A Survey on Big Data Analytics Solutions Deployment

被引：4

作者：

Castellanos, Camilo ^{[1
]}

Perez, Boris ^{[1
,2
]}

Varela, Carlos A. ^{[3
]}

Villamil, Maria del Pilar ^{[1
]}

Correal, Dario ^{[1
]}

机构：

[1] Univ los Andes, Syst Engn & Comp Dept, Bogota, Colombia

[2] Univ Francisco Paula Santander, Syst Engn & Comp Dept, Cucuta, Colombia

[3] Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA

来源：

SOFTWARE ARCHITECTURE, ECSA 2019 | 2019年 / 11681卷

关键词：

D O I：

10.1007/978-3-030-29983-5_13

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

There are widespread and increasing interest in big data analytics (BDA) solutions to enable data collection, transformation, and predictive analyses. The development and operation of BDA application involve business innovation, advanced analytics and cutting-edge technologies which add new complexities to the traditional software development. Although there is a growing interest in BDA adoption, successful deployments are still scarce (a.k.a., the "Deployment Gap" phenomenon). This paper reports an empirical study on BDA deployment practices, techniques and tools in the industry from both the software architecture and data science perspectives to understand research challenges that emerge in this context. Our results suggest new research directions to be tackled by the software architecture community. In particular, competing architectural drivers, interoperability, and deployment procedures in the BDA field are still immature or have not been adopted in practice.

引用

页码：195 / 210

页数：16

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