Significance and Challenges of Big Data Research

By Xiaolong Jin, Benjamin Wan-Sang Wah, Xueqi Cheng, Yuanzhuo Wang. 2015

752 citations recorded by the source corpus.

Subfields: Epidemiology, Information Systems and Management, Management Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Gabriela Ochoa
2. Amaryllis Mavragani
3. Konstantinos P. Tsagarakis

In Information Systems and Management

1. M. del Giudice
2. Gabriela Ochoa
3. Gabriele Santoro
4. Stefano Bresciani
5. Amaryllis Mavragani
6. Konstantinos P. Tsagarakis
7. Veronica Scuotto

In Management Information Systems

1. M. del Giudice
2. Xiao-Jun Zeng
3. Sushma Jain
4. Huchang Liao
5. Gabriele Santoro
6. Stefano Bresciani
7. Mandeep Kaur Saggi
8. Veronica Scuotto

Citations in the corpus

• A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics (2018, background): “Data cleaning is an essential portion of data analysis and challenging too [26].”
• Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance (2017, neutral): “[9], [10].”
• A survey towards an integration of big data analytics to big insights for value-creation (2018, background): “Kshetri, (2014) presented a case study of agriculture to get various benefits, opportunities, and threats by implementing BDA and suggested about soil status to farmers, extreme variations in the weather patterns, new ways of planting, topography. It also provides information regarding variable mark”
• A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “Along with the enormous growth of data in this information age, data management issues like inaccessible data, dirty or unclean data, and data privacy have also increased [115].”
• Shifting intra- and inter-organizational innovation processes towards digital business: An empirical analysis of SMEs (2017, background): “In this regard, ICTs allow the acquisition and management of big data dispersed in the external environment (Jin, Wah, Cheng, & Wang, 2015).”
• Big Data sources and methods for social and economic analyses (2017, background): “Jin et al. (2015) highlight its potential contribution to national and industrial development, as it enforces to change and upgrade research methods, promotes and makes it easy to conduct interdisciplinary research, helps to nowcast the present and to forecast the future more precisely.”
• A Bibliometric Analysis and Visualization of Medical Big Data Research (2018, background): “With the rapid development of “Internet plus”, almost all industry and business data shows explosive growth in recent years [1].”
• Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, methodology): “Big data are characterized by the 8 Vs [1]: volume (exponentially increasing volumes) [2], variety (wide range of datasets), velocity (high processing speed) [3], veracity, value [4,5], variability, volatility, and validity [1].”
• Data sovereignty: A review (2021, background): “The data sovereignty of a country in cyberspace will be another great power-game space besides land, sea, air, and outer spaces” (Jin et al., 2015: 60).”