Autocannibalistic and Anyspace Indexing Algorithms with Applications to Sensor Data Mining

Abstract

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.