Modern ships are supported by internet of things (IoT) to collect ship performance and navigation information. That should be utilized towards digitalization of the shipping industry. However, such information collection systems are always associated with large-scale data sets, so called Big Data, where various industrial challenges are encountered during the respective data handling processes. This study proposes a data handling framework with data driven models (i.e. digital models) to cope with the shipping industrial challenges as the main contribution, where conventional mathematical models may fail. The proposed data driven models are developed in a high dimensional space, where the respective ship performance and navigation parameters of a selected vessel are separated as several data clusters. Hence, this study identifies the distribution of the respective data clusters and the structure of each data cluster in relation to ship performance and navigation conditions. An appropriate structure into the data set of ship performance and navigation parameters is assigned by this method as the main contribution. However, the domain knowledge (i.e. vessel operational and navigation conditions) is also included in this situation to derive a meaningful data structure.

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