Ignacio Cano
Impact in
-
- IoT and Edge/Fog Computing
- Distributed and Parallel Computing Systems
- Advanced Data Storage Technologies
- Caching and Content Delivery
- Software-Defined Networks and 5G
- Software System Performance and Reliability
-
- Cloud Computing and Resource Management
Papers in
-
- Cloud Computing and Resource Management 3
-
- Distributed systems and fault tolerance 2
- Advanced Data Storage Technologies 2
- Co-authors
- Arvind Krishnamurthy (2 shared papers)Markus Weimer (1 shared paper)Dhruv Mahajan (1 shared paper)Carlo Curino (1 shared paper)Sameer Singh (1 shared paper)Carlos Guestrin (1 shared paper)Brent Chun (1 shared paper)Karan Gupta (1 shared paper)
- Journals
- Text REtrieval Conference (1 paper)IEEE Data(base) Engineering Bulletin (1 paper)Networked Systems Design and Implementation (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Ignacio Cano
5 papers receiving 56 citations
Peers
Comparison fields: 5 of 25
- Computer Networks and Communications 43
- Information Systems 34
- Artificial Intelligence 14
- Computer Science Applications 2
- Hardware and Architecture 2
Countries citing papers authored by Ignacio Cano
This map shows the geographic impact of Ignacio Cano'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 Ignacio Cano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ignacio Cano more than expected).
Fields of papers citing papers by Ignacio Cano
This network shows the impact of papers produced by Ignacio Cano. 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 Ignacio Cano. The network helps show where Ignacio Cano may publish in the future.
Co-authors
The 17 scholars most cited alongside Ignacio Cano, 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 | Towards Geo-Distributed Machine Learning. | 2016 | 28 |
| 2 | 2016 | 18 | |
| 3 | Curator: self-managing storage for enterprise clusters | 2017 | 5 |
| 4 | Extreme Gradient Boosting [R package xgboost version 1.3.2.1] | 2021 | 5 |
| 5 | Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014 | 2014 | 2 |
About Ignacio Cano
Ignacio Cano is a scholar working on Information Systems, Computer Networks and Communications, Artificial Intelligence, General Health Professions and Philosophy, having authored 5 papers that have together received 58 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (3 papers), Distributed systems and fault tolerance (2 papers), Advanced Data Storage Technologies (2 papers), Privacy-Preserving Technologies in Data (1 paper), Hermeneutics and Narrative Identity (1 paper), Natural Language Processing Techniques (1 paper), Aging, Elder Care, and Social Issues (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Computer Networks and Communications (43 citations), Information Systems (34 citations), Artificial Intelligence (14 citations), Computer Science Applications (2 citations) and Hardware and Architecture (2 citations). Ignacio Cano has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Arvind Krishnamurthy, Markus Weimer, Dhruv Mahajan, Carlo Curino, Sameer Singh, Carlos Guestrin, Brent Chun, Karan Gupta, Yutian Li and Tong He. Their work appears in journals such as Text REtrieval Conference, IEEE Data(base) Engineering Bulletin and Networked Systems Design and Implementation.
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.