Matthew Perron
Impact in
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- Advanced Data Storage Technologies
- Advanced Database Systems and Queries
- Distributed systems and fault tolerance
- Caching and Content Delivery
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
Papers in
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- Advanced Data Storage Technologies 3
- Advanced Database Systems and Queries 3
- Distributed systems and fault tolerance 2
- Caching and Content Delivery 1
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- Cloud Computing and Resource Management 3
- Co-authors
- Andrew Pavlo (2 shared papers)Joy Arulraj (2 shared papers)David J. DeWitt (3 shared papers)Raul Castro Fernandez (2 shared papers)Samuel Madden (2 shared papers)Ziqi Wang (1 shared paper)Todd C. Mowry (1 shared paper)Lin Ma (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (2 papers)Proceedings of the ACM on Management of Data (1 paper)Conference on Innovative Data Systems Research (1 paper)
- Partner nations
- United StatesChinaSingapore
In The Last Decade
Matthew Perron
6 papers receiving 281 citations
Peers
Comparison fields: 5 of 18
- Computer Networks and Communications 270
- Hardware and Architecture 71
- Information Systems 163
- Signal Processing 52
- Information Systems and Management 13
Countries citing papers authored by Matthew Perron
This map shows the geographic impact of Matthew Perron'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 Matthew Perron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Perron more than expected).
Fields of papers citing papers by Matthew Perron
This network shows the impact of papers produced by Matthew Perron. 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 Matthew Perron. The network helps show where Matthew Perron may publish in the future.
Co-authors
The 21 scholars most cited alongside Matthew Perron, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Self-Driving Database Management Systems. | 2017 | 130 |
| 2 | 2016 | 79 | |
| 3 | 2020 | 46 | |
| 4 | 2019 | 21 | |
| 5 | 2019 | 16 | |
| 6 | 2023 | 3 |
About Matthew Perron
Matthew Perron is a scholar working on Computer Networks and Communications, Information Systems, Signal Processing, Hardware and Architecture and Management Science and Operations Research, having authored 6 papers that have together received 295 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (3 papers), Cloud Computing and Resource Management (3 papers), Advanced Database Systems and Queries (3 papers), Data Management and Algorithms (2 papers), Distributed systems and fault tolerance (2 papers), Caching and Content Delivery (1 paper), Parallel Computing and Optimization Techniques (1 paper) and Simulation Techniques and Applications (1 paper). The work is most often cited by research in Computer Networks and Communications (270 citations), Hardware and Architecture (71 citations), Information Systems (163 citations), Signal Processing (52 citations) and Information Systems and Management (13 citations). Matthew Perron has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Andrew Pavlo, Joy Arulraj, David J. DeWitt, Raul Castro Fernandez, Samuel Madden, Ziqi Wang, Todd C. Mowry, Lin Ma, Prashanth Menon and Yingjun Wu. Their work appears in journals such as Proceedings of the VLDB Endowment, Proceedings of the ACM on Management of Data 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.