Scientific Footprints in Digital Libraries

  • Claudia Ifrim
  • Xenia Koulouri
  • Manolis Wallace
  • Florin PopEmail author
  • Mariana Mocanu
  • Valentin Cristea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10190)


In recent years, members of the academic community have increasingly turned to digital libraries to follow the latest work within their own field and to estimate papers’, journals’ and researchers’ impact. Yet, despite the powerful indexing and searching tools available, identifying the most important works and authors in a field remains a challenging task, for which a wealth of prior information is needed; existing systems fail to identify and incorporate in their results information regarding connections between publications of different disciplines. In this paper we analyze citation lists in order to not only quantify but also understand impact, by tracing the “footprints” that authors have left, i.e. the specific areas in which they have made an impact. We use the publication medium (specific journal or conference) to identify the thematic scope of each paper and feed from existing digital libraries that index scientific activity, namely Google Scholar and DBLP. This allows us to design and develop a system, the Footprint Analyzer, that can be used to successfully identify the most prominent works and authors for each scientific field, regardless of whether their own research is limited to or even focused on the specific field. Various real life examples demonstrate the proposed concepts and actual results from the developed system’s operation prove the applicability and validity.


Research impact Citations Publication medium Digital library Google Scholar DBLP 



This work has been supported by COST Action IC1302: Semantic keyword-based search on structured data sources (KEYSTONE).

This work has been partially funded by the Sectoral Operational Programme Human Resources Development 2007–2013 of the Ministry of European Funds through the Financial Agreement POSDRU/187/1.5/S/155536 and partially supported by “DataWay - Real-time Data Processing Platform for Smart Cities: Making sense of Big Data” grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-II-RU-TE-2014-4-2731.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Claudia Ifrim
    • 1
  • Xenia Koulouri
    • 2
  • Manolis Wallace
    • 2
  • Florin Pop
    • 1
    Email author
  • Mariana Mocanu
    • 1
  • Valentin Cristea
    • 1
  1. 1.Faculty of Automatic Control and Computers, Computer Science DepartmentUniversity Politehnica of BucharestBucharestRomania
  2. 2.Knowledge and Uncertainty Research Laboratory, Department of Informatics and TelecommunicationsUniversity of the PeloponneseTripolisGreece

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