Online computation and competitive analysis
Online computation and competitive analysis
The space complexity of approximating the frequency moments
Journal of Computer and System Sciences
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Developments from a June 1996 seminar on Online algorithms: the state of the art
Developments from a June 1996 seminar on Online algorithms: the state of the art
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Selection and sorting with limited storage
SFCS '78 Proceedings of the 19th Annual Symposium on Foundations of Computer Science
Online auctions and generalized secretary problems
ACM SIGecom Exchanges
Finding frequent items in data streams
Proceedings of the VLDB Endowment
Competitive Analysis of Aggregate Max in Windowed Streaming
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Encyclopedia of Database Systems
Encyclopedia of Database Systems
Multidimensional online tracking
ACM Transactions on Algorithms (TALG)
Survey: Streaming techniques and data aggregation in networks of tiny artefacts
Computer Science Review
The frequent items problem in online streaming under various performance measures
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
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We study the well-known frequent items problem in data streams from a competitive analysis point of view. We consider the standard worst-case input model, as well as a weaker distributional adversarial setting. We are primarily interested in the single-slot memory case and for both models we give (asymptotically) tight bounds of $\varTheta(\sqrt{N})$ and $\varTheta(\sqrt[3]{N})$ respectively, achieved by very simple and natural algorithms, where N is the stream's length. We also provide lower bounds, for both models, in the more general case of arbitrary memory sizes of k≥1.