# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 2 Related Work  
Research on the data management of ADS-B is limited, but this problem is consistent with the management and use of aviation big data in related fields. Therefore, we present some related work that specifically includes database storage methods, column storage methods, and point-based spatiotemporal indexing methods, among others.  
Traditional database storage methods, such as Google's Bigtable [23], are based on an ordered multi-table structure, and the use of keywords and timestamps for queries. The principle is similar to the GAE DataStore [24], which was one of the earliest systems to use the idea of spatial dissection. In addition, there are general-purpose distributed frameworks based on the Hadoop [25] and Spark system [26]. On the other hand, the general idea of column-based storage [27] has a long tradition in data processing, such as MonetDB [28], and is now commonly used in the current storage database; however, these databases are not well adapted to the embedding and querying of other field parameters of the document.  
Similarly, point-based spatiotemporal indexing methods are mainly divided into two categories: spatiotemporal separated indexing and spatiotemporal integrated indexing. The former category includes many tree structures in GIS tools that are typical representatives, which can only use indexes in the spatial dimension and can only filter in the temporal dimension. Examples include Quad-tree [29], Quad-tree + Inverted Index [30], R-tree [31], and Partial Local Quad-tree [32]. These indexing methods may experience a significant decrease in query efficiency when dealing with problems such as spatiotemporal applications, especially with the influence of the query situation and conditions. In contrast, the latter category includes tree-based spatiotemporal indexes, which directly transform traditional spatial data into spatiotemporal indexes by combining time more easily. Examples of this category include CloST (Hierarchical quad-tree) [33], Elite (Oct-tree) [34], and grid-based spatiotemporal indexes by combining the grid and Space Fill Curve (SFC) such as Z-curve [35] and Hillbert-curve [36] to convert multiple dimensions of space and time into one-dimensional dictionary indexes. Examples of this include CoPST (SFC) [37] and GeoMesa (Geohash + SFC) [38]. However, the blockiness, readability, and identifiability of the grid are not incorporated into the practical use in these cases.  
Based on the considerations mentioned above, we have selected the document-based database MongoDB for experimental operations. This database allows for the retention of certain parameters of ADS-B, such as takeoff and landing airport heading angles, in the form of JSON data. We have used the grid indexing method, considering that the grid profile is compatible with the flight chart. The resulting grid code has the characteristics of both indexing primary key and position identification. Furthermore, this method decouples the altitude and time dimensions to improve efficiency, and this performs well in extracting the ADS-B data in the target airspace.