Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The Wisdom of Crowds
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
On the "localness" of user-generated content
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Accelerating Spatial Data Processing with MapReduce
ICPADS '10 Proceedings of the 2010 IEEE 16th International Conference on Parallel and Distributed Systems
MongoDB: The Definitive Guide
Scalable SQL and NoSQL data stores
ACM SIGMOD Record
Jeocrowd: collaborative searching of user-generated point datasets
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Automatic gazetteer enrichment with user-geocoded data
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
| Hi-index | 0.00 |
The ever-increasing stream of Web and mobile applications addressing geospatial data creation has been producing a large number of user-contributed geospatial datasets. This work proposes a means to query such data using a collaborative Web-based approach. We employ crowdsourcing to the fullest in that used-generated point-cloud data will be mined by the crowd not only by providing feature names, but also by contributing computing resources. We employ browser-based collaborative search for deriving the extents of geospatial objects (Points of Interest) from point-cloud data such as Flickr image locations and tags. The data is aggregated by means of a hierarchical grid in connection with an exploratory and a refinement search phase. A performance study establishes the effectiveness of our approach with respect to the amount of data that needs to be retrieved from the sources and the quality of the derived spatial features.