All Outputs (2)

Collaborative Streaming: Trust Requirements for Price Sharing (2019)
Presentation / Conference Contribution
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.

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.