Sample Sizes for Query Probing in Uncooperative Distributed Information Retrieval

• Published in: Frontiers of WWW Research and Development - APWeb 2006 pp. 63 - 75
• Main Authors: Shokouhi, Milad, Scholer, Falk, Zobel, Justin
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Central Index; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Information systems. Data bases; Language Model; Mean Average Precision; Memory organisation. Data processing; Server Selection; Software; Unique Term; Statistical analysis; Probabilistic approach; Database query; Sample size; Distributed database; Lexicon; Information retrieval; Sampling
• ISBN: 3540311424; 9783540311423
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11610113_7

The goal of distributed information retrieval is to support effective searching over multiple document collections. For efficiency, queries should be routed to only those collections that are likely to contain relevant documents, so it is necessary to first obtain information about the content of the target collections. In an uncooperative environment, query probing — where randomly-chosen queries are used to retrieve a sample of the documents and thus of the lexicon — has been proposed as a technique for estimating statistical term distributions. In this paper we rebut the claim that a sample of 300 documents is sufficient to provide good coverage of collection terms. We propose a novel sampling strategy and experimentally demonstrate that sample size needs to vary from collection to collection, that our methods achieve good coverage based on variable-sized samples, and that we can use the results of a probe to determine when to stop sampling.