RoadRunner: Towards Automatic Data Extraction from Large Web Sites

547 indexed citations
published 2001
Journal
Iris (Roma Tre University)

In The Last Decade

doi.org/w4438119 →

Countries where authors are citing RoadRunner: Towards Automatic Data Extraction from Large Web Sites

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Citations

This map shows the geographic impact of RoadRunner: Towards Automatic Data Extraction from Large Web Sites. 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 RoadRunner: Towards Automatic Data Extraction from Large Web Sites with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites RoadRunner: Towards Automatic Data Extraction from Large Web Sites more than expected).

Fields of papers citing RoadRunner: Towards Automatic Data Extraction from Large Web Sites

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of RoadRunner: Towards Automatic Data Extraction from Large Web Sites. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the RoadRunner: Towards Automatic Data Extraction from Large Web Sites.

About RoadRunner: Towards Automatic Data Extraction from Large Web Sites

This paper, published in 2001, received 547 indexed citations . Written by Valter Crescenzi, Giansalvatore Mecca and Paolo Merialdo covering the research area of Signal Processing, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Information Systems (525 citations), Artificial Intelligence (278 citations) and Computer Networks and Communications (262 citations). Published in Iris (Roma Tre University).

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/w4438119.

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