Distributed Spatial Retrieval and Organization of Massive Ocean Spatiotemporal Data

Marine environmental data field is a kind of high-dimensional and multi variable spatial-temporal data field, which is large-volume, time-varying, multi-format and has strong diversity. Spatiotemporal data is the basis of data browsing and data analysis, so improving the efficiency of retrieval has become a reliable solution to improve the timeliness of processing. Aiming at the organization and spatial retrieval of massive marine spatiotemporal data. Our work was carried out from the following aspects, one is to unify the storage organization and build a data model for ocean spatiotemporal data, the other is to construct distributed spatial data index of ocean spatiotemporal data. Ocean spatiotemporal data model realizes the unified organization modeling of data with different sources, high dimensions, diverse forms, heterogeneous formats and high spatial temporal connection. We describe this model from data sources, collection time and geospatial information. Based on the strategy of data segmentation and spatial region division, two kinds of distributed spatial data retrieval indexes are designed and implemented: single level R-tree spatial distributed data index based on ocean spatiotemporal data fragmentation and multi-level spatial distributed data indexing of R-tree + grid index based on global geospatial partitioning. The experiment results prove that our strategy could effectively minimize the interference of irrelevant data when retrieving, reduce retrieval redundancy, efficiency enhanced the task timeliness of the ocean spatiotemporal data processing platform.