Información de la conferencia

COMAD 2024: International Conference on Management of Data

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COMAD
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Día de Entrega:
2024-07-24 Extended
Fecha de Notificación:
2024-09-07
Fecha de conferencia:
2024-12-18
Ubicación:
IIT Jodhpur, India
Ediciones:
30
Vistas: 25759   Seguidores: 2   Asistentes: 0

Índice Conference Partner (CP-I)

53,1 / 100
Puesto n.º 993 de 5.647 congresos · 18% superior

N.º 52 de 336 en Minería de datos y bases de datos

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%)
92
Atención de la comunidad (10%)
19
Integridad del registro público (15%)
35

Datos utilizados: Ediciones documentadas: 30 · 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 2026-09-03

Solicitud de Artículos

COMAD 2024 (International Conference on Management of Data) is an academic conference held in IIT Jodhpur, India on 2024-12-18. The paper submission deadline is 2024-07-24 (extended). Acceptance notifications are sent on 2024-09-07.

Topics of Interest include, but are not limited to, the following: AI, ML, and Data Mining: Classification and regression; Knowledge discovery; knowledge representation and knowledge-based systems; data preprocessing and wrangling; feature engineering; reinforcement learning; deep learning; Bayesian methods; time series analysis; optimization; graphical models; statistical relational learning; matrix and tensor methods; parallel and distributed learning; semi- and unsupervised learning; graph mining; network analytics; text analytics and NLP; information retrieval; learning-based computer vision; multimodal learning and analytics; human-in-the-loop learning; planning and reasoning; ML for mobiles and other resource-constrained environments; federated learning; AutoML; causality; weak supervision and data augmentation; new benchmark tasks and datasets for AI/ML/data mining. Data Management: Data management systems (subtopics including but not limited to benchmarking, monitoring, testing, and tuning database systems, cloud, distributed, decentralized and parallel data management, database systems on emerging hardware, embedded databases, IoT and Sensor networks, Storage, indexing, and physical database design, Query processing and optimization, Transaction processing, Data warehousing, OLAP, Analytics); Models and Languages (subtopics including but not limited to Data models and semantics, Declarative programming languages and optimization, Spatial and temporal data management, Graphs, social networks, web data, and semantic web, Multimedia and information retrieval, Uncertain, probabilistic, and approximate databases, Streams and complex event processing); Human-Centric Data Management (subtopics including but not limited to Data exploration, visualization, query languages, and user interfaces, Crowdsourced and collaborative data management, User-centric and human-in-the-loop data management, Natural language processing for databases); Data Governance (subtopics including but not limited to Data provenance and workflows, Data integration, information extraction, and schema matching, Data quality, data cleaning, Data security, privacy, and access control, Responsible data management and data fairness, Metadata Management). Intersection of AI & Data Management: Structured queries over unstructured data: images, video, natural language, natural language queries; machine learning methods for database engine internals; machine learning methods for database tuning; data management and metadata for machine learning pipelines; knowledge base management. Data Science Ethics: Subtopics including, but not limited to quantifying and mitigating fairness and bias issues; improving model trust, transparency and explainability; data privacy; model alignment; environmental costs; governance and regulation.
Última actualización por Dou Sun el

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