On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Continuous queries over data streams
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
TrafficView: traffic data dissemination using car-to-car communication
ACM SIGMOBILE Mobile Computing and Communications Review
A native extension of SQL for mining data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IBM infosphere streams for scalable, real-time, intelligent transportation services
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
KOIOS: utilizing semantic search for easy-access and visualization of structured environmental data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Very large scale OWL reasoning through distributed computation
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Proceedings of the 2013 international conference on Intelligent user interfaces
TYPifier: Inferring the type semantics of structured data
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
Improving traffic prediction with tweet semantics
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Predicting knowledge in an ontology stream
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. We present how semantic diagnosis and predictive reasoning, both using and interpreting semantics of data to deliver useful, accurate and consistent inferences, have been exploited and adapted systematized in an intelligent user interface. Our prototype of semantics-aware traffic analytics and reasoning, experimented in Dublin City Ireland, works and scales efficiently with historical together with real live and heterogeneous stream data.