MOA-TweetReader: real-time analysis in Twitter streaming data
DS'11 Proceedings of the 14th international conference on Discovery science
A randomized algorithm for finding frequent elements in streams using o(loglogn) space
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Information Sciences: an International Journal
Finding patterns in large star schemas at the right aggregation level
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Pushing constraints into data streams
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Mining stable patterns in multiple correlated databases
Decision Support Systems
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In many real-world applications, information such as web click data, stock ticker data, sensor network data, phone call records, and traffic monitoring data appear in the form of data streams. Online monitoring of data streams has emerged as an important research undertaking. Estimating the frequency of the items on these streams is an important aggregation and summary technique for both stream mining and data management systems with a broad range of applications. This paper reviews the state-of-the-art progress on methods of identifying frequent items from data streams. It describes different kinds of models for frequent items mining task. For general models such as cash register and Turnstile, we classify existing algorithms into sampling-based, counting-based, and hashing-based categories. The processing techniques and data synopsis structure of each algorithm are described and compared by evaluation measures. Accordingly, as an extension of the general data stream model, four more specific models including time-sensitive model, distributed model, hierarchical and multi-dimensional model, and skewed data model are introduced. The characteristics and limitations of the algorithms of each model are presented, and open issues waiting for study and improvement are discussed.