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

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Bench
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투고 마감일:
2025-09-15 Extended
통보일:
2025-10-21
개최일:
2025-12-03
개최지:
Chengdu, China
개최 횟수:
17
조회: 17817   팔로우: 2   참가: 1

회반 지수 (CP-I)

50.2 / 100
전체 5,682개 중 1,528위 · 상위 27%
학술적 인정 (35%) 데이터 없음 — 중립 기준값 50점으로 계산 —
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%)
78
커뮤니티 관심도 (10%)
19
공개 자료 충실도 (15%)
35

사용한 입력: 확인되는 개최 횟수: 17 · 회반에서 팔로우 중인 연구자: 2명 · 지난 24개월 동안 이 페이지를 연 연구자: 2명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 45% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-19

논문 모집

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
최종 수정: Dou Sun ()

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CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

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