Advanced programming in the UNIX environment
Advanced programming in the UNIX environment
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient time-series subsequence matching using duality in constructing windows
Information Systems
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
RTAS '04 Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Summarizing and mining inverse distributions on data streams via dynamic inverse sampling
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Incorporating quality aspects in sensor data streams
Proceedings of the ACM first Ph.D. workshop in CIKM
Representing Data Quality in Sensor Data Streaming Environments
Journal of Data and Information Quality (JDIQ)
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In recent years, real-time processing of a large amount of infinite stream data becomes a hot research issue. For handheld devices, minimization of CPU operations is the most important factor in performance. In this paper, we propose the efficient algorithms that extract sequences similar to the given query sequence from the time-series stream such as network traffic data, stock prices, and sensor data. First, we formally define the stream sequence matching that finds similar sequences from the time-series stream. Second, we propose an efficient window-based approach by using the window construction mechanism of traditional subsequence matching methods. Third, we provide the notion of a window MBR and propose two different stream sequence matching algorithms based on the notion. Fourth, we formally prove correctness of the proposed algorithms by presenting the related theorems. Last, through extensive analysis and experiments, we show that our approach improves performance significantly compared with the naive approach. Experimental results show that our window-based approach improves performance by tens to hundreds of times over the naive approach. Overall, we believe that our methods would be very suitable for handheld devices as the embedded algorithms.