Scalable real-time classification of data streams with concept drift

Tennant, Mark; Stahl, Frederic; Rana, Omer; Gomes, João Bártolo

doi:10.1016/j.future.2017.03.026

Cited by 77 publications

(50 citation statements)

References 16 publications

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“…Regarding the SL topic, many researches have focused on it due to its mentioned relevance, such as [15][16][17][18][19], and more recently in [20][21][22][23]. The application of regression techniques to SL has been recently addressed in [24], where the authors cover the most important online regression methods.…”

Section: Related Workmentioning

confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

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Section: Related Workmentioning

confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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“…Recent work focused on combining method with a grouping attribute of constraint and penalty regression using concept drift [Wang, Park, Yeon et al (2017)]. Following methods could be used in evolution stages partition [Tennant, Stahl, Rana et al (2017); Sidhu and Bhatia (2017); Sethi and Kantardzic (2018); Duda, Jaworski and Rutkowski (2017); Sethi and Kantardzic (2018)].…”

Section: Concept Drift Of Process Objectmentioning

confidence: 99%

Stream-Based Data Sampling Mechanism for Process Object

Lin¹,

Liu²,

Chen³

et al. 2019

Computers, Materials &Amp; Continua

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Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. R ecently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. T here i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.

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“…A new algorithm for building decision trees is presented in SPDT [19] based on Parallel binning instead of the Hoeffding Bound used by HTs. [20] propose MC-NN, based on the combination of Micro Clusters (MC) and nearest neighbour (NN), with less scalability than the design proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

Low-latency multi-threaded ensemble learning for dynamic big data streams

Marrón

Ayguadé

Herrero

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

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Abstract-Real-time mining of evolving data streams involves new challenges when targeting today's application domains such as the Internet of the Things: increasing volume, velocity and volatility requires data to be processed on-thefly with fast reaction and adaptation to changes. This paper presents a high performance scalable design for decision trees and ensemble combinations that makes use of the vector SIMD and multicore capabilities available in modern processors to provide the required throughput and accuracy. The proposed design offers very low latency and good scalability with the number of cores on commodity hardware when compared to other state-of-the art implementations. On an Intel i7-based system, processing a single decision tree is 6x faster than MOA (Java), and 7x faster than StreamDM (C++), two wellknown reference implementations. On the same system, the use of the 6 cores (and 12 hardware threads) available allow to process an ensemble of 100 learners 85x faster that MOA while providing the same accuracy. Furthermore, our solution is highly scalable: on an Intel Xeon socket with large core counts, the proposed ensemble design achieves up to 16x speedup when employing 24 cores with respect to a single threaded execution.

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Scalable real-time classification of data streams with concept drift

Cited by 77 publications

References 16 publications

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Stream-Based Data Sampling Mechanism for Process Object

Low-latency multi-threaded ensemble learning for dynamic big data streams

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