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Architecture Enabling Adaptation of Data Integration Processes for a Research Information System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Today, many efforts have been made to implement information systems for supporting research evaluation activities. To produce a good framework for research evaluation, the selection of appropriate measures is important. Quality aspects of the systems’ implementation should also not be overlooked. Incomplete or faulty data should not be used and metric computation formulas should be discussed and valid. Correctly integrated data from different information sources provide a complete picture of the scientific activity of an institution. Knowledge from the data integration field can be adapted in research information management. In this paper, we propose a research information system for bibliometric indicator analysis that is incorporated into the adaptive integration architecture based on ideas from the data warehousing framework for change support. A data model of the integrated dataset is also presented. This paper also provides a change management solution as a part of the data integration framework to keep the data integration process up to date. This framework is applied for the implementation of a publication data integration system for excellence-based research analysis at the University of Latvia.
Rocznik
Strony
129--149
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
  • University of Latvia, Faculty of Computing, Riga, Latvia
autor
  • University of Latvia, Faculty of Computing, Riga, Latvia
Bibliografia
  • [1] Aagaard K., Bloch C., Schneider J.W., Impacts of performance-based research funding systems: the case of the Norwegian Publication Indicator. Research Evaluation 24, 2, 2015, 106-117.
  • [2] Cabinet Regulation No. 1316 Regulations regarding calculation and assignment of grant- based funding for research institutions, https://likumi.lv/doc.php?id=262508
  • [3] Chen Z., Wu D., Lu J., Chen Y., Metadata-based information resource integration for research management, Procedia Computer Science, 17, 2013, 54-61.
  • [4] Di Tria F., Lefons E., Tangorra F., Academic data warehouse design using a hybrid methodology, Computer Science and Information Systems, 12, 1, 2015, 135-160.
  • [5] DSpace-CRIS Home. https://wiki.duraspace.org/display/DSPACECRIS/DSpace-CRIS+Home.
  • [6] Galimberti P., Mornati S., The Italian model of distributed research information management systems: a case study, Procedia Computer Science, 106, 2017, 183-195.
  • [7] Golfarelli M., Lechtenbörger J., Rizzi S., Vossen G., Schema versioning in data warehouses: Enabling cross-version querying via schema augmentation, Data & Knowledge Engineering, 59, 2, 2006, 435-459.
  • [8] Gonzalez-Pereira B., Guerrero-Bote V.P., Moya-Anegon F., A new approach to the metric of journals’ scientific prestige: The SJR indicator, Journal of Informetrics, 4, 3, 2010, 379-391.
  • [9] Hardcastle J., New journal citation metric - Impact per Publication, 2014, http://editorresources.taylorandfrancisgroup.com/new-journal-citation-metric-impact-per-publication/.
  • [10] Hicks D., Wouters P., Waltman L., De Rijcke S., Rafols I., The Leiden Manifesto for research metrics, Nature, 520, 7548, 2015, 429-431.
  • [11] Inmon W.H., Building the Data Warehouse, 3rd edition, Wiley Computer Publishing, 2002.
  • [12] The International Organisation for Research Information, http://eurocris.org/cerif/main-features-cerif.
  • [13] Jörg B., CERIF: The common European research information format model, Data Science Journal, 9, 2010, CRIS24-CRIS31.
  • [14] Kosten J., A classification of the use of research indicators, Scientometrics, 108, 1, 2016, 457-464.
  • [15] Kulczycki E., Korzeń M., Korytkowski P., Toward an excellence-based research funding system: Evidence from Poland, Journal of Informetrics, 11, 1, 2017, 282-298.
  • [16] Moed H.F., Measuring contextual citation impact of scientific journals, Journal of Informetrics, 4, 3, 2010, 265-277.
  • [17] Nadal S., Romero O., Abelló A., Vassiliadis P., Vansummeren S., An integration- oriented ontology to govern evolution in big data ecosystems, in: Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, 2017.
  • [18] Niedrite L., Solodovnikova D., University IS Architecture for the Research Evaluation Support, in: Proceedings of 11 th International Scientific and Practical Conference "Environment. Technology. Resources", Rezekne Academy of Technologies, Rezekne, 2017, 112-117.
  • [19] Niedrite L., Solodovnikova D., Niedritis A., Publication Data Integration as a Tool for Excellence-Based Research Analysis at the University of Latvia, in: M. Kirikova, K. Norvåg, G. Papadopoulos, J. Gamper, R. Wrembel, J. Darmont, S. Rizzi (eds.), New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, 767, Springer, Berlin, 2017, 125-136.
  • [20] Nikolić S., Penca V., Ivanović D., Surla D., Konjović Z., Storing of Bibliometric Indicators in CERIF Data Model, in: International Conference on Internet Society Technology, 2015.
  • [21] Parmenter D., Key Performance Indicators: Developing, Implementing, and Using Winning KPIs, Second Edition, Jon Wiley & Sons, Inc., 2010.
  • [22] Quix C., Matthias J., Information integration in research information systems, Procedia Computer Science, 33, 2014, 18-24.
  • [23] Sivertsen G., Data integration in Scandinavia, Scientometrics, 106, 2, 2016, 849-855.
  • [24] Solodovnikova D., Data Warehouse Evolution Framework, in: Proceedings of Spring Young Researcher's Colloquium on Database and Information Systems, Moscow, Russia, 2007, 4.
  • [25] Teixeira da Silva J.A., Memon A.R., CiteScore: A cite for sore eyes, or a valuable, transparent metric?, Scientometrics, 111, 1, 2017, 553-556.
  • [26] Winkler W., The state of record linkage and current research problems, Technical report, Statistics of Income Division, US Census Bureau, 1999.
  • [27] Winter R., Strauch B., A method for demand-driven information requirements analysis in data warehousing projects, in: Proceedings of the 36th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Washington, DC, 2003, 231.1.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-21c70dc6-f7b1-4ac0-a5e7-6c9e34edc41f
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