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.
The entity-relationship model—toward a unified view of data
19762.5k citationsPeter Pin-Shan ChenACM Transactions on Database Systemsprofile →
Countries citing papers authored by Peter Pin-Shan Chen
Since Specialization
Citations
This map shows the geographic impact of Peter Pin-Shan Chen'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 Peter Pin-Shan Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Pin-Shan Chen more than expected).
Fields of papers citing papers by Peter Pin-Shan Chen
This network shows the impact of papers produced by Peter Pin-Shan Chen. 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 Peter Pin-Shan Chen. The network helps show where Peter Pin-Shan Chen may publish in the future.
Peter Pin-Shan Chen is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence, having authored 5 papers that have together received 2.5k indexed citations. Recurring topics across this work include Semantic Web and Ontologies (4 papers), Advanced Database Systems and Queries (4 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Signal Processing (873 citations), Computer Networks and Communications (1.7k citations) and Management Information Systems (416 citations). Peter Pin-Shan Chen has collaborated with scholars based in United States. Their work appears in journals such as ACM Transactions on Database Systems, ACM SIGIR Forum and DSpace@MIT (Massachusetts 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.