BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Maintaining time-decaying stream aggregates
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
EUROMICRO '03 Proceedings of the 29th Conference on EUROMICRO
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Annotating geospatial data based on its semantics
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Early warning systems in practice: performance of the SAFE system in the field
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Self-Adaptive Anytime Stream Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
The Journal of Machine Learning Research
Context- and situation-awareness in information logistics
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Least squares quantization in PCM
IEEE Transactions on Information Theory
Continuous queries on trajectories of moving objects
Proceedings of the 16th International Database Engineering & Applications Sysmposium
| Hi-index | 0.00 |
Natural calamities and man-made hazards can occur in an unexpected and unanticipated manner. They cause large-scale damage, create disruptions, and need instant reaction. In the event of sudden onset of a crisis, rapid formulation of a notification strategy, timely dispatch of alerts, and action on those alerts are important elements of early warning systems that can save lives. However, current methods of disaster alerting lack in the area of targeted communication of hazard information. Location data of the population available as a spatial data stream can allow dynamic identification of homogeneous clusters of people. Crisis notifications can then be targeted by personalizing information and instructions for each cluster. In this paper, we present an approach for dynamically partitioning a region into areas around a hazard using clustering of real-time streaming data to aid emergency response management. We lay down important requirements for the clustering technique from the perspective of our scenario and select an algorithm for our implementation after comparison with others. We employ a weighted distance measure and demonstrate the performance of our model in different settings through a series of experiments using a dataset of cell tower locations of users in Ivory Coast in Africa.