Dynamic Spatio-Temporal Summarization using Information Based Fusion
Humayra Tasnim Soumya Dutta Melanie Moses
In the era of burgeoning data generation, managing and storing large-scale
time-varying datasets poses significant challenges. With the rise of
supercomputing capabilities, the volume of data produced has soared,
intensifying storage and I/O overheads. To address this issue, we propose a
dynamic spatio-temporal data summarization technique that identifies
informative features in key timesteps and fuses less informative ones. This
approach minimizes storage requirements while preserving data dynamics. Unlike
existing methods, our method retains both raw and summarized timesteps,
ensuring a comprehensive view of information changes over time. We utilize
information-theoretic measures to guide the fusion process, resulting in a
visual representation that captures essential data patterns. We demonstrate the
versatility of our technique across diverse datasets, encompassing
particle-based flow simulations, security and surveillance applications, and
biological cell interactions within the immune system. Our research
significantly contributes to the realm of data management, introducing enhanced
efficiency and deeper insights across diverse multidisciplinary domains. We
provide a streamlined approach for handling massive datasets that can be
applied to in situ analysis as well as post hoc analysis. This not only
addresses the escalating challenges of data storage and I/O overheads but also
unlocks the potential for informed decision-making. Our method empowers
researchers and experts to explore essential temporal dynamics while minimizing
storage requirements, thereby fostering a more effective and intuitive
understanding of complex data behaviors.