Whether its the movement of ships, vehicles, financial transactions, email, or user activity, motion analytics provides your organization a simple, yet sophisticated way to track the movements of your data and automatically detect behaviors and flag anomalies. Using historical data and machine learning techniques, Analyze develops custom algorithms that address your organization's needs. These algorithms learn and adapt to changes in your data to ensure that each behavior signature remains relevant.
Understanding the behavior of moving data has a wide range of applications from nuclear physics and astronomy to counterterrorism and consumer applications. Predicting the location and behavior of data and drawing meaningful conclusions from their motion is conceptually and mathematically challenging. Many theoretical approaches have been developed over the years, and understanding which techniques are best applied to a particular problem is more art than science. For example, consider a set of ships moving on the surface of the ocean. Embedded in the movements of these ships is a whole host of information about the behaviors and intentions of their owners, captains and crews. Why does one vessel take a seemingly inefficient route between two places in perfectly good weather? Does it want to avoid being seen by other ships? Why do two fishing vessels from different countries rendezvous on the open ocean? Are they illegally exchanging their catch, or engaged in human trafficking? Why does a cruise ship's movements closely follow the wind and ocean currents? Is it adrift and in distress?
The techniques used to analyze objects in motion depend on the information sought. Analyze data scientists bring a range of mathematical and algorithmic approaches to motion analytics, including:
Graph Theoretical and Network Techniques
Sometimes motion data is best understood as a collection of route and destinations (edges and vertices). Graph theory is a rich mathematical discipline with established solutions to many common problems such as connectedness, path finding, distances and probabilistic prediction. Interconnectedness in human relationships (sometimes called social network analysis, but not just applied to applications like Facebook and Twitter) can also be explored effectively through graph theoretical techniques.
Geometric and Pattern Matching Techniques
Sometimes motion is expressed in regular, predictable or distinctive mathematical or geometric patterns. A fishing trawler might leave port, cast its nets in a particular manner, retrieve them at a later time and return to its home port whereas a ferry may travel back and forth between two destinations in a straight line, never deviating from its pattern. When drawn out in time series or other projections, these ships create unique geometric patterns. Shape and object recognition techniques, series pattern detection and genetic algorithms help us understand, organize and classify this kind of motion.
Machine Learning Techniques
Machine learning techniques help make sense of complex, unorganized and seemingly random data sets. Clustering identifies data elements that share a common set of features or properties. Correlation tells us whether some features of data (for example, the size or class of ship) help us predict other features (such as its speed or location). Dimensionality reduction helps us reduce the data search space to focus on only those pieces of information that matter. The most powerful techniques, prediction techniques, tell us what an object in motion should do based on past behavior as well as whether it is behaving outside the norm.