Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Distributed streams algorithms for sliding windows
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Exploiting k-constraints to reduce memory overhead in continuous queries over data streams
ACM Transactions on Database Systems (TODS)
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
On joining and caching stochastic streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Characterizing and Exploiting Reference Locality in Data Stream Applications
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Sketching asynchronous streams over a sliding window
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Continuous Nearest Neighbor Queries over Sliding Windows
IEEE Transactions on Knowledge and Data Engineering
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximate NN queries on streams with guaranteed error/performance bounds
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ad-hoc top-k query answering for data streams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Time-decaying aggregates in out-of-order streams
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
The gist of everything new: personalized top-k processing over web 2.0 streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
SKIF: a data imputation framework for concept drifting data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An optimal strategy for monitoring top-k queries in streaming windows
Proceedings of the 14th International Conference on Extending Database Technology
Complex pattern ranking (CPR): evaluating top-k pattern queries over event streams
Proceedings of the 5th ACM international conference on Distributed event-based system
MTopS: scalable processing of continuous top-k multi-query workloads
Proceedings of the 20th ACM international conference on Information and knowledge management
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Top-k publish-subscribe for social annotation of news
Proceedings of the VLDB Endowment
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We study the problem of continuous monitoring of top-k queries over multiple non-synchronized streams. Assuming a sliding window model, this general problem has been a well addressed research topic in recent years. Most approaches, however, assume synchronized streams where all attributes of an object are known simultaneously to the query processing engine. In many streaming scenarios though, different attributes of an item are reported in separate non-synchronized streams which do not allow for exact score calculations. We present how the traditional notion of object dominance changes in this case such that the k dominance set still includes all and only those objects which have a chance of being among the top-k results in their life time. Based on this, we propose an exact algorithm which builds on generating multiple instances of the same object in a way that enables efficient object pruning. We show that even with object pruning the necessary storage for exact evaluation of top-k queries is linear in the size of the sliding window. As data should reside in main memory to provide fast answers in an online fashion and cope with high stream rates, storing all this data may not be possible with limited resources. We present an approximate algorithm which leverages correlation statistics of pairs of streams to evict more objects while maintaining accuracy. We evaluate the efficiency of our proposed algorithms with extensive experiments.