Debapriyo Majumdar
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Holger BastSumit BhatiaPrasenjit MitraMartin TheobaldGerhard WeikumRalf SchenkelIngmar WeberShajith Ikbal
- Topics
- Data Management and Algorithms (8 papers)Advanced Text Analysis Techniques (4 papers)Topic Modeling (3 papers)
- Journals
- Lecture notes in computer scienceThe VLDB JournalVery Large Data Bases
- Partner nations
- GermanyIndiaUnited States
In The Last Decade
Debapriyo Majumdar
16 papers receiving 294 citations
Peers
Comparison fields: 5 of 35
- Signal Processing 160
- Artificial Intelligence 156
- Information Systems 155
- Computer Networks and Communications 132
- Computer Vision and Pattern Recognition 60
Countries citing papers authored by Debapriyo Majumdar
This map shows the geographic impact of Debapriyo Majumdar'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 Debapriyo Majumdar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debapriyo Majumdar more than expected).
Fields of papers citing papers by Debapriyo Majumdar
This network shows the impact of papers produced by Debapriyo Majumdar. 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 Debapriyo Majumdar. The network helps show where Debapriyo Majumdar may publish in the future.
Co-authorship network of co-authors of Debapriyo Majumdar
This figure shows the co-authorship network connecting the top 25 collaborators of Debapriyo Majumdar. A scholar is included among the top collaborators of Debapriyo Majumdar 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 Debapriyo Majumdar. Debapriyo Majumdar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 100 | |
| 6 | 4 | |
| 7 | Extracting Problem and Resolution Information from Online Discussion Forums | 14 |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 22 | |
| 11 | 52 | |
| 12 | 80 | |
| 13 | Sequences Characterizing k-Trees | 4 |
| 14 | Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis | 2 |
| 15 | IO-Top-k at TREC 2006: Terabyte Track | 2 |
| 16 | IO-Top-k: Index-Access Optimized Top-k Query Processing | 18 |
| 17 | 1 |
About Debapriyo Majumdar
Debapriyo Majumdar is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 315 indexed citations. Recurring topics across this work include Data Management and Algorithms (8 papers), Advanced Text Analysis Techniques (4 papers) and Topic Modeling (3 papers). The work is most often cited by research in Signal Processing (160 citations), Information Systems (155 citations) and Computer Networks and Communications (132 citations). Debapriyo Majumdar has collaborated with scholars based in Germany, India and United States. Frequent co-authors include Holger Bast, Sumit Bhatia, Prasenjit Mitra, Martin Theobald, Gerhard Weikum, Ralf Schenkel, Ingmar Weber, Shajith Ikbal, P Deepak and Preethi Raghavan. Their work appears in journals such as Lecture notes in computer science, The VLDB Journal and Very Large Data Bases.
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.