Apache flink : Stream and batch processing in a single engine
- Journal
- KTH Publication Database DiVA (KTH Royal Institute of Technology)
In The Last Decade
doi.org/w22517697 →Countries where authors are citing Apache flink : Stream and batch processing in a single engine
This map shows the geographic impact of Apache flink : Stream and batch processing in a single engine. 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 Apache flink : Stream and batch processing in a single engine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Apache flink : Stream and batch processing in a single engine more than expected).
Fields of papers citing Apache flink : Stream and batch processing in a single engine
This network shows the impact of Apache flink : Stream and batch processing in a single engine. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Apache flink : Stream and batch processing in a single engine.
About Apache flink : Stream and batch processing in a single engine
This paper, published in 2015, received 750 indexed citations . Written by Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi and Kostas Tzoumas covering the research area of Information Systems and Computer Networks and Communications. It is primarily cited by scholars working on Computer Networks and Communications (572 citations), Information Systems (414 citations) and Artificial Intelligence (242 citations). Published in KTH Publication Database DiVA (KTH Royal Institute of Technology).
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.
This paper is also available at doi.org/w22517697.