Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
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
Query Processing Issues in Image(Multimedia) Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IO-Top-k: index-access optimized top-k query processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Distributed spatio-temporal similarity search
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Efficient top-k processing in large-scaled distributed environments
Data & Knowledge Engineering
Smooth Interpolating Histograms with Error Guarantees
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Optimizing Distributed Top-k Queries
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Finding the K highest-ranked answers in a distributed network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed top-k aggregation queries at large
Distributed and Parallel Databases
Evaluation of top-k queries in peer-to-peer networks using threshold algorithms
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Top-k vectorial aggregation queries in a distributed environment
Journal of Parallel and Distributed Computing
Distributed threshold querying of general functions by a difference of monotonic representation
Proceedings of the VLDB Endowment
Power efficiency through tuple ranking in wireless sensor network monitoring
Distributed and Parallel Databases
Peer-to-peer web search: euphoria, achievements, disillusionment, and future opportunities
From active data management to event-based systems and more
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Ranking-aware queries, or top-k queries, have received much attention recently in various contexts such as web, multimedia retrieval, relational databases, and distributed systems. Top-k queries play a critical role in many decision-making related activities such as, identifying interesting objects, network monitoring, load balancing, etc. In this paper, we study the ranking aggregation problem in distributed systems. Prior research addressing this problem did not take data distributions into account, simply assuming the uniform data distribution among nodes, which is not realistic for real data sets and is, in general, inefficient. In this paper, we propose three efficient algorithms that consider data distributions in different ways. Our extensive experiments demonstrate the advantages of our approaches in terms of bandwidth consumption.