Big Data and Cognitive Computing

770 papers and 6.9k indexed citations i.

About

The 770 papers published in Big Data and Cognitive Computing in the last decades have received a total of 6.9k indexed citations. Papers published in Big Data and Cognitive Computing usually cover Artificial Intelligence (325 papers), Information Systems (149 papers) and Computer Vision and Pattern Recognition (105 papers) specifically the topics of Topic Modeling (57 papers), Sentiment Analysis and Opinion Mining (56 papers) and Blockchain Technology Applications and Security (44 papers). The most active scholars publishing in Big Data and Cognitive Computing are Viriya Taecharungroj, Hossein Hassani, Emmanuel Sirimal Silva, Gary Wills, Hany F. Atlam, Xu Huang, Robert John Walters, Angkoon Phinyomark, Erik Scheme and Massimo Stella.

In The Last Decade

Fields of papers published in Big Data and Cognitive Computing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

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).

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.

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2025