title: Computing Iceberg Queries Efficiently.
creator: Fang, Min
creator: Shivakumar, Narayanan
creator: Garcia-Molina, Hector
creator: Motwani, Rajeev
creator: Ullman, Jeffrey D.
subject: Digital Libraries
description: Many applications compute aggregate functions (such as COUNT, SUM) over an attribute (or set of attributes) to find aggregate values above some specified threshold. We call such queries iceberg queries because the number of above-threshold results is often very small (the tip of an iceberg), relative to the large amount of input data (the iceberg). Such iceberg queries are common in many applications, including data warehousing, information-retrieval, market basket analysis in data mining, clustering and copy detection. We propose efficient algorithms to evaluate iceberg queries using very little memory and significantly fewer passes over data, as compared to current techniques that use sorting or hashing. We present an experimental case study using over three gigabytes of Web data to illustrate the savings obtained by our algorithms. 
publisher: Stanford InfoLab
date: 1999-11-11
type: Techreport
type: NonPeerReviewed
format: application/pdf
identifier: http://ilpubs.stanford.edu:8090/423/1/1999-67.pdf
identifier: Fang, Min and Shivakumar, Narayanan and Garcia-Molina, Hector and Motwani, Rajeev and Ullman, Jeffrey D. (1999) Computing Iceberg Queries Efficiently. Technical Report. Stanford InfoLab. (Publication Note: International Conference on Very Large Databases (VLDB'98), New York, August 1998)
relation: http://ilpubs.stanford.edu:8090/423/