LinkDaViz – automatic binding of linked data to visualizations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9366, Issue , 2015, Pages 147-162

  Thellmann, Klaudia ^a   Galkin, Michael ^a   Orlandi, Fabrizio ^a   Auer, Sören ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; LINKED DATA; SEMANTIC WEB; VISUALIZATION;

BINDING DATA; HEURISTIC ANALYSIS; NON-TECHNICAL USERS; TECHNICAL DETAILS; USER FRIENDLY; VISUALIZATION MODELING; WEB OF DATUM; WEB-BASED IMPLEMENTATION;

DATA VISUALIZATION;

EID: 84952310419     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-25007-6_9     Document Type: Conference Paper

References (14)

1. Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:Synopsviz - a framework for hierarchical linked data visual exploration and analysis. In: 11th Extended Semantic Web Conference (ESWC 2014) (2014)
2. Brunetti, J.M., Auer, S., García, R., Klímek, J., Nečaský, M.: Formal linked data visualization model. In: Proc. IIWAS, IIWAS 2013, pp. 309–318. ACM, NY (2013)
3. Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems, Revised Reprint: Other titles in applied mathematics. Society for Industrial and Applied Mathematics (SIAM) (2009)
4. Dadzie, A.S., Rowe, M.: Approaches to visualising linked data: A survey. Semantic Web 2(2), 89–124 (2011)
5. Höfler, P., Mutlu, B.: Code query wizard and vis wizard: Supporting exploration and analysis of linked data. ERCIM News (96) (2014)
6. Klíme, J., Helmich, J., Nečaský, M.: Payola: collaborative linked data analysis and visualization framework. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 147–151. Springer, Heidelberg (2013)
7. Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5(1), 32–38 (1957)
8. Mutlu, B., Hoefler, P., Sabol, V., Tschinkel, G., Granitzer, M.: Automated visualization support for linked research data. In: Lohmann, S. (ed.) CEUR Workshop Proceedings, vol. 1026, pp. 40–44. CEUR-WS.org (2013)
9. Nielsen, J., Landauer, T.K.: A mathematical model of the finding of usability problems. In: Proceedings of the INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems, pp. 206–213. ACM (1993)
10. Skjaeveland, M.G.: Sgvizler: a javascript wrapper for easy visualization of sparql result sets. In: Simperl, E., Norton, B., Mladenic, D., Della Valle, E., Fundulaki, I., Passant, A., Troncy, R. (eds.) The Semantic Web: ESWC 2012 Satellite Events, Lecture Notes in Computer Science, vol. 7540, pp. 361–365. Springer, Heidelberg (2015). http://dx.doi.org/10.1007/978-3-662-46641-427
11. Stevens, S.S.: On the theory of scales of measurement. Science 103, 677–680 (1946)
12. Stevens, S.S.: Measurement. In: Maranell, G.M. (ed.) Scaling; a Sourcebook for Behavioral Scientists. Aldine Publishing Company (1974)
13. Voigt, M., Pietschmann, S., Grammel, L., Meißner, K.: Context-aware recommendation of visualization components. In: 4th Int. Conf. on Information, Process, and Knowledge Management, eKNOW 2012, pp. 101–109 (2012)
14. Voigt, M., Pietschmann, S., Meißner, K.: A semantics-based, end-user-centered information visualization process for semantic web data. In: Semantic Models for Adaptive Interactive Systems, pp. 83–107. Springer (2013)