AIRSTD: An approach for indexing and retrieving spatio-temporal data

Geographical information about the real world changes rapidly with time. We can see examples of these changes when we look at any city or area. New buildings are built, new roads and highways are constructed, and many other new constructions are added or updated. Moreover, there are changes to the geometries of physical (i.e., buildings) and abstract (i.e., land parcels) entities that need to be recorded. Another example of spatial information that is changing with time is moving objects like airplanes, cars or even moving people. The databases that hold these types of information should always be updated to maintain a record of the old information, which could be needed for analysis or studies by specialists. Databases that have geographical data or spatial data which is changing with time are called spatio-temporal databases. Most spatio-temporal databases fall under three main categories of applications: (1) applications dealing with moving objects where continuous data is needed, (2) applications dealing with objects that have certain geometries and are located in space and (3) applications that combine the other two.; Spatio-temporal databases need to store information about space and the dynamic change of objects over time. We need to store these changes while keeping the old information in the database. Dealing with this issue increased the difficulty of finding efficient models that handle the time and space complexities of spatio-temporal databases.; The main goal our study is to find an efficient way to deal with spatio-temporal data, including the ability to store, retrieve, update, and query. We offer an Approach for Indexing and Retrieving Spatio-Temporal Data (AIRSTD). We concentrate on two main objectives: (1) Provide an efficient algorithm to retrieve spatio-temporal data at any time T by designing and using two indexes: (i) a temporal index using a BS-tree structure and (ii) a spatial index using an R-Tree structure. (2) Provide an efficient algorithm to build a snapshot at any time T using the two indexes (temporal and spatial).; In order to achieve these goals we do the following: (1) Build a spatio-temporal database model, associated indexing scheme, and the data structures used in the proposed algorithms. (2) Propose a temporal indexing structure for clustering the spatio-temporal data according to their temporal and spatial attributes. (3) Use a spatial indexing structure for indexing current information according to the spatial information.