会议信息

Bench 2025: BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing

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嵌入截止倒计时徽章
Bench
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截稿日期:
2025-09-15 Extended
通知日期:
2025-10-21
会议日期:
2025-12-03
会议地点:
Chengdu, China
届数:
17
浏览: 17806   关注: 2   参加: 1

会伴指数 (CP-I)

50.2 / 100
全站第 1,528 名 / 共 5,682 个会议 · 前 27%
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
78
社区关注 (10%)
19
资料公开度 (15%)
35

用到的输入: 有据可查的届次：17 · 在会伴关注它的研究者：2 人 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-19

征稿

Bench 2025 (BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing) is an academic conference held in Chengdu, China on 2025-12-03. The paper submission deadline is 2025-09-15 (extended). Acceptance notifications are sent on 2025-10-21.

The Bench conference encompasses a wide range of topics in benchmarks, datasets, metrics, indexes, measurement, evaluation, optimization, supporting methods and tools, and other best practices in computer science, medicine, finance, education, management, etc. Bench's multidisciplinary and interdisciplinary emphasis provides an ideal environment for developers and researchers from different areas and communities to discuss practical and theoretical work. The topics of interest include, but are not limited to the following: Topics Evaluation theory and methodology Formal specification of evaluation requirements Development of evaluation models Design and implementation of evaluation systems Analysis of evaluation risk Cost modeling for evaluations Accuracy modeling for evaluations Evaluation traceability Identification and establishment of evaluation conditions Equivalent evaluation conditions Design of experiments Statistical analysis techniques for evaluations Methodologies and techniques for eliminating confounding factors in evaluations Analytical modeling techniques and validation of models Simulation and emulation-based modeling techniques and validation of models Development of methodologies, metrics, abstractions, and algorithms specifically tailored for evaluations The engineering of evaluation Benchmark design and implementation Benchmark traceability Establishing least equivalent evaluation conditions Index design, implementation Scale design, implementation Evaluation standard design and implementations Evaluation and benchmark practice Tools for evaluations Real-world evaluation systems Testbed Data set Explicit or implicit problem definition deduced from the data set Detailed descriptions of research or industry datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements Analyses or meta-analyses of existing data Systems, technologies, and techniques that advance data sharing and reuse to support reproducible research Tools that generate large-scale data while preserving their original characteristics Evaluating the rigor and quality of the experiments used to generate the data and the completeness of the data description Benchmarking Summary and review of state-of-the-art and state-of-the-practice Searching and summarizing industry best practice Evaluation and optimization of industry practice Retrospective of industry practice Characterizing and optimizing real-world applications and systems Evaluations of state-of-the-art solutions in the real-world setting Measurement and testing Workload characterization Instrumentation, sampling, tracing, and profiling of large-scale, real-world applications and systems Collection and analysis of measurement and testing data that yield new insights Measurement and testing-based modeling (e.g., workloads, scaling behavior, and assessment of performance bottlenecks) Methods and tools to monitor and visualize measurement and testing data Systems and algorithms that build on measurement and testing-based findings Reappraisal of previous empirical measurements and measurement-based conclusions Reappraisal of previous empirical testing and testing-based conclusions
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