The history of humankind is intimately connected to insects. Insect borne diseases kill a million people and destroy tens of billions of dollars worth of crops annually. However, at the same time, beneficial insects pollinate the majority of crop species, and it has been estimated that approximately one third of all food consumed by humans is directly pollinated by bees alone. Given the importance of insects in human affairs, it is somewhat surprising that computer science has not had a larger impact in entomology. We believe that recent advances in sensor technology are beginning change this, and a new field of Computational Entomology will emerge. We will demonstrate an inexpensive sensor that allows us to capture data from flying insects, and the software that allows us to analyze the data. Moreover, we will distribute both the sensors and software for free, to parties willing to take part in a crowdsourcing project on insect classification.

Efficient indexing is at the heart of many data mining algorithms. A simple and extremely effective algorithm for indexing under any metric space was introduced in 1991 by Orchard. Orchard's algorithm has not received much attention in the data mining and database community because of a fatal flaw; it requires quadratic space. In this work we show that we can produce a reduced version of Orchard's algorithm that requires much less space, but produces nearly identical speedup. We achieve this by casting the algorithm in an anyspace framework, allowing deployed applications to take as much of an index as their main memory/sensor can afford. As we shall demonstrate, this ability to create an anyspace algorithm also allows us to create auto-cannibalistic algorithms. Auto-cannibalistic algorithms are algorithms which initially require a certain amount of space to index or classify data, but if unexpected circumstances require them to store additional information, they can dynamically delete parts of themselves to make room for the new data. We demonstrate the utility of auto-cannibalistic algorithms in a fielded project on insect monitoring with low power sensors, and a simple autonomous robot application.

The problem of efficiently finding images that are similar to a target image has attracted much attention in the image processing community and is rightly considered an information retrieval task. However, the problem of finding structure and regularities in large image datasets is an area in which data mining is beginning to make fundamental contributions. In this work, we consider the new problem of discovering shape motifs, which are approximately repeated shapes within (or between) image collections. As we shall show, shape motifs can have applications in tasks as diverse as anthropology, law enforcement, and historical manuscript mining. Brute force discovery of shape motifs could be untenably slow, especially as many domains may require an expensive rotation invariant distance measure. We introduce an algorithm that is two to three orders of magnitude faster than brute force search, and demonstrate the utility of our approach with several real world datasets from diverse domains.

In many applications, it is desirable to monitor a streaming time series for predefined patterns. In domains as diverse as the monitoring of space telemetry, patient intensive care data, and insect populations, where data streams at a high rate and the number of predefined patterns is large, it may be impossible for the comparison algorithm to keep up. We propose a novel technique that exploits the commonality among the predefined patterns to allow monitoring at higher bandwidths, while maintaining a guarantee of no false dismissals. Our approach is based on the widely used envelope-based lower bounding technique. Extensive experiments demonstrate that our approach achieves tremendous improvements in performance in the offline case, and significant improvements in the fastest possible arrival rate of the data stream that can be processed with guaranteed no false dismissal.