Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A measurement study of Napster and Gnutella as examples of peer-to-peer file sharing systems
ACM SIGCOMM Computer Communication Review
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
PlanetP: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The Piazza peer data management project
ACM SIGMOD Record
Relational data sharing in peer-based data management systems
ACM SIGMOD Record
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Epidemic-Style Proactive Aggregation in Large Overlay Networks
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Optimizing Top-k Selection Queries over Multimedia Repositories
IEEE Transactions on Knowledge and Data Engineering
Efficient top-K query calculation in distributed networks
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
Progressive Distributed Top-k Retrieval in Peer-to-Peer Networks
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
Reducing network traffic in unstructured P2P systems using Top-k queries
Distributed and Parallel Databases
Analysis and comparison of P2P search methods
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Efficient top-k processing in large-scaled distributed environments
Data & Knowledge Engineering
Bubblestorm: resilient, probabilistic, and exhaustive peer-to-peer search
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Best position algorithms for top-k queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Anytime measures for top-k algorithms
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
On efficient top-k query processing in highly distributed environments
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Best-Effort Top-k Query Processing Under Budgetary Constraints
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Structuring unstructured peer-to-peer networks
HiPC'07 Proceedings of the 14th international conference on High performance computing
Distributed top-k query processing by exploiting skyline summaries
Distributed and Parallel Databases
Proceedings of the VLDB Endowment
Distributed Processing of Probabilistic Top-k Queries in Wireless Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
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Top-k query processing techniques provide two main advantages for unstructured peer-to-peer (P2P) systems. First they avoid overwhelming users with too many results. Second they reduce significantly network resources consumption. However, existing approaches suffer from long waiting times. This is because top-k results are returned only when all queried peers have finished processing the query. As a result, query response time is dominated by the slowest queried peer. In this paper, we address this users' waiting time problem. For this, we revisit top-k query processing in P2P systems by introducing two novel notions in addition to response time: the stabilization time and the cumulative quality gap. Using these notions, we formally define the as-soon-as-possible (ASAP) top-k processing problem. Then, we propose a family of algorithms called ASAP to deal with this problem. We validate our solution through implementation and extensive experimentation. The results show that ASAP significantly outperforms baseline algorithms by returning final top-k result to users in much better times.