An open architecture for complex event processing with machine learning

被引：6

作者：

Luong, Nhan Nathan Tri

Milosevic, Zoran ^{[1
,2
]}

Berry, Andrew ^{[1
]}

Rabhi, Fethi ^{[3
]}

机构：

[1] Deontik, Toowong, Qld, Australia

[2] Griffith Univ, Inst Integrated & Intelligent Syst, Nathan, Qld, Australia

[3] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia

来源：

2020 IEEE 24TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE (EDOC 2020) | 2020年

关键词：

streaming; real-time analytics; complex event processing; machine learning; artificial inteligence;

D O I：

10.1109/EDOC49727.2020.00016

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper proposes an advanced, open architecture to augment streaming data platforms with both complex event processing (CEP) and predictive machine learning models. We leverage the power of CEP to preprocess streams using sophisticated event pattern expressions then present these preprocessed streams for downstream training and predictive computations. We demonstrate this approach using specific technology components.

引用

页码：51 / 56

页数：6

共 6 条

[1] Extending choreography with business contract constraints [J].

Berry, A ;

Milosevic, Z .

INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS, 2005, 14 (2-3) :131-179

[2]

Bifet A., 2018, Machine learning for data streams: with practical examples in MOA

[3]

Mattson T, 2013, IEEE HIGH PERF EXTR

[4]

Milosevic Z., INT J DATA SCI ANAL, V2, P13

[5]

Milosevic Z., 2016, REAL TIME ANULYT BIG

[6] Predictive Analytics for Event Stream Processing [J].

Roudjane, Massiva ;

Rebaine, Djamal ;

Khoury, Raphael ;

Halle, Sylvain .

2019 IEEE 23RD INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE (EDOC), 2019, :171-182

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