Performance and Scalability of a Large-Scale N-gram Based Information Retrieval System

Ethan Millar, Dan Shen, Junli Liu, Charles Nicholas

Abstract


Information retrieval has become more and more important due to the rapid growth of all kinds of information. However, there are few suitable systems available. This paper presents a few approaches that enable large-scale information retrieval for the TELLTALE system. TELLTALE is an information retrieval environment that provides full-text search for text corpora that may be garbled by OCR (optical character recognition) or transmission errors, and that may contain multiple languages. It can find similar documents against a 1 kB query from 1 GB of text data in 45 seconds. This remarkable performance is achieved by integrating new data structures and gamma compression into the TELLTALE framework. This paper also compares several different types of query methods such as tf.idf and incremental similarity to the original technique of centroid subtraction. The new similarity techniques give better performance but less accuracy.

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