Moving object: an object that generates information including timestamps when the position of the object changes over time. For example, at the time point T, an airplane that is flying can be viewed as a moving object because information related to the status, position, altitude, and speed is generated at this point. The status information indicates whether the airplane is flying or parked, and the position information indicates the latitude and longitude.
Spatiotemporal trajectory: a data model that records continuous position changes of objects in Moving Objects Databases (MODs). For more information, see Terms. You can use MODs to perform basic operations and query and analyze spatiotemporal trajectory data.
For spatiotemporal trajectory data of moving objects, Ganos provides the following major features:
Trajectory data storage at low costs
Write multiple trajectory points in a batch. You can write two-dimensional trajectory points that contain timestamps and custom attributes.
Use models to store trajectory data. You can use two types of storage models: point model and line model. Compared with the point model, the line model uses secondary indexes to make queries efficient and reduce storage costs.
Split trajectories into trajectory objects for storage. Four types of built-in data split policies are available. You can configure policies to split trajectories based on the number of trajectory points, distance, latitude and longitude, and point in time. You can also split trajectory data that uses the point model.
Efficient queries for spatiotemporal data
Perform basic queries. You can specify an ID and a time range to query data. You can also query spatiotemporal data by attribute, spatial range, or time range.
Perform efficient spatiotemporal queries. You can build Z2 indexes or Z3 indexes for point models, or build XZ2 indexes or XZ2T indexes for line models to make spatiotemporal queries efficient.
Perform multidimensional queries based on point models. You can query data by attribute and specified spatiotemporal range. For example, you can build a spatiotemporal index based on the row key of a base table and build secondary indexes based on other attributes. We recommend that you do not build more than two secondary indexes.