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TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications

Summary: TRACER enables accurate, interpretable high-stakes analytics with TITV, separating time-invariant/time-variant feature importance. Self-attention and feature-wise transform yield patient- and feature-level explanations; validated on real hospital data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5935
Venue
SIGMOD
Year
2020
Pagerank
4.1945683e-05
Overall Rank
11,594 | 19.35%
DOI
10.1145/3318464.3389720

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
557 SystemML: Declarative Machine Learning on Spark 2016 VLDB 0.00020197988
761 Materialization Optimizations for Feature Selection Workloads 2014 SIGMOD 0.00017053783
2,825 Smile: A System to Support Machine Learning on EEG Data at Scale 2019 VLDB 8.0563426e-05
3,753 Choosing A Cloud DBMS: Architectures and Tradeoffs 2019 VLDB 6.7871241e-05
5,830 GEMINI: An Integrative Healthcare Analytics System 2014 VLDB 5.3113542e-05
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