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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Tang Hongyan Li Feifei Li Lilue Fan |
| Copyright Year | 2013 |
| Description | Author affiliation: Key Lab. of Machine Perception, Peking Univ., Beijing, China (Yan Tang; Hongyan Li; Feifei Li; Lilue Fan) |
| Abstract | In medical field, patterns over time-varied data streams usually imply high domain value. The variations of patterns can often be very complex and hard to evaluate. Traditional methods usually take each pattern as a whole to analyze data stream variations or only focus on one type of variation; however, few works have achieved a widely applicable resolution. This paper considers the feature of sub parts for data stream patterns and studies their variations and relationships from the perspective of multiple dimensions, to explore a comprehensive understanding for the variation history and effectively support different types of queries to help analyze the variations. This paper first decomposes patterns into different dimensions and then evaluates the variations of each dimension. After that, a data cube called VS-Cube is used to find out the variations of a single dimension as well as the relationships between different dimensions within a certain pattern. At last, a case study on disease MI over medical stream is given to demonstrate the effectiveness and efficiency of our proposed methods. |
| Sponsorship | IEEE Comput.Soc. |
| Starting Page | 33 |
| Ending Page | 39 |
| File Size | 250125 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781479913091 |
| DOI | 10.1109/BIBM.2013.6732597 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Pattern variations Aggregates Multi-dimensional pattern Electrocardiography Educational institutions OLAP Real-time systems Medical diagnostic imaging Diseases Data Stream |
| Content Type | Text |
| Resource Type | Article |
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