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DTSTAMP:20260402T024534Z
LOCATION:3008\, Level 3
DTSTART;TZID=America/Los_Angeles:20250624T164500
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UID:dac_DAC 2025_sess157_RESEARCH1307@linklings.com
SUMMARY:STREAM: Spatiotemporal Similarity-based Efficient Approximate Medi
 an with Tunable Granularity
DESCRIPTION:Fenfang Li and Huizhang Luo (Hunan University), Weichen Liu (N
 anyang Technological University), Anthony Chronopoulos (The University of 
 Texas at San Antonio), and Kenli Li and Chubo Liu (Hunan University)\n\nTh
 e median (MED) is a crucial statistic for measuring the central tendency. 
 However, exact MED computation remains costly, with even state-of-the-art 
 (SOTA) algorithms failing to meet (near) real-time processing demands. Whi
 le approximate MED algorithm has arisen as a promising candidate, existing
  approaches ignore the potential opportunity of spatiotemporal similarity 
 within the application and fail to provide application-specific trade-offs
  between execution time and accuracy. Our goal is to design an enhanced ap
 proximate MED algorithm STREAM, which is capable of exploiting the spatiot
 emporal similarity to achieve bucket reuse and establish a tunable-grained
  bucket mechanism to meet the accuracy of application-specific requirement
 s. Experimental results show that while maintaining nearly identical accur
 acy, STREAM outperforms the SOTA approximate methods DDSketch (up to 10x, 
 4.7x on average) and KLL (up to 71.2x, 10.1x on average).\n\nTopics: Syste
 ms\n\nTracks: SYS3: Embedded Software\n\nSession Chairs: Hoeseok Yang (San
 ta Clara University) and Younghyun Kim (Purdue University)\n\n
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