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Guidelines for method users

Based on the results of our benchmark, we propose a set of practical guidelines for method users (Figure 1 and We postulate that, as a method’s performance is heavily dependent on the trajectory type being studied, the choice of method should currently be primarily driven by the anticipated trajectory topology in the data. For the majority of use cases, the user will know very little about the expected trajectory, except perhaps whether the data is expected to contain multiple disconnected trajectories, cycles or a complex tree structure. In each of these use cases, our evaluation suggests a different set of optimal methods, as shown in Figure 1. Several other factors will also impact the choice of methods, such as the dimensions of the dataset, and the prior information which is available. These factors and several others can all be dynamically explored in our interactive app ( This app can also be used to query the results of this evaluation, such as filtering the datasets, or changing the importance of the evaluation metrics for the final ranking.

Figure 1: Practical guidelines for method users. As the performance of a method mostly depends on the topology of the trajectory, the choice of TI method will be primarily influenced by the user’s existing knowledge about the expected topology in the data. We therefore devised a set of practical guidelines, which combines the method’s performance, user friendliness and the number of assumptions a user is willing to make about the topology of the trajectory. Methods to the right are ranked according to their performance on a particular (set of) trajectory type. Further to the right are shown the accuracy (+: scaled performance ≥ 0.9, ±: > 0.6), usability scores (+: ≥ 0.9, ± ≥ 0.6), estimated running times and required prior information.

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