QFL for the Web Data Extraction from Multiple Data Sources

Author(s):  
Shivani W. Borle ◽  
Amol D. Potgantwar
2013 ◽  
Vol 756-759 ◽  
pp. 1585-1589 ◽  
Author(s):  
Gui Li ◽  
Zi Yang Han ◽  
Zhao Xin Chen ◽  
Zheng Yu Li ◽  
Ping Sun

The purpose of WEB data extraction and integration is to provide the domain oriented value-added services. Based on the requirements of domain, and the features of web pages data. this paper proposes a WEB data schema and a domain data model. It also puts forward the web table positioning and web table records extracting based on WEB data schema and an integration algorithm based on the main data model. The experiment results are given to show effectiveness of the proposed algorithm and model.


Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma

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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