Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Randomized algorithms
Topological sorting of large networks
Communications of the ACM
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Complex event detection at wire speed with FPGAs
Proceedings of the VLDB Endowment
Accurate, low-energy trajectory mapping for mobile devices
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Online windowed subsequence matching over probabilistic sequences
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Uncertain time-series similarity: return to the basics
Proceedings of the VLDB Endowment
Temporal Correlation of Interference in Wireless Networks with Rayleigh Block Fading
IEEE Transactions on Mobile Computing
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Regular expression matching over sequences in real time is a crucial task in complex event processing on data streams. Given that such data sequences are often noisy and errors have temporal and spatial correlations, performing regular expression matching effectively and efficiently is a challenging task. Instead of the traditional approach of learning a distribution of the stream first and then processing queries, we propose a new approach that efficiently does the matching based on an error model. In particular, our algorithms are based on the realistic Markov chain error model, and report all matching paths to trace relevant basic events that trigger the matching. This is much more informative than a single matching path. We also devise algorithms to efficiently return only top-k matching paths, and to handle negations in an extended regular expression. Finally, we conduct a comprehensive experimental study to evaluate our algorithms using real datasets.