Autocannibalistic and Anyspace Indexing Algorithms with Application 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 tocreate 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. Keywords Indexing, Anyspace Algorithms, Data Mining, Sensors.