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Life & fun · February 21, 2025

I presented a novel efficient streaming algorithm

This paper has been accepted to KDD2025.

I presented a novel efficient streaming algorithm launched on February 21, 2025 with 12 votes, #1,365 of 5,999 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.

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In plain words

This is a research paper on streaming algorithms that has been accepted to KDD2025, a major conference in data science and machine learning. The work focuses on developing an efficient approach to processing data streams. The paper is available on GitHub, making it accessible to researchers and practitioners interested in streaming computation methods.

written from the facts on this page · September 2026

From the sources

[KDD2025] Modeling Time-evolving Causality over Data Streams. https://dl.acm.org/doi/10.1145/3690624.3709283 - C-Naoki/ModePlait

Implementation of "Modeling Time-Evolving Causality over Data Streams," Naoki Chihara, Yasuko Matsubara, Ren Fujiwara, and Yasushi Sakurai. The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD2025 . We focus on causal relationships that evolve over time in data streams and refer such relationships as "time-evolving causality." We presented ModePlait, which aims to discover time-evolving causalities in multivariate co-evolving data streams, and forecast future values in a stream fashion simultaneously. The overview of our proposed model is following: The following preview of our results shows the effectiveness of ModePlait over an epidemiological data stream. We would…from github.com

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