Tracking join and self-join sizes in limited storage
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Join-distinct aggregate estimation over update streams
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
ACM SIGMOD Record
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Fast approximate wavelet tracking on streams
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Counting distinct items over update streams
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
CR-PRECIS: a deterministic summary structure for update data streams
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
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Because of important applications such as denial-of-service attack detection, finding frequent items in data streams under different models has been studied extensively. Finding frequent items in a turnstile data stream is the most challenging because both insertions and deletions of items are allowed in the stream. In this paper, we propose a deterministic algorithm that solves the problem. Furthermore, we propose a randomized algorithm for the problem. Empirical results show that our randomized algorithm provides better results than existing randomized algorithms for the problem and our algorithm uses much smaller space, and supports faster query time and similar update time.