Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Bigtable
20082.1k citationsFay W. Chang, Jay B. Dean et al.ACM Transactions on Computer Systemsprofile →
Bigtable: a distributed storage system for structured data
20061.2k citationsFay W. Chang, Jay B. Dean et al.Operating Systems Design and Implementationprofile →
The Chubby lock service for loosely-coupled distributed systems
2006527 citationsMike BurrowsOperating Systems Design and Implementationprofile →
This map shows the geographic impact of Mike Burrows'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 Mike Burrows with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mike Burrows more than expected).
This network shows the impact of papers produced by Mike Burrows. 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 Mike Burrows. The network helps show where Mike Burrows may publish in the future.
Co-authorship network of co-authors of Mike Burrows
This figure shows the co-authorship network connecting the top 25 collaborators of Mike Burrows.
A scholar is included among the top collaborators of Mike Burrows 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 Mike Burrows. Mike Burrows is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
16 of 16 papers shown
1.
Phothilimthana, Phitchaya Mangpo, Yanqi Zhou, Charith Mendis, et al.. (2021). A Learned Performance Model for Tensor Processing Units. arXiv (Cornell University). 3. 387–400.3 indexed citations
Medina-Oliva, Gabriela, et al.. (2014). Health and Performances Machine Tool Monitoring Architecture. KTH Publication Database DiVA (KTH Royal Institute of Technology). 139–144.2 indexed citations
Chang, Fay W., Jay B. Dean, Sanjay Ghemawat, et al.. (2008). Bigtable. ACM Transactions on Computer Systems. 26(2). 1–26.2111 indexed citations breakdown →
10.
Chang, Fay W., Jay B. Dean, Sanjay Ghemawat, et al.. (2006). Bigtable: a distributed storage system for structured data. Operating Systems Design and Implementation. 205–218.1182 indexed citations breakdown →
11.
Burrows, Mike. (2006). The Chubby lock service for loosely-coupled distributed systems. Operating Systems Design and Implementation. 335–350.527 indexed citations breakdown →
12.
Abadi, Martı́n, Mike Burrows, Mark S. Manasse, & Ted Wobber. (2005). Moderately hard, memory-bound functions. ACM Transactions on Internet Technology. 5(2). 299–327.101 indexed citations
13.
Burrows, Mike, et al.. (2004). Bicycle Design: The Search for the Perfect Machine.2 indexed citations
14.
Lillibridge, Mark, Sameh Elnikety, Andrew Birrell, Mike Burrows, & Michael Isard. (2003). A Cooperative Backup System. Infoscience (Ecole Polytechnique Fédérale de Lausanne).6 indexed citations
15.
Lillibridge, Mark, Sameh Elnikety, Andrew Birrell, Mike Burrows, & Michael Isard. (2003). A cooperative internet backup scheme. USENIX Annual Technical Conference. 3–3.111 indexed citations
16.
Doorn, Leendert van, Martı́n Abadi, Mike Burrows, & Edward Wobber. (1999). Secure Network Objects. Lecture notes in computer science. 211–221.1 indexed citations
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.