Tracking Moving Objects in a Graph Database

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 behaviour. 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 behaviour.

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:

Which MOB are within a given bound from a given latitude, longitude, and time?
Detect when two given MOBs were within a given distance.
Given a MOB, detect all MOBs ever within a given distance.
Find all occurrences of two MOBs within a certain distance.

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.

Jans Aasman started his career as an experimental and cognitive psychologist, earning his PhD in cognitive science with a detailed model of car driver behavior using Lisp and Soar. He has spent most of his professional life in telecommunications research, specializing in intelligent user interfaces and applied artificial intelligence projects. From 1995 to 2004, he was also a part-time professor in the Industrial Design department of the Technical University of Delft. Jans is currently the CEO of Franz Inc., the leading supplier of commercial, persistent, and scalable RDF database products that provide the storage layer for powerful reasoning and ontology modeling capabilities for Semantic Web applications.