  With the rapid amassing of spatial-temporal (ST) ocean data, many
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, including climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated but with unique characteristics, e.g., diverse regionality and high
sparsity. These characteristics make it difficult to design and train STDM
models on ST ocean data. To the best of our knowledge, a comprehensive survey
of existing studies remains missing in the literature, which hinders not only
computer scientists from identifying the research issues in ocean data mining
but also ocean scientists to apply advanced STDM techniques. In this paper, we
provide a comprehensive survey of existing STDM studies for ocean science.
Concretely, we first review the widely-used ST ocean datasets and highlight
their unique characteristics. Then, typical ST ocean data quality enhancement
techniques are explored. Next, we classify existing STDM studies in ocean
science into four types of tasks, i.e., prediction, event detection, pattern
mining, and anomaly detection, and elaborate on the techniques for these tasks.
Finally, promising research opportunities are discussed. This survey can help
scientists from both computer science and ocean science better understand the
fundamental concepts, key techniques, and open challenges of STDM for ocean
science.
