@InProceedings{10.1007/978-3-032-28466-2_14,
author="Tasnim, Humayra
and Dutta, Soumya
and Moses, Melanie",
editor="Arabnia, Hamid R.
and Deligiannidis, Leonidas
and Shenavarmasouleh, Farzan
and Ghareh Mohammadi, Farid
and Amirian, Soheyla",
title="Dynamic Spatio-Temporal Data Summarization Using Information Based Fusion",
booktitle="Emerging Trends in Computational Science, Computational Biology, Parallel {\&} Distributed Computing, and Internet of Things",
year="2027",
publisher="Springer Nature Switzerland",
address="Cham",
pages="180--198",
abstract="In the era of burgeoning data generation, managing large-scale datasets poses significant challenges. With the rise of computing, the volume of data produced has soared, intensifying storage and I/O overheads. To address this issue, we propose a data summarization technique that identifies informative features in key timesteps and fuses less informative ones. This approach minimizes storage requirements while preserving data features. Unlike existing methods, our method retains both raw and summarized timesteps. We utilize information-theoretic measures to devise the fusion process, visually representing underlying data patterns. We demonstrate the versatility of proposed technique across datasets from diverse application domains.",
isbn="978-3-032-28466-2"
}