Technology in our phones and cars have shaped consumers into profitable moving objects of interest. Knowing where an object is at any point in time will increase our ability to accurately predict that object's behavior. Tracking moving objects is an obvious application of massively scaling NOSQL technologies and in this presentation we will argue that graph databases are particularly well suited. Graph search can show us interesting connections in our social networks and the addition of location and time allows us to reason about the when and where and help us predict future behavior.
In this presentation we discuss a query framework that can combine geospatial, temporal and social network analysis. In addition, we will discuss recent NoSQL technologies that allow finding objects within a certain geospatial and temporal bounding box with a minimum amount of joins and disk access.
We will discuss increasingly complex queries over moving objects (MOB) in extremely large databases. From simple to complicated:
In this presentation we will demonstrate the queries noted above on a real world data set and show the resulting moving objects on Google Earth.
Efficient 3D and 4D Geospatial Indexing in RDF Stores with a Focus on Moving Objects - by Steve Haflich and Jans Aasman
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