Dissertation (December 18, 2025): Michel Siqueira Reis
Student: Michel Siqueira Reis
Title: Matching Detections to Events in Time Series with Computational Efficiency and Guaranteed Optimality
Advisors: Rafaelli Coutinho (Advisor) and Eduardo Ogasawara (Co-advisor)
Committee: Rafaelli Coutinho (Cefet/RJ), Eduardo Ogasawara (Cefet/RJ), Laura Assis (Cefet/RJ) and Rebecca Salles (INRIA)
Day/Hour: December 18, 2025 / 1 p.m.
Abstract: This work presents SmartSoftED, an optimized metric for evaluating the detection of point events in time series. The original metric, SoftED, introduces a “soft” evaluation based on temporal tolerance, assigning gradual scores to detections that occur near actual events. However, its current formulation relies on a greedy approach that does not guarantee optimality in all cases and incurs a quadratic computational cost, limiting its applicability in large-scale or real-time processing environments. SmartSoftED overcomes these limitations by introducing a strategy that decomposes the problem into manageable, disjoint subproblems: some can be solved efficiently without loss of optimality, while others are modeled as maximum-weighted matching problems on unbalanced bipartite graphs. This approach preserves the optimality of correspondences between detections and events while significantly reducing computational cost. In practice, the method achieves an average speedup of two orders of magnitude, making it suitable for certain large-scale applications and systems with strict temporal constraints.