Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)
Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/4/4/42 |
| _version_ | 1797458661670387712 |
|---|---|
| author | Yili Chen Congdong Li Han Wang |
| author_facet | Yili Chen Congdong Li Han Wang |
| author_sort | Yili Chen |
| collection | DOAJ |
| description | Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application. |
| first_indexed | 2024-03-09T16:40:21Z |
| format | Article |
| id | doaj.art-b387bfb3aabc4f7b80ec25d337805a72 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-09T16:40:21Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-b387bfb3aabc4f7b80ec25d337805a722023-11-24T14:52:49ZengMDPI AGForecasting2571-93942022-09-014476778610.3390/forecast4040042Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)Yili Chen0Congdong Li1Han Wang2School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, ChinaSchool of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, ChinaThe Faculty of Data Science, City University of Macau, Macao 999078, ChinaBig data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.https://www.mdpi.com/2571-9394/4/4/42big datapredictive analyticsbusiness intelligencebibliographic studyCiteSpace |
| spellingShingle | Yili Chen Congdong Li Han Wang Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) Forecasting big data predictive analytics business intelligence bibliographic study CiteSpace |
| title | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) |
| title_full | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) |
| title_fullStr | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) |
| title_full_unstemmed | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) |
| title_short | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) |
| title_sort | big data and predictive analytics for business intelligence a bibliographic study 2000 2021 |
| topic | big data predictive analytics business intelligence bibliographic study CiteSpace |
| url | https://www.mdpi.com/2571-9394/4/4/42 |
| work_keys_str_mv | AT yilichen bigdataandpredictiveanalyticsforbusinessintelligenceabibliographicstudy20002021 AT congdongli bigdataandpredictiveanalyticsforbusinessintelligenceabibliographicstudy20002021 AT hanwang bigdataandpredictiveanalyticsforbusinessintelligenceabibliographicstudy20002021 |