@inproceedings{bifet_streamdm_2015,
	title = {{StreamDM}: {Advanced} {Data} {Mining} in {Spark} {Streaming}},
	shorttitle = {{StreamDM}},
	doi = {10.1109/ICDMW.2015.140},
	abstract = {Real-time analytics are becoming increasingly important due to the large amount of data that is being created continuously. Drawing from our experiences at Huawei Noah's Ark Lab, we present and demonstrate here StreamDM, a new open source data mining and machine learning library, designed on top of Spark Streaming, an extension of the core Spark API that enables scalable stream processing of data streams. StreamDM is designed to be easily extended and used, either practitioners, developers, or researchers, and is the first library to contain advanced stream mining algorithms for Spark Streaming.},
	booktitle = {2015 {IEEE} {International} {Conference} on {Data} {Mining} {Workshop} ({ICDMW})},
	author = {Bifet, Albert and Maniu, Silviu and Qian, Jianfeng and Tian, Guangjian and He, Cheng and Fan, Wei},
	month = nov,
	year = {2015},
	note = {ISSN: 2375-9259},
	keywords = {Algorithm design and analysis, Data mining, Data structures, Libraries, Machine learning algorithms, Spark Streaming, Sparks, Training, open-source, software, stream mining},
	pages = {1608--1611},
}

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