Outputs (19)

Astera, an Educational Game About the Evolution of Galaxies (2023)
Conference Proceeding
Grubenmann, T., & Shankar, F. (2024). Astera, an Educational Game About the Evolution of Galaxies. In Games and Learning Alliance 12th International Conference, GALA 2023, Dublin, Ireland, November 29 – December 1, 2023, Proceedings (402-407). https://doi.org/10.1007/978-3-031-49065-1_40

The Universe at its largest scales remains still almost a mystery for most of the people not working in this field. With Astera, we present an educational video game that can teach about the cosmos whilst providing a thrilling and fun gaming experien... Read More about Astera, an Educational Game About the Evolution of Galaxies.

Modeling Long-Range Travelling Times with Big Railway Data (2022)
Conference Proceeding
Sun, W., Grubenmann, T., Cheng, R., Kao, B., & Ching, W. (2022). Modeling Long-Range Travelling Times with Big Railway Data. In Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022 (443-454). https://doi.org/10.1007/978-3-031-00129-1_38

Big Railway Data, such as train movement logs and timetables, have become increasingly available. By analyzing these data, insights about train movement and delay can be extracted, allowing train operators to make smarter train management decisions.... Read More about Modeling Long-Range Travelling Times with Big Railway Data.

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.

Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks (2022)
Conference Proceeding
Han, X., Cheng, R., Grubenmann, T., Maniu, S., Ma, C., & Li, X. (2022). Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks. In A. Banerjee, Z. Zhou, E. E. Papalexakis, & M. Riondato (Eds.), Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (64-72). https://doi.org/10.1137/1.9781611977172.8

Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network wher... Read More about Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks.

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.

Geolog: Scalable Logic Programming on Spatial Data (2021)
Conference Proceeding
Grubenmann, T., & Lehmann, J. (2021). Geolog: Scalable Logic Programming on Spatial Data. In Proceedings ICLP 2021 (191-204). https://doi.org/10.4204/eptcs.345.34

Spatial data is ubiquitous in our data-driven society. The Logic Programming community has been investigating the use of spatial data in different settings. Despite the success of this research, the Geographic Information System (GIS) community has r... Read More about Geolog: Scalable Logic Programming on Spatial Data.

Traffic Incident Detection: A Trajectory-based Approach (2020)
Conference Proceeding
Han, X., Grubenmann, T., Cheng, R., Wong, S. C., Li, X., & Sun, W. (2020). Traffic Incident Detection: A Trajectory-based Approach. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). https://doi.org/10.1109/icde48307.2020.00190

Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on... Read More about Traffic Incident Detection: A Trajectory-based Approach.

TSA: A Truthful Mechanism for Social Advertising (2020)
Conference Proceeding
Grubenmann, T., Cheng, R. C., & Lakshmanan, L. V. (2020). TSA: A Truthful Mechanism for Social Advertising. In WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining (214-222). https://doi.org/10.1145/3336191.3371809

Social advertising exploits the interconnectivity of users in social networks to spread advertisement and generate user engagements. A lot of research has focused on how to select the best subset of users in a social network to maximize the number of... Read More about TSA: A Truthful Mechanism for Social Advertising.

Collaborative Streaming: Trust Requirements for Price Sharing (2019)
Conference Proceeding
Grubenmann, T., Dell'Aglio, D., & Bernstein, A. (2019). Collaborative Streaming: Trust Requirements for Price Sharing. In 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9005470

Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple differe... Read More about Collaborative Streaming: Trust Requirements for Price Sharing.