SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Heavy-tailed probability distributions in the World Wide Web
A practical guide to heavy tails
Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Towards Efficient Multi-Feature Queries in Heterogeneous Environments
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Efficient top-K query calculation in distributed networks
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Progressive Distributed Top-k Retrieval in Peer-to-Peer Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Optimizing Access Cost for Top-k Queries over Web Sources: A Unified Cost-Based Approach
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
KLEE: a framework for distributed top-k query algorithms
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Distributed top-N query processing with possibly uncooperative local systems
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Incremental access can be essential for top-k queries, as users often want to sift through top answers until satisfied. In this paper, we propose the progressive rank (PR, for short) algorithm, a new non-blocking top-k query algorithm that deals with data items from remote sources via unpredictable, slow, or bursty network traffic. By accessing remote sources asynchronously and scheduling background processing reactively, PR hides intermittent delays in data arrival and produces the first few results quickly. Experiments results show that PR is an effective solution for producing fast query responses in the presence of slow and bursty remote sources, and can be scaled well.