Affiliation:
1. University of Bamberg, Germany
Abstract
In this chapter, the authors outline how collections of georeferenced media items can be indexed and searched in P2P IR systems. They discuss different types of P2P IR systems and focus in detail on an approach based on collection description and selection techniques. This approach tries to adequately describe and select collections of georeferenced media items. Finally, the authors discuss its broad applicability in various application fields.
Reference39 articles.
1. Ahlers, D., & Boll, S. (2009). Adaptive geospatially focused crawling. In D. Cheung, I. Song, W. Chu, X. Hu, J. Lin, J. Li, & Z. Peng (Eds.), Proceedings of the 18th ACM International Conference on Information and Knowledge Management, (pp. 445-454). Hong Kong, China: ACM.
2. Arguello, J., Diaz, F., Callan, J., & Crespo, J.-F. (2009). Sources of evidence for vertical selection. In M. Sanderson, C. Zhai, J. Zobel, J. Allan, & J.-A. Aslan (Eds.), Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 315-322). Boston, MA: ACM.
3. Beaver, D., Kumar, S., Li, H. C., Sobel, J., & Vajgel, P. (2010). Finding a needle in haystack: Facebook's photo storage. In Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI 2010). Berkeley, CA: USENIX Association.
4. Becker, B., Franciosa, P. G., Gschwind, S., Ohler, T., Thiemt, G., & Widmayer, P. (1991). An optimal algorithm for approximating a set of rectangles by two minimum area rectangles. In H. Bieri & H. Noltemeier (Eds.), Proceedings of the International Workshop on Computational Geometry, (pp. 13-25). Berlin, Germany: Springer.
5. Combining the evidence of multiple query representations for information retrieval