Neha Narkhede
- Computer Networks and Communications top 10%
- Information Systems top 5%
- Artificial Intelligence
- Signal Processing
- Computer Vision and Pattern Recognition
- Co-authors
- Jun RaoJoel KoshyJay KrepsGuozhang WangSriram SubramanianRichard ParkA. K. JaswalRaul Castro Fernandez
- Topics
- Distributed systems and fault tolerance (2 papers)Advanced Data Storage Technologies (2 papers)Scientific Computing and Data Management (2 papers)
- Journals
- Proceedings of the VLDB EndowmentRevista de Fomento SocialConference on Innovative Data Systems Research
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Neha Narkhede
6 papers receiving 225 citations
Peers
Comparison fields: 5 of 60
- Computer Networks and Communications 135
- Information Systems 113
- Artificial Intelligence 65
- Signal Processing 33
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Neha Narkhede
This map shows the geographic impact of Neha Narkhede'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 Neha Narkhede with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neha Narkhede more than expected).
Fields of papers citing papers by Neha Narkhede
This network shows the impact of papers produced by Neha Narkhede. 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 Neha Narkhede. The network helps show where Neha Narkhede may publish in the future.
Co-authorship network of co-authors of Neha Narkhede
This figure shows the co-authorship network connecting the top 25 collaborators of Neha Narkhede. A scholar is included among the top collaborators of Neha Narkhede 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 Neha Narkhede. Neha Narkhede is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Kafka: The definitive guide | 14 |
| 2 | Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale | 37 |
| 3 | Liquid: Unifying Nearline and Offline Big Data Integration | 28 |
| 4 | 86 | |
| 5 | 20 | |
| 6 | Building LinkedIn's Real-time Activity Data Pipeline. | 61 |
| 7 | 0 |
About Neha Narkhede
Neha Narkhede is a scholar working on Information Systems and Management, Computer Networks and Communications and Literature and Literary Theory, having authored 7 papers that have together received 246 indexed citations. Recurring topics across this work include Distributed systems and fault tolerance (2 papers), Advanced Data Storage Technologies (2 papers) and Scientific Computing and Data Management (2 papers). The work is most often cited by research in Computer Networks and Communications (135 citations), Information Systems (113 citations) and Signal Processing (33 citations). Neha Narkhede has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Jun Rao, Joel Koshy, Jay Kreps, Guozhang Wang, Sriram Subramanian, Richard Park, A. K. Jaswal, Raul Castro Fernandez, Peter Pietzuch and Dong Lin. Their work appears in journals such as Proceedings of the VLDB Endowment, Revista de Fomento Social and Conference on Innovative Data Systems Research.
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.