The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
The VLDB Journal — The International Journal on Very Large Data Bases
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Indexing mobile objects using dual transformations
The VLDB Journal — The International Journal on Very Large Data Bases
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling algorithms in a stream operator
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Remembrance of streams past: overload-sensitive management of archived streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Query languages and data models for database sequences and data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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There is much interest in the processing of data streams for applications in the fields such as financial analysis, network monitoring, mobile services, and sensor network management. The key characteristic of stream data, that it continues to arrive, demands a new approach. This paper focuses on the problem of detecting, exactly, similar pairs of subsequences of arbitrary length in streaming fashion. We propose DAPSS (DAta stream Processing for Store and Search), an efficient and effective method to detect the similar pairs, which keeps (1) the feature data of each sequence in the memory space and (2) the compressed data of the original sequences in the disk space. Experiments on synthetic and real data sets show that DAPSS is significantly (up to 35 times) faster than the naive method while it guarantees the correctness of query results.