Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A middleware for fast and flexible sensor network deployment
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
IBM infosphere streams for scalable, real-time, intelligent transportation services
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A scalable distributed stream mining system for highway traffic data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Event processing and real-time monitoring over streaming traffic data
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
Trajectories for novel and detailed traffic information
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Adaptive input admission and management for parallel stream processing
Proceedings of the 7th ACM international conference on Distributed event-based systems
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Traffic information systems based on mobile, in-car sensor technology are a challenge for data management systems as a huge amount of data has to be processed in real-time. Data mining methods must be adapted to cope with these challenges in handling streaming data. Although several data stream mining methods have been proposed, an evaluation of such methods in the context of traffic applications is yet missing. In this paper, we present an evaluation framework for data stream mining for traffic applications. We apply a traffic simulation software to emulate the generation of traffic data by mobile probes. The framework is evaluated in a first case study, namely queue-end detection. We show first results of the evaluation of a data stream mining method, varying multiple parameters for the traffic simulation. The goal of our work is to identify parameter settings for which the data stream mining methods produce useful results for the traffic application at hand.