Saikat Mukherjee
- Information Systems top 5%
- Artificial Intelligence top 10%
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Materials Chemistry
- Co-authors
- I. V. RamakrishnanAmresh KumarGuizhen YangAditya Abha SinghDorin ComaniciuSascha SeifertAlexander CavallaroMartin Huber
- Topics
- Web Data Mining and Analysis (10 papers)Algorithms and Data Compression (6 papers)Cloud Computing and Resource Management (4 papers)
- Partner nations
- United StatesIndiaGermany
In The Last Decade
Saikat Mukherjee
22 papers receiving 215 citations
Peers
Comparison fields: 5 of 49
- Information Systems 122
- Artificial Intelligence 115
- Computer Networks and Communications 47
- Computer Vision and Pattern Recognition 40
- Materials Chemistry 28
Countries citing papers authored by Saikat Mukherjee
This map shows the geographic impact of Saikat Mukherjee'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 Saikat Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saikat Mukherjee more than expected).
Fields of papers citing papers by Saikat Mukherjee
This network shows the impact of papers produced by Saikat Mukherjee. 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 Saikat Mukherjee. The network helps show where Saikat Mukherjee may publish in the future.
Co-authorship network of co-authors of Saikat Mukherjee
This figure shows the co-authorship network connecting the top 25 collaborators of Saikat Mukherjee. A scholar is included among the top collaborators of Saikat Mukherjee 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 Saikat Mukherjee. Saikat Mukherjee 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 | 4 | |
| 3 | 7 | |
| 4 | 14 | |
| 5 | 16 | |
| 6 | Verification and Validation of MapReduce Progra m Model for Parallel Support Vector Machine Alg orithm on Hadoop Cluster | 24 |
| 7 | 32 | |
| 8 | On the Complexity of Multi-Query Optimization in Stream Grids | 0 |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 1 | |
| 13 | 5 | |
| 14 | 9 | |
| 15 | 0 | |
| 16 | 18 | |
| 17 | 27 | |
| 18 | 5 | |
| 19 | 10 | |
| 20 | 1 |
About Saikat Mukherjee
Saikat Mukherjee is a scholar working on Information Systems, Human Factors and Ergonomics and Computer Networks and Communications, having authored 26 papers that have together received 237 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (10 papers), Algorithms and Data Compression (6 papers) and Cloud Computing and Resource Management (4 papers). The work is most often cited by research in Human Factors and Ergonomics (24 citations), Information Systems (122 citations) and Artificial Intelligence (115 citations). Saikat Mukherjee has collaborated with scholars based in United States, India and Germany. Frequent co-authors include I. V. Ramakrishnan, Amresh Kumar, Guizhen Yang, Aditya Abha Singh, Dorin Comaniciu, Sascha Seifert, Alexander Cavallaro, Martin Huber, Hasan Davulcu and Jalal Mahmud. Their work appears in journals such as IEEE Transactions on Cloud Computing, World Wide Web and ACM Transactions on the Web.
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.