Spatio-Temporal Data Mining and Artificial Intelligence for Computer Animation

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Applications (apps) running on mobile devices, such as smart phones and tablet computers, are generating massive databases of spatio-temporal data. In spatio-temporal data, a physical location, such as one given by the Global Position System (GPS) of the mobile device, and a time, such as when the user was at that location, are important. For example, if the app shares information about mosquito density, the place and time where a user reports encountering a heavy mosquito infestation are important. In my research, I will develop new techniques for performing data mining on spatio-temporal data and then implement these ideas in software. Data mining refers to an automated process that runs on a computer, typically a larger central computer that the app communicates with. It takes some data as input, finds patterns in the data, and then either displays the results on the screen or passes them to some other software. Although many kinds of patterns might be considered, I will concentrate on two types, called high utility itemsets and interesting cuboids. In this summary, I will describe only the first type. A high utility itemset is a combination of some items that were reported together (such as a combination of grocery items in a purchase or features observed at a city intersection) that is highly useful to the person looking for patterns in the data. Thus, the person who wants to perform data mining decides what properties would make combinations highly useful and then the data mining software searches through the database to find all the combinations that have those properties. In my research, I will look in particular for combinations that happen at some places and some times. A challenge will be to identify how large of a place (it will vary in size for different problems) and how long a duration (it will also vary) should be considered. One can imagine a chain of convenience store using the results of our research to decide which mobile users should be offered which special offers (buy these two items together right now!) and at what places and times. If successful, this line of research will lead to new software products that will be of immediate interest to retailers and advertisers and may also be useful in less commercial crowd-sourced apps. Although it is not closely related, I will also pursue curiosity-driven research on applying Artificial Intelligence techniques to simulating large numbers of animals. For example, biologists have many questions about how humpback whales are able to work together to herd fish into a compact ball surrounded by a sort of curtain of bubbles and then suddenly all surge up together from under the fish and eat them. Since it is difficult and expensive to see these events occurring, we will create an animated simulation of the events running on the computer. We will encode ideas from the biologists about what the whales may be doing and run the simulation to see if they have the expected effect. Since the biologists will be able to see the animated whales doing the actions, they will be able to judge whether their ideas were correct and whether our simulation is correct. The hundreds of thousands of fish that need to be simulated will be a challenge for our programming and we expect to devise some new techniques to allow the simulations to be efficient enough to run at their natural speed. The results of our research will be of interest to biologists, especially those interested in humpback whales but also those interested in other types of animals, such as bats, that form large groups. The techniques may also be useful in new computer games and animated films.