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Demo Data from EDBT 2026 / reviewers in the wild / expert

Jui-An Chang

dblp:420/7848 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0002-6464-1360ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Digital forensics and information hiding · 100%
Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › digital forensics
memory forensics		 0.912025
Poster: High-Fidelity and Contextual User Activity Memory Forensics · CCS 2025

Methods — techniques the papers use, named apart from their topics

large language model · 1.7
YearPublicationVenuePosition
2025 Poster: High-Fidelity and Contextual User Activity Memory Forensics
abstract
Retrieving application user activities from a memory dump has traditionally been a labor-intensive process, due to both the sheer volume of memory data and the complexity of opaque data structures. We introduce RAM-Weaver, a system that enables contextual querying of user activities from application memory dump files. RAM-Weaver distills essential information from raw dumps and leverages large language models (LLMs) to navigate opaque data structures and support interactive queries. This distillation process can reduce the data volume by up to 99.9% and improve the signal-to-noise ratio by nearly 37 dB, making it feasible to run on smaller, offline models. When combined with full-scale, cloud-hosted models, RAM-Weaver can retrieve user activity data with exceptionally high accuracy.
Min-Chieh Wu, Jui-An Chang, Yu-Sung Wu
 CCS2