Big Data-driven Algorithmic Decision-Making in Selecting and Managing Employees: Advanced Predictive Analytics, Workforce Metrics, and Digital Innovations for Enhancing Organizational Human Capital Social Sciences, Sociology, Management and complex organi

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

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Big data driven decision-making for batch-based production systems

Procedia CIRP ◽

10.1016/j.procir.2019.05.023 ◽

2019 ◽

Vol 83 ◽

pp. 814-818 ◽

Cited By ~ 2

Author(s):

Yongheng Zhang ◽

Rui Zhang ◽

Yizhong Wang ◽

Hongfei Guo ◽

Ray Y Zhong ◽

...

Keyword(s):

Decision Making ◽

Big Data ◽

Production Systems ◽

Data Driven ◽

Data Driven Decision Making

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Data-driven decision support under concept drift in streamed big data

Complex & Intelligent Systems ◽

10.1007/s40747-019-00124-4 ◽

2019 ◽

Vol 6 (1) ◽

pp. 157-163 ◽

Cited By ~ 2

Author(s):

Jie Lu ◽

Anjin Liu ◽

Yiliao Song ◽

Guangquan Zhang

Keyword(s):

Decision Making ◽

Big Data ◽

Real Time ◽

Concept Drift ◽

High Volume ◽

Streaming Data ◽

Data Driven ◽

Research Directions ◽

Decision Outcomes ◽

Past Data

Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$D3M in streamed big data environments.

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