Susan V. Vrbsky
- Computer Networks and Communications top 2%
- Information Systems top 2%
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Hardware and Architecture top 5%
- Co-authors
- Ming LeiJ.W.S. LiuXiaoyan HongYang XiaoNenad JukićAllen ParrishBrandon DixonMichael Galloway
- Topics
- Distributed systems and fault tolerance (24 papers)Cloud Computing and Resource Management (18 papers)Advanced Database Systems and Queries (17 papers)
- Journals
- Expert Systems with ApplicationsComputerIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- United StatesGermany
In The Last Decade
Susan V. Vrbsky
67 papers receiving 633 citations
Peers
Comparison fields: 5 of 65
- Computer Networks and Communications 525
- Information Systems 276
- Signal Processing 141
- Artificial Intelligence 136
- Hardware and Architecture 128
Countries citing papers authored by Susan V. Vrbsky
This map shows the geographic impact of Susan V. Vrbsky'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 Susan V. Vrbsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Susan V. Vrbsky more than expected).
Fields of papers citing papers by Susan V. Vrbsky
This network shows the impact of papers produced by Susan V. Vrbsky. 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 Susan V. Vrbsky. The network helps show where Susan V. Vrbsky may publish in the future.
Co-authorship network of co-authors of Susan V. Vrbsky
This figure shows the co-authorship network connecting the top 25 collaborators of Susan V. Vrbsky. A scholar is included among the top collaborators of Susan V. Vrbsky 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 Susan V. Vrbsky. Susan V. Vrbsky 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 | 4 | |
| 4 | Data Modeling in the Cloud | 1 |
| 5 | Database Systems: Introduction to Databases and Data Warehouses | 11 |
| 6 | Simplifying the Development and Deployment of MapReduce Algorithms | 1 |
| 7 | 1 | |
| 8 | 17 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 7 | |
| 13 | 12 | |
| 14 | A Data Replication Strategy to Increase Data Availability in Data Grids. | 11 |
| 15 | 2 | |
| 16 | 3 | |
| 17 | Scheduling pull-based broadcast with timing constraints | 1 |
| 18 | 25 | |
| 19 | 2 | |
| 20 | Distributed Query Processing Allowing for Redundant Data. | 1 |
About Susan V. Vrbsky
Susan V. Vrbsky is a scholar working on Computer Networks and Communications, Hardware and Architecture and Signal Processing, having authored 73 papers that have together received 708 indexed citations. Recurring topics across this work include Distributed systems and fault tolerance (24 papers), Cloud Computing and Resource Management (18 papers) and Advanced Database Systems and Queries (17 papers). The work is most often cited by research in Computer Networks and Communications (525 citations), Hardware and Architecture (128 citations) and Signal Processing (141 citations). Susan V. Vrbsky has collaborated with scholars based in United States and Germany. Frequent co-authors include Ming Lei, J.W.S. Liu, Xiaoyan Hong, Yang Xiao, Nenad Jukić, Allen Parrish, Ming Lei, Brandon Dixon, Michael Galloway and Ke Meng. Their work appears in journals such as Expert Systems with Applications, Computer and IEEE Transactions on Knowledge and Data Engineering.
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.