OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Identifying content for planned events across social media sites
Proceedings of the fifth ACM international conference on Web search and data mining
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Location Extraction from Social Networks with Commodity Software and Online Data
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
Efficient event detection by exploiting crowds
Proceedings of the 7th ACM international conference on Distributed event-based systems
| Hi-index | 0.00 |
The wealth of information that is readily available nowadays grants researchers and practitioners the ability to develop techniques and applications that monitor and react to all sorts of circumstances: from network congestions to natural catastrophies. Therefore, it is no longer a question of whether this can be done, but how to do it in real-time, and if possible proactively. Consequently, it becomes a necessity to develop a platform that will aggregate all the necessary information and will orchestrate it in the best way possible, towards meeting these goals. A main problem that arises in such a setting is the high diversity of the incoming data, obtained from very different sources such as sensors, smart phones, GPS signals and social networks. The large volume of the incoming data is a gift that ensures high quality of the produced output, but also a curse, because higher computational resources are needed. In this paper, we present the architecture of a framework designed to gather, aggregate and process a wide range of sensory input coming from very different sources. A distinctive characteristic of our framework is the active involvement of citizens. We guide the description of how our framework meets our requirements through two indicative use cases.