M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
Impact in
Classified as
- Journal
- Very Large Data Bases
In The Last Decade
doi.org/w8798651 →Countries where authors are citing M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
This map shows the geographic impact of M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. 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 M-tree: An Efficient Access Method for Similarity Search in Metric Spaces with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M-tree: An Efficient Access Method for Similarity Search in Metric Spaces more than expected).
Fields of papers citing M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
This network shows the impact of M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the M-tree: An Efficient Access Method for Similarity Search in Metric Spaces.
About M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
This paper, published in 1997, received 965 indexed citations . Written by Paolo Ciaccia, Marco Patella and Pavel Zezula covering the research area of Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing. It is primarily cited by scholars working on Signal Processing (689 citations), Computer Vision and Pattern Recognition (549 citations), Artificial Intelligence (319 citations), Computer Networks and Communications (238 citations) and Information Systems (178 citations). Published in Very Large Data Bases.
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/w8798651.