Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
The multimedia challenges raised by pervasive games
Proceedings of the 13th annual ACM international conference on Multimedia
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
The gopher game: a social, mobile, locative game with user generated content and peer review
Proceedings of the international conference on Advances in computer entertainment technology
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
EyeSpy: supporting navigation through play
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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The traditional, expert-based process of knowledge acquisition is known to be both slow and costly. With the advent of theWeb 2.0, community-based approaches have appeared. These promise a similar or even higher level of information quantity by using the collaborative work of voluntary contributors. Yet, the community-driven approach yields new problems on its own, most prominently contributor motivation and data quality. Our former work [1] has shown, that the issue of contributor motivation can be solved by embedding the data collection activity into a gaming scenario. Additionally, good games are designed to be replayable and thus well suited to generate redundant datasets. In this paper we propose semantic view area clustering as a novel approach to aggregate semantically tagged objects to achieve a higher overall data quality. We also introduce the concept of semantic barriers as a method to account for interaction betwen spatial and semantic data. We also successfully evaluate our algorithm against a traditional clustering method.