase of the time resolution of stored sensor data is a promising way to collect more data in order to train better models. For a holistic energy-efficiency analysis, one has to include long-term costs as for example maintenance costs, that also depend on the operational profile of the engines. The slow-steaming in shipping might save fuel but can increase maintenance overhead in cases in which the engines were designed for a different speed. All these create a vast space for big data analytics. On the other hand, the work in big data analytics and its derived discussions above also indicate there is much space for future ethnographic research, which can be employed to discover the meanings behind big data analytics [28]. The words of the sociologist [PERSON] is that not everything that counts can be counted, and not everything that can be counted counts. If human users can gain knowledge (as a social construct) during the use of the technology, would the technology support the crews' social interactions (e.g., exchanging ideas to save more fuel)? How would these types of data-driven predictive tools influence communication in the communities of practice, or the communication between ship and shore, considering collaborative learning is critical for maritime EE optimization practices [24]? More importantly, how is the known issue of lack of trust between ship and shore organization [26] likely to be influenced by the introduction of the human-AI decision systems also remains to be explored, as the intelligent system will be more involved in the ship operations but still be put under \"administration\" of the shore organization? It is important to consider the role of advanced technology and avoid the erosion of the trust and communication link between ship and shore. Technology should support the whole organization to evaluate performance and understand what concrete operations could constitute \"best practice\" or \"worst practice\" of EE optimization. A feasible research direction is to understand how the big data analytics could shape the navigators' work in the actual field and how would the management practices need to accommodate this. For designers, this is also a question pertinent to \"work as imagine\" vs. \"work as done\" [60].