Skip to main content

Clustering Spatial Data for Aggregate Query Processing in Walkthrough: A Hypergraph Approach

  • Conference paper
  • 1041 Accesses

Part of the Lecture Notes in Computer Science book series (TEDUTAIN,volume 7145)

Abstract

Nowadays, classical 3D object management systems use only direct visible properties and common features to model relationships between objects. In this paper we propose a new Object-oriented HyperGraph-based Clustering (OHGC) approach based on a behavioral walkthrough system that uses traversal patterns to model relationships between users and exploits semantic-based clustering techniques, such as association, intra-relationships, and inter-relationships, to explore additional links throughout the behavioral walkthrough system. The final aim consists in involving these new links in prediction generation, to improve performance of walkthrough system. OHGC is evaluated in terms of response time and number of retrieved objects on a real traversal dataset.

Keywords

  • walkthrough
  • data placement
  • hypergraph
  • prefetching
  • clustering

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sajadi, B., et al.: A Novel Page-Based Data Structure for Interactive Walkthroughs. In: ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D (2009)

    Google Scholar 

  2. Bertini, E., Lalanne, D.: Investigating and Reflecting on the Integration of Automatic Data Analysis and Visualization in Knowledge Discovery. ACM SIGKDD Exploration 11(2), 9–18 (2009)

    CrossRef  Google Scholar 

  3. Plemenos, D., Miaoulis, G.: Visual Complexity and Intelligent Computer Graphics Techniques Enhancements. Springer, New York (2009)

    CrossRef  Google Scholar 

  4. Zhu, Y.: Uniform Remeshing with an Adaptive Domain: a New Scheme for View-Dependent Level-of-Detail Rendering of Meshes. IEEE Transactions on Visualization and Computer Graphics 11(3), 306–316 (2005)

    CrossRef  Google Scholar 

  5. Yoon, S.E., Manocha, D.: Cache-Efficient Layouts of Bounding Volume Hierarchies. Eurographics 25(3), 507–516 (2006)

    Google Scholar 

  6. Chisnall, D., Chen, M., Hansen, C.: Knowledge-based Out-of-Core Algorithms for Data Management in Visualization. In: EG/IEEE-VGTC Symposium on Visualization (2006)

    Google Scholar 

  7. Hung, S.S., Liu, D.S.M.: Using Predictive Prefetching to Improve Interactive Walkthrough Latency. CAVW Journal 17(3-4), 469–478 (2006)

    Google Scholar 

  8. Sivathanu, M., et al.: Arpaci-Dusseau. Semantically-Smart Disk Systems. In: Proceedings of the Second USENIX Conference on File and Storage Technologies (2003)

    Google Scholar 

  9. Han, E.-H., Karypis, G., Kumar, V., Mobasher, B.: Clustering based on Association Rule Hypergraph. In: Workshop on Research Issues on DMKD (1997)

    Google Scholar 

  10. Chim, J., et al.: CyberWalk: A Web-based Distributed Virtual Walkthrough Environment. IEEE Transactions on Multimedia 5(4), 503–515 (2003)

    Google Scholar 

  11. Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)

    Google Scholar 

  12. Nam, G.-J., et al.: A Fast Hierarchical Quadratic Placement Algorithm. IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems 25(4), 678–691 (2006)

    CrossRef  Google Scholar 

  13. Demir, E., Aykanat, C., Cambazoglu, B.B.: Clustering Spatial Networks for Aggregate Query Processing: A Hypergraph Approach. Information Systems 33, 1–17 (2008)

    CrossRef  Google Scholar 

  14. Jaccard, P.: The Distribution of the Flora of the Alpine Zone. New Phytologist, 37–50 (1912)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hung, SS., Chiu, C.M., Fu, T.T., Chen, JT., Tsaih, D., Tsay, JJ. (2012). Clustering Spatial Data for Aggregate Query Processing in Walkthrough: A Hypergraph Approach. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds) Transactions on Edutainment VII. Lecture Notes in Computer Science, vol 7145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29050-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29050-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29049-7

  • Online ISBN: 978-3-642-29050-3

  • eBook Packages: Computer ScienceComputer Science (R0)