Mining data streams: a review

By Mohamed Medhat Gaber, Arkady B. Zaslavsky, Shonali Priyadarsini Krishnaswamy. SGMD, 2005

1,065 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7df052d7f402f95751428edf8d5f215f03dcefa0

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Citations in the corpus

Streaming submodular maximization: massive data summarization on the fly (2014, background): “Streaming algorithms with a limited memory available to them (much less than the ground set) and limited processing time per item [12] are practical solutions in such scenarios.”
A Stochastic Quasi-Newton Method for Large-Scale Optimization (2014, neutral): “O C ] 2 7”
Sequential pattern mining -- approaches and algorithms (2013, background): “A review of the .eld of mining data streams can be found elsewhere [Gaber et al. 2005; Aggarwal 2007].”
A survey on data stream clustering and classification (2015, methodology): “[38] introduced a surveywith theoretical foundations and basic algorithms for various tasks of data streammining, including classification, clustering, frequency counting, and time series analysis.”
A survey of multiple classifier systems as hybrid systems (2014, background): “Concept drift occurs frequently in real life [157].”
Learning under Concept Drift: an Overview (2010, background): “It was gradually overtaken by data stream mining , where the data ﬂow is continuous an rapid [53].”
Incremental on-line learning: A review and comparison of state of the art algorithms (2018, background): “Incremental learning is more frequently treated in the setting of streaming scenarios [17, 18], although most of the work particularly targets concept drift [19, 20, 6].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, positive): “Utilizing data characteristics, temporal and spatial mining can extract knowledge structures represented in models and patterns for high-speed data streams and sensor data [197].”
Open challenges for data stream mining research (2014, methodology): “Data stream mining is a research field that studies methods and algorithms for extracting knowledge from volatile streaming data [14; 5; 1].”
On expressiveness and uncertainty awareness in rule-based classification for data streams (2017, neutral): “Furthermore, DSM classiﬁcation techniques need to allow dynamic adaptation to concept drifts as the data streams in [4] .”
Learning With Feature Evolvable Streams (2017, background): “For more details, please refer to [13], [14], [15], [16].”
A Survey of Classification Methods in Data Streams (2007, background): “On the other hand, in task based solutions, some standard algorithmic modification techniques can be used to achieve time and space efficient solutions [13].”
The CART decision tree for mining data streams (2014, neutral): “…i L Þ are given analogously as in ( 8 ) GiniðL q ðA i L ÞÞ ¼ 1 À XK k¼1 ðp kL ; q ðA i L ÞÞ 2 ; ð10Þ GiniðR q ðA i L ÞÞ ¼ 1 À X K k¼1 ðp kR ; q ðA i L ÞÞ 2 : ð11Þ A split measure function in the CART algorithm is defined as a difference between Gini index ( 8 ) and the weighted Gini index ( 9 ) .”
Characterizing concept drift (2015, background): “rom data, for which interested readers are encouraged to read the numerous existing excellent tutorials and surveys [Bifet et al., 2011, Nguyen et al., 2014, Gama and Rodrigues, 2009, Aggarwal, 2009, Gaber et al., 2005, Krempl et al., 2014] for the current state of the art and the open challenges. W”