Decision Support Framework for Big Data Analytics

被引：2

作者：

Agarwal, Sakshi ^{[1
]}

Narayanan, Krishnaprasad ^{[1
]}

Sinha, Manjira ^{[2
]}

Gupta, Rohit ^{[1
]}

Eswaran, Sharanya ^{[3
]}

Mukherjee, Tridib ^{[3
]}

机构：

[1] Conduent Labs India, Bengaluru, Karnataka, India

[2] Accenture, Bangalore, Karnataka, India

[3] Play Games24x7, Data Sci Team, Bangalore, Karnataka, India

来源：

2018 IEEE WORLD CONGRESS ON SERVICES (IEEE SERVICES 2018) | 2018年

关键词：

Decision Support Framework; Big Data; Urban Informatics; Analytic Hierarchy Process (AHP); Decision Tree Modeling; Multi-modal Data Analytics; Crime;

D O I：

10.1109/SERVICES.2018.00040

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Making design choices for big data systems is not trivial. If not planned out efficiently, keeping in mind the practical requirements, there's a possibility that the deployed system can lack important features to match up the application or it may contain over-sophisticated methods that incurs a large cost, but little increase in the efficiency, output. To equip the end user towards wise design choices, we have proposed a decision support framework for big data systems that can evaluate the suitability over numerous design combinations and outputs the one most efficient for the end-user requirement.

引用

页码：53 / 54

页数：2

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