Información de la conferencia

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

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Día de Entrega:
2025-09-15 Extended
Fecha de Notificación:
2025-10-21
Fecha de conferencia:
2025-12-03
Ubicación:
Chengdu, China
Ediciones:
17
Vistas: 17815   Seguidores: 2   Asistentes: 1

Índice Conference Partner (CP-I)

50,2 / 100
Puesto n.º 1.528 de 5.682 congresos · 27% superior
Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
78
Atención de la comunidad (10%)
19
Integridad del registro público (15%)
35

Datos utilizados: Ediciones documentadas: 17 · Investigadores que lo siguen aquí: 2 · Investigadores que abrieron esta página en los últimos 24 meses: 2

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Confianza 45 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-19

Solicitud de Artículos

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
Última actualización por Dou Sun el

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