The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Optimizing Top-k Selection Queries over Multimedia Repositories
IEEE Transactions on Knowledge and Data Engineering
On-the-fly sharing for streamed aggregation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Joining ranked inputs in practice
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Evaluating top-k queries over incomplete data streams
Proceedings of the 18th ACM conference on Information and knowledge management
A shared execution strategy for multiple pattern mining requests over streaming data
Proceedings of the VLDB Endowment
Sliding-window top-k queries on uncertain streams
The VLDB Journal — The International Journal on Very Large Data Bases
Monitoring reverse top-k queries over mobile devices
Proceedings of the 10th ACM International Workshop on Data Engineering for Wireless and Mobile Access
MTopS: scalable processing of continuous top-k multi-query workloads
Proceedings of the 20th ACM international conference on Information and knowledge management
Efficient processing of top-k join queries by attribute domain refinement
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Continuous top-k queries, which report a certain number (k) of top preferred objects from data streams, are important for a broad class of real-time applications, ranging from financial analysis to network traffic monitoring. Existing solutions for tackling this problem aim to reduce the computational costs by incrementally updating the top-k results upon each window slide. However, they all suffer from the performance bottleneck of periodically requiring a complete recomputation of the top-k results from scratch. Such an operation is not only computationally expensive but also causes significant memory consumption, as it requires keeping all objects alive in the query window. To solve this problem, we identify the "Minimal Top-K candidate set" (MTK), namely the subset of stream objects that is both necessary and sufficient for continuous top-k monitoring. Based on this theoretical foundation, we design the MinTopk algorithm that elegantly maintains MTK and thus eliminates the need for recomputation. We prove the optimality of the MinTopk algorithm in both CPU and memory utilization for continuous top-k monitoring. Our experimental study shows that both the efficiency and scalability of our proposed algorithm is clearly superior to the state-of-the-art solutions.