A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

2019 ◽  
Vol 253 ◽  
pp. 403-411 ◽  
Author(s):  
YuJie Ben ◽  
FuJun Ma ◽  
Hao Wang ◽  
Muhammad Azher Hassan ◽  
Romanenko Yevheniia ◽  
...  
Keyword(s):  
Big Data ◽  
Hybrid Model ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Pm2.5 Exposure

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

scholarly journals Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet of Things

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 31269-31280 ◽  
Author(s):  
Busik Jang ◽  
Sangdon Park ◽  
Joohyung Lee ◽  
Sang-Geun Hahn
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Sources ◽  
Market Model ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Hierarchical Levels ◽  
Data Market

Big data solar power forecasting based on deep learning and multiple data sources

2019 ◽  
Vol 36 (4) ◽  
Author(s):  
José F. Torres ◽  
Alicia Troncoso ◽  
Irena Koprinska ◽  
Zheng Wang ◽  
Francisco Martínez‐Álvarez
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Solar Power ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Solar Power Forecasting ◽  
Power Forecasting

Particular matter prediction using synergy of multiple source urban big data in smart cities

2021 ◽  
pp. 1-15
Author(s):  
Ali Reza Honarvar ◽  
Ashkan Sami
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Smart Cities ◽  
Data Sources ◽  
Quality Data ◽  
Data Set ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Air Quality Data

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

scholarly journals Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020 ◽  
Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Sources ◽  
Learning Models ◽  
Multiple Data Sources ◽  
Multiple Data

scholarly journals A dataset on affiliation of venture capitalists in China between 2000 and 2016

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma
Keyword(s):  
Data Sources ◽  
Venture Capitalists ◽  
High Quality ◽  
Public Agency ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Multi Stage ◽  
The World ◽  
Prior Literature ◽  
Innovation And Entrepreneurship

AbstractPrior literature emphasizes the distinct roles of differently affiliated venture capitalists (VCs) in nurturing innovation and entrepreneurship. Although China has become the second largest VC market in the world, the unavailability of high-quality datasets on VC affiliation in China’s market hinders such research efforts. To fill up this important gap, we compiled a new panel dataset of VC affiliation in China’s market from multiple data sources. Specifically, we drew on a list of 6,553 VCs that have invested in China between 2000 and 2016 from CVSource database, collected VC’s shareholder information from public sources, and developed a multi-stage procedure to label each VC as the following types: GVC (public agency-affiliated, state-owned enterprise-affiliated), CVC (corporate VC), IVC (independent VC), BVC (bank-affiliated VC), FVC (financial/non-bank-affiliated VC), UVC (university endowment/spin-out unit), and PenVC (pension-affiliated VC). We also denoted whether a VC has foreign background. This dataset helps researchers conduct more nuanced investigations into the investment behaviors of different VCs and their distinct impacts on innovation and entrepreneurship in China’s context.

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