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teaching:mfe:is [2016/04/13 16:19] msakr [A Generic Similarity Measure For Symbolic Trajectories] |
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| A symbolic trajectory is a very simple structure composed of a sequence of pairs (time interval, label). So, it is a time dependent label, where every label can tell something about the semantics of the moving object during its associated time interval. We think this model is promising because of its simplicity and genericness. | A symbolic trajectory is a very simple structure composed of a sequence of pairs (time interval, label). So, it is a time dependent label, where every label can tell something about the semantics of the moving object during its associated time interval. We think this model is promising because of its simplicity and genericness. | ||
| - | The goal of this thesis is to implement a similarity operator for symbolic trajectories. There are three dimensions of similarity in symbolic trajectories: temporal similarity, value similarity, and semantic similarity. Such an operator should be flexible to express arbitrary combinations of them. It should accept a pair of semantic trajectories and return a numerical value that can be used for clustering or ranking objects based on their similarity. Symbolic trajectories are similar to time series, except that labels are annotated by time intervals, rather than time points. We think that the techniques of time series similarity can be adopted for symbolic trajectories. This thesis should assess that, and implement a similarity measure based on time series similarity. The implementation is required to be done as an extension to PostGIS. We have already implemented some temporal types and operations on top of PostGIS, where you can start from. | + | The goal of this thesis is to implement a similarity operator for symbolic trajectories. There are three dimensions of similarity in symbolic trajectories: temporal similarity, value similarity, and semantic similarity. Such an operator should be flexible to express arbitrary combinations of them. It should accept a pair of semantic trajectories and return a numerical value that can be used for clustering or ranking objects based on their similarity. Symbolic trajectories are similar to time series, except that labels are annotated by time intervals, rather than time points. We think that the techniques of time series similarity can be adopted for symbolic trajectories. This thesis should assess that, and implement a similarity measure based on time series similarity. The implementation is required to be done as an extension to PostGIS. We have already implemented some temporal types and operations on top of PostGIS, where you can start from. |
| - | Here is a list of deliverables that should guide you working on this thesis. It is, however, fine to suggest modifications to this list during the work. | + | |
| + | **Deliverables** of the master thesis project | ||
| * Reporting on the state of art of semantic trajectory similarity measures. | * Reporting on the state of art of semantic trajectory similarity measures. | ||
| * Reporting on the state of art in time series similarity measures. | * Reporting on the state of art in time series similarity measures. | ||
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| + | **Interested?** | ||
| + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| - | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | + | **Status**: available |