scholarly journals The Social Sustainability of Citizen-centered Urban Governance Networks: Sensor-based Big Data Applications and Real-Time Decision-Making

2019 ◽  
Vol 11 (2) ◽  
pp. 27 ◽  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Urban Governance ◽  
Social Sustainability ◽  
Big Data Applications ◽  
The Social ◽  
Governance Networks

scholarly journals BDWatchdog: Real-time monitoring and profiling of Big Data applications and frameworks

2018 ◽  
Vol 87 ◽  
pp. 420-437 ◽  
Author(s):  
Jonatan Enes ◽  
Roberto R. Expósito ◽  
Juan Touriño
Keyword(s):  
Big Data ◽  
Real Time ◽  
Real Time Monitoring ◽  
Big Data Applications

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

2019 ◽  
Vol 6 (1) ◽  
pp. 157-163 ◽  
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.

scholarly journals Bayesian network method for decision-making about the social sustainability of infrastructure projects

2018 ◽  
Vol 176 ◽  
pp. 521-534 ◽  
Author(s):  
Leonardo A. Sierra ◽  
Víctor Yepes ◽  
Tatiana García-Segura ◽  
Eugenio Pellicer
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Social Sustainability ◽  
Infrastructure Projects ◽  
The Social ◽  
Network Method

scholarly journals Symposium review: Real-time continuous decision making using big data on dairy farms

2020 ◽  
Vol 103 (4) ◽  
pp. 3856-3866 ◽  
Author(s):  
Victor E. Cabrera ◽  
Jorge A. Barrientos-Blanco ◽  
Hector Delgado ◽  
Liliana Fadul-Pacheco
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Dairy Farms ◽  
Symposium Review
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