Efficient Mining of Active Components in a Network of Time Series
| dc.contributor.advisor | Papangelis, Emmanouil | |
| dc.contributor.author | Shafieesabet, Mahta | |
| dc.date.accessioned | 2022-08-08T15:45:39Z | |
| dc.date.available | 2022-08-08T15:45:39Z | |
| dc.date.copyright | 2022-02-11 | |
| dc.date.issued | 2022-08-08 | |
| dc.date.updated | 2022-08-08T15:45:39Z | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Master's | |
| dc.degree.name | MSc - Master of Science | |
| dc.description.abstract | Let a network of time series be a set of nodes assuming an underlying network structure, where each node is associated with a discrete time series. The road network, the human brain, online social media are a few examples of domain-specific applications that can be modelled as networks of time series. Now assume that the sequence of time series data points observed on a node determines whether the node is on (active) or off (inactive). Then, at each time step, a set of induced subgraphs can be formed from the subset of active nodes; we call these induced subgraphs active components. In this research, our goal is to efficiently detect and maintain/report the active components over time. | |
| dc.identifier.uri | http://hdl.handle.net/10315/39584 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject | Computer science | |
| dc.subject.keywords | Networks | |
| dc.subject.keywords | Graph | |
| dc.subject.keywords | Dynamic graphs | |
| dc.subject.keywords | Subgraph model | |
| dc.subject.keywords | Time series | |
| dc.subject.keywords | Active nodes | |
| dc.title | Efficient Mining of Active Components in a Network of Time Series | |
| dc.type | Electronic Thesis or Dissertation |
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