Sheng, H. and Wang, X., 2019. Intelligent retrieval system for ship fault information based on big data analysis. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1019–1025. Coconut Creek (Florida), ISSN 0749-0208.

In order to improve the efficiency of ship fault information retrieval and shorten the retrieval time, an intelligent retrieval system for ship fault information based on big data analysis is designed. The Lucene retrieval component is used to build a multi-level classification module, which matches the query keywords input in the process of ship fault information retrieval with the ship operation status to increase the intelligent retrieval conditions. Combining the big data analysis technology to classify the ship fault information at different levels, the tree classification structure is presented in the process of ship fault status classification, which can refine the classification of ship fault, describe the attributes of retrieval objects in detail, reduce the number of retrieval times, and improve the efficiency of ship fault status retrieval, thus completing the design of the intelligent retrieval system for ship fault information. The experimental results show that under the same conditions, the retrieval time of the system is nearly half shorter than that of the traditional system, and the longest retrieval time can be controlled within 2 seconds, which effectively improves the retrieval efficiency.