Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
Impact in
Classified as
- Journal
- Very Large Data Bases
In The Last Decade
doi.org/w540132 →Countries where authors are citing Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
This map shows the geographic impact of Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. 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 Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases more than expected).
Fields of papers citing Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
This network shows the impact of Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases.
About Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
This paper, published in 1995, received 522 indexed citations . Written by Rakesh Agrawal, King-Ip Lin, Harpreet Sawhney and Kyuseok Shim covering the research area of Economics and Econometrics and Signal Processing. It is primarily cited by scholars working on Signal Processing (450 citations), Artificial Intelligence (188 citations), Computer Networks and Communications (106 citations), Information Systems (105 citations) and Computer Vision and Pattern Recognition (89 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/w540132.