Outputs (4)

DeepTEA: effective and efficient online time-dependent trajectory outlier detection (2022)
Journal Article
Han, X., Cheng, R., Ma, C., & Grubenmann, T. (2022). DeepTEA: effective and efficient online time-dependent trajectory outlier detection. Proceedings of the VLDB Endowment, 15(7), 1493-1505. https://doi.org/10.14778/3523210.3523225

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to... Read More about DeepTEA: effective and efficient online time-dependent trajectory outlier detection.

Spatial concept learning and inference on geospatial polygon data (2022)
Journal Article
Westphal, P., Grubenmann, T., Collarana, D., Bin, S., Bühmann, L., & Lehmann, J. (2022). Spatial concept learning and inference on geospatial polygon data. Knowledge-Based Systems, 241, Article 108233. https://doi.org/10.1016/j.knosys.2022.108233

Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data... Read More about Spatial concept learning and inference on geospatial polygon data.

A framework for differentially-private knowledge graph embeddings (2021)
Journal Article
Han, X., Dell’Aglio, D., Grubenmann, T., Cheng, R., & Bernstein, A. (2022). A framework for differentially-private knowledge graph embeddings. Journal of Web Semantics, 72, Article 100696. https://doi.org/10.1016/j.websem.2021.100696

Knowledge graph (KG) embedding methods are at the basis of many KG-based data mining tasks, such as link prediction and node clustering. However, graphs may contain confidential information about people or organizations, which may be leaked via embed... Read More about A framework for differentially-private knowledge graph embeddings.

LINC: a motif counting algorithm for uncertain graphs (2019)
Journal Article
Ma, C., Cheng, R., Lakshmanan, L. V. S., Grubenmann, T., Fang, Y., & Li, X. (2019). LINC: a motif counting algorithm for uncertain graphs. Proceedings of the VLDB Endowment, 13(2), 155-168. https://doi.org/10.14778/3364324.3364330

In graph applications (e.g., biological and social networks), various analytics tasks (e.g., clustering and community search) are carried out to extract insight from large and complex graphs. Central to these tasks is the counting of the number of mo... Read More about LINC: a motif counting algorithm for uncertain graphs.