Bing Tian Dai
- Artificial Intelligence top 2%
- Molecular Biology
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Patricia L. WhetzelMichael C. DorfMark A. MusenMargaret‐Anne StoreyNigam H. ShahClément JonquetNiall GriffithChristopher G. Chute
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (6 papers)Advanced Database Systems and Queries (6 papers)
- Journals
- Nucleic Acids ResearchSHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- SingaporeChinaUnited States
In The Last Decade
Bing Tian Dai
38 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 726
- Molecular Biology 532
- Information Systems 165
- Computer Networks and Communications 108
- Computer Vision and Pattern Recognition 100
Countries citing papers authored by Bing Tian Dai
This map shows the geographic impact of Bing Tian Dai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bing Tian Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bing Tian Dai more than expected).
Fields of papers citing papers by Bing Tian Dai
This network shows the impact of papers produced by Bing Tian Dai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bing Tian Dai. The network helps show where Bing Tian Dai may publish in the future.
Co-authorship network of co-authors of Bing Tian Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Bing Tian Dai. A scholar is included among the top collaborators of Bing Tian Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bing Tian Dai. Bing Tian Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 0 | |
| 11 | 54 | |
| 12 | How Can Consumer Preferences Be Leveraged for Targeted Upselling in Cable TV Services | 3 |
| 13 | 8 | |
| 14 | BioPortal: ontologies and integrated data resources at the click of a mousebreakdown → | 556 |
| 15 | 26 | |
| 16 | Column heterogeneity as a measure of data quality | 8 |
| 17 | 2 | |
| 18 | 65 | |
| 19 | 18 | |
| 20 | 19 |
About Bing Tian Dai
Bing Tian Dai is a scholar working on Artificial Intelligence, Management Science and Operations Research and Signal Processing, having authored 44 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Advanced Database Systems and Queries (6 papers). The work is most often cited by research in Artificial Intelligence (726 citations), Health Information Management (43 citations) and Information Systems and Management (62 citations). Bing Tian Dai has collaborated with scholars based in Singapore, China and United States. Frequent co-authors include Patricia L. Whetzel, Michael C. Dorf, Mark A. Musen, Margaret‐Anne Storey, Nigam H. Shah, Clément Jonquet, Niall Griffith, Christopher G. Chute, Natasha Noy and Daniel L. Rubin. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.