Countries where authors publish in Big Data and Cognitive Computing
Since Specialization
Citations
This map shows the geographic impact of research published in Big Data and Cognitive Computing. 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 papers published in Big Data and Cognitive Computing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Big Data and Cognitive Computing more than expected).
Fields of papers published in Big Data and Cognitive Computing
This network shows the impact of papers published in Big Data and Cognitive Computing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Big Data and Cognitive Computing.
About Big Data and Cognitive Computing
The 907 papers published in Big Data and Cognitive Computing in the last decades have received a total of 10.8k indexed citations . Papers published in Big Data and Cognitive Computing usually cover Health Informatics (25 papers), Artificial Intelligence (388 papers) and Information Systems (172 papers) specifically the topics of Topic Modeling (73 papers), Sentiment Analysis and Opinion Mining (62 papers), Blockchain Technology Applications and Security (49 papers), Advanced Text Analysis Techniques (40 papers), IoT and Edge/Fog Computing (36 papers), Big Data and Business Intelligence (36 papers), Anomaly Detection Techniques and Applications (36 papers) and Natural Language Processing Techniques (33 papers). The most active scholars publishing in Big Data and Cognitive Computing are Viriya Taecharungroj, Hossein Hassani, Emmanuel Sirimal Silva, Hany F. Atlam, Gary Wills, Xu Huang, Robert John Walters, Erik Scheme, Angkoon Phinyomark and Ηλίας Δρίτσας.
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.