Purpose-of-Visit-Driven Semantic Similarity Analysis on Semantic Trajectories for Enhancing The Future Location Prediction

Karatzoglou, Antonios; Koehler, Dominik; Beigl, Michael

doi:10.1109/percomw.2018.8480230

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…This work relies on our previous investigations [ 5 ] and extends them as follows. First, it provides a more thorough analytical view upon the related work and the fundamental components of our approach ( Section 2 , Section 3 , Section 5 and Section 6 ).…”

Section: Introductionmentioning

confidence: 95%

“…In Section 5 , we extend the spatial Markov model by taking both time and activity additionally into account and we compare it with our initial vanilla spatial model. Finally, in Section 6 , we implement and evaluate, besides the multi-user model in [ 5 ], the respective single-user variants of our aforementioned semantic-similarity enhanced PoVDF-based approach as well, by comparing them among other against the semantic trajectory mining and prefix tree based approach of Ying et al [ 6 ]. Moreover, we explore how individual features (e.g., time, day, activity, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Karatzoglou

Kohler

Beigl

2018

Sensors

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In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Karatzoglou

Kohler

Beigl

2018

Sensors

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“…Although the above perspectives suggest multiple methods to the challenge of filling the semantic gaps in the location data, only one paper [34] provides a mechanism to facilitate the evaluation of the external Geo-location provider. Nevertheless, none of the selected papers rationalised the selection of specific CDS nor provides a comparison or inter-reliability test of the accuracy of various Geolocation APIs, despite the significance of this matter.…”

Section: Annotationmentioning

confidence: 99%

From GPS to semantic data: how and why—a framework for enriching smartphone trajectories

et al. 2021

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Deriving human behaviour from smartphone location data is a multitask enrichment process that can be of value in behavioural studies. Optimising the algorithmic details of the enrichment tasks has shaped the current advances in the literature. However, the lack of a processing framework built around those advances complicates the planning for implementing the enrichment. This work fulfils the need for a holistic and integrative view that comprehends smartphone-specific requirements and challenges to help researchers plan the implementation. We propose a structural framework from a systematic literature review conducted to pinpoint the main challenges and requirements of research on enriching location data. We classify findings based on the enrichment task and integrate them accordingly into workflows that facilitate the task’s implementation. These workflows help researchers better streamline their implementations of the enrichment process and analyse errors within and across tasks. Moreover, researchers can integrate the presented findings with the proposed opportunities to better predict the impact of their research.

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“…Karatzoglou et al's work explores a big variety of models with respect to modeling human semantic trajectories and predicting the user's next semantic location. In [12] and in [18] they evaluate a multi-dimensional Markov Chain model with respect to predicting among activity-enriched semantic trajectories and show that it is able to outperform Ying et al's framework in terms of accuracy. With regard to recall however, they could identify certain limitations on behalf of the model due to its adverse dependency on the small size and the sparsity of the available training dataset.…”

Section: Related Workmentioning

confidence: 99%

Multi-channel Convolutional Neural Networks for Handling Multi-dimensional Semantic Trajectories and Predicting Future Semantic Locations

Karatzoglou

2020

Lecture Notes in Computer Science

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Current location-aware systems rely increasingly on location prediction techniques in order to provide their services in a timely fashion. At the same time, it has been shown that the use of additional context information, that is, elevating the degree of semantic enrichment of movement data, can lead to significant better results both in analyzing as well as in modeling human trajectories and predicting upon them. In this work, we propose a Multi-Channel Convolutional Neural Network (CNN) based approach for capturing all the available context dimensions in our semantic trajectory dataset aiming at achieving a higher prediction accuracy compared to a vanilla locations-only Single-Channel CNN. Moreover, we investigate whether and to what degree time, activity, companionship and the user's emotional state have an impact on the predictive performance of our multi-dimensional CNN. We evaluate our model using a real-world dataset and compare it among others to a probabilistic Markov Chain model and a vanilla CNN at two semantic representation levels. It can be shown that especially for higher level representations, the present approach is able to outperform the baseline models achieving an overall higher accuracy and F1-Score. Keywords: Semantic locations and trajectories • Location prediction • Purpose of visit • Human activities • Emotional states • Context awareness • Multi-channel convolutional neural networks

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Purpose-of-Visit-Driven Semantic Similarity Analysis on Semantic Trajectories for Enhancing The Future Location Prediction

Cited by 10 publications

References 9 publications

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

From GPS to semantic data: how and why—a framework for enriching smartphone trajectories

Multi-channel Convolutional Neural Networks for Handling Multi-dimensional Semantic Trajectories and Predicting Future Semantic Locations

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