Loading...
Please wait, while we are loading the content...
Similar Documents
Similarity Search for Multidimensional Data Sequences (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lee, Seok-Lyong Chun, Seok-Ju Kim, Deok-Hwan Lee, Ju-Hong Chung, Chin-Wan |
| Description | Time-series data, which are a series of one-dimensional real numbers, have been studied in various database applications. In this paper, we extend the traditional similarity search methods on time-series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multidimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences. Our method is designed (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and c... |
| File Format | |
| Language | English |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Various Database Application One-dimensional Real Number Multidimensional Data Sequence Time-series Data Entire Sequence Minimum Bounding Rectangle Large Database Efficient Similarity Function Data Element Candidate Sequence Similarity Search Video Stream Traditional Similarity Search Method Similar Multidimensional Data Sequence Query Sequence Irrelevant Sequence Special Importance In ICDE Query Processing Data Sequence |
| Content Type | Text |
| Resource Type | Article |
