# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 5 Discussion  
### Design of Decision Support Systems and Management Practices  
The commercially available EE monitoring toolboxes, like the ETA-pilot systems that are employed on dozens of ferry vessels, are mainly used to display ship sensor information (wind, trim, fuel consumption etc.). A huge problem is that such systems usually tend to assume the display of fuel-consumption data curves equals knowledge creation. However, the ethnographic findings reject this simple assumption, but identify the need to involve context in the process of knowledge mobilization [57]. A major problem is that the design of this existing tool did not really afford the meanings behind the numbers, thus the crews would feel difficult to perform efficient monitoring or flexibly adapt themselves under new circumstances. For example, 20 kg fuel consumption shown per nautical mile is just a number about the rate of burning fuel, but the crews have little understanding about its meaning in EE operations. The information about the ongoing trend to spend more than 5 kg fuel per nautical mile than the \"historical average consumption performance\" provides probably more support to their decision making. In other words, a better decision support interface design should provide the meanings behind the visualizations to enable direct perception and manipulations, emphasizing the context in which the knowledge could be applied and information could be operationalized. Confronted with the energy-saving challenges in the design and use of technology identified in the ethnographic study, the research team took the predictive-analytical approaches with big data analytics to explore a path forward. The big data analytics conducted and their evolution in this paper have clearly suggested that, given an operational and environmental profile, machine learning could be feasible to (1) predict fuel consumption in real time and (2) represent the most \"similar\" journeys and their fuel consumption data (as benchmarks). This is essentially providing a context by attaching meanings to visual data and making it much easier for human operators to understand, e.g., how this maneuvering would affect fuel consumption tendency, or how the energy consumption curve for the same ferry ship under the similar conditions would look like. One concrete example is given here to show how big data analytics could be employed to support decision making under different weather conditions. Bad weather could lead to more fuel consumption while good weather could help to save fuel. The expected values of fuel consumption under both situations can be considered as useful indicators or measures, to suggest the performance of the real-time fuel consumption, so that the navigators could have an easily understandable metrics to evaluate their own EE practice and possibilities to develop their knowledge and awareness in real-time (or retrospectively). Figure 12 shows a simplified heuristic design example. The interface plots three curves regarding fuel consumption from time 14:00 to 23:00. Based on the machine-learning algorithm, trips under similar weather conditions were classified from the historical big data set. Then the average fuel consumption values (kg per nautical mile) for \"best\" and \"worst\" circumstances are plotted as benchmarks. The dynamic yellow curve describes the real-time fuel consumption, with the dotted line predicting how the consumption is going to be if maintaining the same speed and course under the current weather conditions. In principle the yellow consumption curve should be located between the two other lines (normal performance), but it might go higher than the worst case or lower than the best case, which suggests the \"moments of learning\" for seafarers and the management. The idea is not to present a fully-fledged design solution but to suggest a promising direction for system design: by exploiting the power of big data analytics we may directly relieve some of the existing pains and create desired gains.