Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Wissensmodelle als Basis für intelligente Visualisierungssysteme

  • Chapter
Informationssysteme im Bauwesen 1

Part of the book series: VDI-Buch ((VDI-BUCH))

  • 8148 Accesses

Zusammenfassung

Durch die stetig wachsende Datenflut wird es zunehmend schwerer, interessante Informationen zu erkennen, diese richtig zu interpretieren und letztlich Erkenntnisse zu gewinnen. Durch den Einsatz von Visualisierungswerkzeugen kann dieser Prozess erheblich verbessert werden. Jedoch adressieren aktuelle Anwendungen vor allem den Experten. Um auch Endanwendern die Informationsvisualisierung zu ermöglichen, muss das Visualisierungssystem fehlendes Expertenwissen kompensieren. Hierzu eignen sich besonders Wissensmodelle. Ein verbreiteter Ansatz sind Ontologien, da sie die Beschreibung von komplexen Zusammenhängen und dabei die gleichzeitige Trennung vom Anwendungscode ermöglichen. Für die Visualisierung existierten zwar schon erste Bestrebungen für eine Visualisierungsontologie, jedoch sind diese nicht für die Verwendung in aktuellen intelligenten Systemen geeignet und zugänglich. Daher wird eine modulare, frei zugängliche Visualisierungsontologie, VISO, vorgestellt und deren praktischer Einsatz skizziert. Die iterativ entwickelte Ontologie basiert auf einer Vielzahl von verbreiteten Terminologien und Taxonomien der Domäne und deckt dabei Teilbereiche wie Daten, graphisches Vokabular und menschliche Aktivität ab.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www-958.ibm.com/software/data/cognos/manyeyes/.

  2. 2.

    http://www.tableausoftware.com/public/.

  3. 3.

    http://visual.ly/.

  4. 4.

    http://mmt.inf.tu-dresden.de/VO/.

  5. 5.

    http://www.w3.org/TR/system-info-api/.

  6. 6.

    http://bibliontology.com/.

  7. 7.

    http://www.w3.org/TR/owl2-overview/.

  8. 8.

    http://www.w3.org/2002/ws/sawsdl/.

Literatur

  1. Pierre Audion Conultants (2012) In-Memory-Datenanalyse in Zeiten von Big Data. Tech report

    Google Scholar 

  2. Heer J, van Ham F, Carpendale S, Weaver C, Isenberg P (2008) Creation and collaboration: engaging new audiences for information visualization. In: Information visualization, Bd 4950. Springer, Berlin, S 92–133

    Chapter  Google Scholar 

  3. Grammel L, Tory M, Storey MA (2010) How information visualization novices construct visualizations. In: Proc of InfoVis 2010

    Google Scholar 

  4. Ruiz F, Hilera JR (2006) Using ontologies in software engineering and technology. In: Calero C, Ruiz F, Piattini M (Hrsg) Ontologies for software engineering and software technology. Springer, Berlin, S 49–102

    Chapter  Google Scholar 

  5. Schapke SE, Katranuschkov P, Scherer RJ (2010) Towards ontology-based management of distributed multi-model project spaces. In: Proc of the CIB-W78 conference on IT in construction 2010

    Google Scholar 

  6. Paulheim H, Probst F (2010) Ontology-enhanced user interfaces: a survey. Int J Semantic Web Inf Syst, 36–59

    Google Scholar 

  7. Haber R, McNabb DA (1990) Visualization idioms: a conceptual model for scientific visualization systems visualization. In: Scientific computing. IEEE Comput Soc, Los Alamitos, S 74–93

    Google Scholar 

  8. Card SK, Mackinlay JD, Shneiderman B (1999) Readings in information visualization: using vision to think. Morgan Kaufmann, San Mateo

    Google Scholar 

  9. Chi EA (2000) Taxonomy of visualization techniques using the data state reference model. In: IEEE symposium on information visualization, S 69–75

    Google Scholar 

  10. Boukhelifa N, Roberts J, Rodgers P (2003) A coordination model for exploratory multiview visualization. In: Coordinated and multiple views in exploratory visualization, S 76–85

    Google Scholar 

  11. van Wijk JJ (2005) The value of visualization. In: Proc of IEEE visualization, S 79–86

    Google Scholar 

  12. Tory M, Möller T (2004) Rethinking visualization: a high-level taxonomy. In: IEEE symposium on information visualization, S 151–158

    Chapter  Google Scholar 

  13. Duke D, Brodlie K, Duce D, Herman I (2005) Do you see what I mean? IEEE Comput Graph Appl 25:6–9

    Article  Google Scholar 

  14. Allen RB (1997) Mental models and user models. In: Helander MG, Landauer TK, Prabhu PV (Hrsg) Handbook of human-computer interaction. Elsevier, Amsterdam, S 49–63

    Google Scholar 

  15. Gilson O, Silva N, Grant P, Chen M (2008) From web data to visualization via ontology mapping. Comput Graph Forum 27:959–966

    Article  Google Scholar 

  16. Duke D, Brodlie K, Duce D (2004) Building an Ontology of Visualization. IEEE Vis

    Google Scholar 

  17. Potter R, Wright H (2006) An ontological approach to visualization resource management. DSV-IS, S 151–156

    Google Scholar 

  18. Rhodes P, Kraemer E, Reed B (2006) VisIOn: an interactive visualization ontology. ACM-SE 44. In: Proc of the 44th annual Southeast regional conference. ACM, New York, S 405–410

    Chapter  Google Scholar 

  19. Shu G, Avis N, Rana O (2008) Bringing semantics to visualization services. Adv Eng Softw 39:514–520

    Article  Google Scholar 

  20. Paulheim H (2009) Ontologies for user interface integration. In: Proc of the 8th international semantic web conference. Springer, Berlin, S 973–981

    Google Scholar 

  21. Pietschmann S (2012) Modellgetriebene Entwicklung adaptiver, komponentenbasierter Mashup-Anwendungen. Dissertation, TU Dresden

    Google Scholar 

  22. Wang X, Jeong DH, Dou W, Lee SW, Ribarsky W, Chang R (2009) Defining and applying knowledge conversion processes to a visual analytics system. Comput Graph 33:616–623

    Article  Google Scholar 

  23. Chen M, Ebert D, Hagen H, Laramee R, van Liere R, Ma KL, Ribarsky W, Scheuermann G, Silver D (2009) Data, information, and knowledge in visualization. IEEE Comput Graph Appl 29:12–19

    Article  Google Scholar 

  24. Andrienko N, Andrienko G (2006) Exploratory analysis of spatial and temporal data: a systematic approach. Springer, New York

    Google Scholar 

  25. Keim DA (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph 8:1–8

    Article  Google Scholar 

  26. Mazza R (2009) Introduction to information visualization. Springer, Berlin

    Google Scholar 

  27. Kemp K, Vckovski A (1998) Towards an ontology of fields. In: Third international conference on GeoComputation

    Google Scholar 

  28. Stevens SS (1946) On the theory of scales of measurement. Science 103:677–680

    Article  MATH  Google Scholar 

  29. Ware C (2004) Information visualization: perception for design, 2. Aufl. Morgan Kaufmann, San Mateo

    Google Scholar 

  30. Wilkinson L (2005) The grammar of graphics. Springer, Berlin

    MATH  Google Scholar 

  31. Taylor BN, Thompson A (2001) The international system of units (SI). US Dept. of Commerce, Technology Administration, National Institute of Standards and Technology

    Google Scholar 

  32. Engelhardt vJ (2002) The language of graphics. PhD thesis, Universiteit van Amsterdam

    Google Scholar 

  33. Mackinlay J (1986) Automating the design of graphical presentations of relational information. ACM Trans Graph 5:110–141

    Article  Google Scholar 

  34. Lengler R, Eppler MJ (2007) Towards a periodic table of visualization methods for management. In: Proc of the conference on graphics and visualization in engineering (GVE 2007), IASTED, S 1–6

    Google Scholar 

  35. Bertin J (1983) Semiology of graphics. University of Wisconsin Press, Madison

    Google Scholar 

  36. Limbourg Q, Vanderdonckt J (2003) Comparing task models for user interface design. In: Diaper D, Stanton N (Hrsg) The handbook of task analysis for human-computer interaction. Lawrence Erlbaum Associates, Mahwah, S 135–154

    Google Scholar 

  37. Norman DA (1988) The psychology of everyday things. Doubleday, New York

    Google Scholar 

  38. Zhang J (1996) A representational analysis of relational information displays. Int J Hum-Comput Stud 45(1):59–74

    Article  Google Scholar 

  39. OASIS BPEL4People (2009). TC Web services – human task (ws-humantask) specification

    Google Scholar 

  40. Zhou MX, Feiner SK (1998) Visual task characterization for automated visual discourse synthesis. In: Proc of the SIGCHI conference on human factors in computing systems. ACM, New York, S 392–399

    Google Scholar 

  41. Yi JS, Kang Ya, Stasko J, Jacko J (2007) Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans Vis Comput Graph 13:1224–1231

    Article  Google Scholar 

  42. Bardram JE (1997) I love the system – I just don’t use it! GROUP’97: proc of the international ACM SIGGROUP conference on supporting group work ACM, New York, S 251–260

    Google Scholar 

  43. Card SK, Newell A, Moran TP (1983) The psychology of human-computer interaction. Lawrence Erlbaum Associates Inc, Mahwah

    Google Scholar 

  44. Gotz D, Zhou M (2008) Characterizing users’ visual analytic activity for insight provenance. In: IEEE symposium on visual analytics science and technology (VAST’008), S 123–130

    Chapter  Google Scholar 

  45. Paterno F (2001) Task models in interactive software systems. In: Chang S (Hrsg) Handbook of software engineering and knowledge engineering. World Scientific Publishing Company, Singapore, S 1

    Google Scholar 

  46. Amar R, Stasko J (2004) A knowledge task-based framework for design and evaluation of information visualizations. In: IEEE symposium on information visualization, S 143–150

    Chapter  Google Scholar 

  47. Amar R, Eagan J, Stasko J (2005) Low-level components of analytic activity in information visualization. In: IEEE symposium on information visualization, S 111–117

    Google Scholar 

  48. Nigay L, Vernier F (1998) Design method of interaction techniques for large information spaces. In: Proc of the working conference on advanced visual interfaces. ACM, New York, S 37–46

    Google Scholar 

  49. Bezold M (2009) Describing user interactions in adaptive interactive systems. In: User modeling, adaptation, and personalization, Bd 5535. Springer, Berlin/Heidelberg, S 150–161

    Chapter  Google Scholar 

  50. Wehrend S, Lewis C (1990) A problem-oriented classification of visualization techniques. In: Proc of the 1st conference on Visualization’90. IEEE Comput Soc, Los Alamitos, S 139–143

    Google Scholar 

  51. Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: IEEE symposium on visual languages, S 336–343

    Google Scholar 

  52. Chi EHH, Riedl J (1998) An operator interaction framework for visualization systems. In: IEEE symposium on information visualization, S 63–70

    Google Scholar 

  53. Fikkert W, D’Ambros M, Bierz T, Jankun-Kelly TJ (2007) Interacting with visualizations. Human-centered visualization environments, Bd 4417. Springer, Berlin, S 77–162

    Book  Google Scholar 

  54. Allen RB (1997) Mental models and user models. In: Helander MG, Landauer TK, Prabhu PV (Hrsg) Handbook of human-computer interaction. Elsevier, Amsterdam, S 49–63

    Google Scholar 

  55. Brusilovsky P, Millán E (2007) User models for adaptive hypermedia and adaptive educational systems. In: The adaptive web: methods and strategies of web personalization. Springer, Berlin, S 3–53

    Chapter  Google Scholar 

  56. Rich E (1983) Users are individuals: individualizing user models. Int J Man-Mach Stud 18:199–214

    Article  Google Scholar 

  57. Golemati M, Halatsis C, Vassilakis C, Katifori A, Lepouras GA (2006) Context-based adaptive visualization environment. In: Tenth international conference on information visualization, S 62–67

    Google Scholar 

  58. Golemati M, Katifori A, Vassilakis C, Lepouras G, Halatsis C (2007) Creating an ontology for the user profile: method and applications. In: Proc of the first international conference on research challenges in information science

    Google Scholar 

  59. Heckmann D, Schwartz T, Brandherm B, Schmitz M, von Wilamowitz-Moellendorff M (2005) GUMO – the general user model ontology. User modeling, Bd 3538. Springer, Berlin, S 428–432

    Google Scholar 

  60. Brickley D, Miller L (2010) FOAF vocabulary specification 0.98. Online

    Google Scholar 

  61. Abel F, Heckmann D, Herder E, Hidders J, Houben GJ, Krause D, Leonardi E, van der Slujis K (2009) A framework for flexible user profile mashups. In: Proc of international workshop on adaptation and personalization for Web 2.0 (AP-WEB 2.0 2009), S 1–10.

    Google Scholar 

  62. Pietschmann S, Mitschick A, Winkler R, Meißner K (2008) CroCo: ontology-based, cross-application context management. In: Proc of the 3rd international workshop on semantic media adaptation and personalization (SMAP 2008), IEEE CPS

    Google Scholar 

  63. Strang T, Linnhoff-Popien C (2004) A context modeling survey. In: First international workshop on advanced context modelling, reasoning and management

    Google Scholar 

  64. Strassner J, O’Sullivan D (2009) Knowledge management for context-aware, policy-based ubiquitous computing systems. In: Proc of the 6th international workshop on managing ubiquitous communications and services. ACM, New York, S 67–76

    Google Scholar 

  65. Fernández-López M, Gómez-Pérez A, Juristo N (1997) Methontology: from ontological art towards ontological engineering. In: Proc symposium on ontological engineering of AAAI

    Google Scholar 

  66. Simperl E, Mochol M, Bürger T, Popov I (2009) Achieving maturity: the state of practice in ontology engineering. In: Meersman R, Dillon T, Herrero P (Hrsg) On the move to meaningful Internet systems: OTM 2009, Bd 5871. Springer, Berlin/Heidelberg, S 983–991

    Chapter  Google Scholar 

  67. Bergman MA (2010) New methodology for building lightweight, domain ontologies. Online 01.09.2010

    Google Scholar 

  68. Fuchs S, Kadolsky M, Scherer RJ (2011) Formal description of a generic multi-model. In: Proc of 20th IEEE international workshops on enabling technologies: infrastructure for collaborative enterprises (WETICE’11)

    Google Scholar 

  69. Pietschmann SA (2009) Model-driven development process and runtime platform for adaptive composite web applications. Int J Adv Internet Technol 2(4)

    Google Scholar 

  70. Voigt M, Pietschmann S, Meißner K (2013) A semantics-based, end-user-centered information visualization process for semantic web data. Semantic models for adaptive interactive systems. Springer, Berlin

    Google Scholar 

  71. Voigt M, Werstler A, Polowinski J, Klaus M (2012) Weighted faceted browsing for characteristics-based visualization selection through end users. In: Proc of the 4th ACM SIGCHI symposium on engineering interactive computing systems (EICS). ACM, New York, S 151–156

    Chapter  Google Scholar 

  72. Voigt M, Pietschmann S, Grammel L, Meißner K (2012) Context-aware recommendation of visualization components. In: Proc of the 4th international conference on information, process, and knowledge management. (eKNOW 2012), XPS

    Google Scholar 

  73. Pietschmann S, Voigt M, Meißner K (2009) Adaptive rich user interfaces for human interaction in business processes. In: Proc of the 10th international conference on web information systems engineering (WISE 2009). Springer, Berlin, S 351–364

    Chapter  Google Scholar 

  74. Tauscher H, Scherer RJ (2012) Towards a configurable nd-viewer for building information models. In: Gudnason G, Scherer R (Hrsg) eWork and eBusiness in architecture, engineering and construction, European conference on product and process modelling 2012. CRC Press/Balkema, Boca Raton/Rotterdam, S 271–277, ISBN 978-0415621281

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Software- und Multimediatechnik, Technische Universität Dresden, Dresden, Deutschland

    Martin Voigt, Jan Polowinski & Klaus Meißner

Authors
  1. Martin Voigt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Polowinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Klaus Meißner
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Technische Universität Dresden, Dresden, Germany

    Raimar J. Scherer

  2. Technische Universität Dresden, Dresden, Germany

    Sven-Eric Schapke

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Voigt, M., Polowinski, J., Meißner, K. (2014). Wissensmodelle als Basis für intelligente Visualisierungssysteme. In: Scherer, R., Schapke, SE. (eds) Informationssysteme im Bauwesen 1. VDI-Buch. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40883-0_19

Download citation

Publish with us

Policies and ethics

Search

Navigation