会议信息

IRI 2026: International Conference on Information Reuse & Integration for Data Science

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截稿日期:
2026-04-01
通知日期:
2026-05-15
会议日期:
2026-07-31
会议地点:
Seattle, Washington, USA
届数:
27
QUALIS: B2   浏览: 25735   关注: 6   参加: 0

会伴指数 (CP-I)

60.1 / 100
全站第 583 名 / 共 5,645 个会议 · 前 11%

数据挖掘与数据库 第 31 / 336

学术认可 (35%)
68
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
90
社区关注 (10%)
31
资料公开度 (15%)
35

用到的输入: 收录等级：QUALIS B2 · 有据可查的届次：27 · 在会伴关注它的研究者：6 人 · 过去 24 个月打开过本页的研究者：4 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 80% —— 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 2026-08-31

征稿

IRI 2026 (International Conference on Information Reuse & Integration for Data Science) is a QUALIS B2 conference held in Seattle, Washington, USA on 2026-07-31. The paper submission deadline is 2026-04-01. Acceptance notifications are sent on 2026-05-15.

The IRI conference showcases novel and contemporary research addressing all facets of Data and Information Integration and Reuse in today's era of AI and Data Science – including both Scientific Theory and Technology-based Applications. The IEEE IRI conference and its workshops serve as a forum for researchers and practitioners from academia, industry, and government to present, discuss, and exchange ideas that address real-world problems with real-world solutions. Theoretical and applied papers are both welcome. The conference program will feature a regular research paper track, an application/industry paper track, and a poster paper track, and include several plenary speeches from academia, industry, and government as well as specialized workshops. Best Paper Awards: IRI'26 will also select and present a Best Paper Award, a Best Student Paper Award, and a Best Poster Award. The conference includes, but is certainly not limited to: Information Integration Multimodal Information Fusion Information Science & Theory AI and Security Symbolic AI Hallucination Detection and Mitigation in AI Novel AI applications in Computer Vision, Computer Forensics, Security (fake/forgery detection), Robotics, Recommender Systems, and Biomedical areas, etc. Information Reuse Data Lifecycle Reuse Afterlives of data in AI/ML models Preventing model collapse via improved data lineage Knowledge Graphs (KGs) Ontologies, variable alignment, contextual metadata Cross-domain and multilingual metadata systems Reuse in LLM and AI Training Data Interoperability and Cross-Disciplinary Reuse Linking datasets from different domains or formats Tools for data transformation and harmonization Reusable Systems Acceleration of AI System Reuse and Reuse Efficiency Brain-Computer Interface: Applications and Algorithms Reusable AI for Edge and IoT Reusable AI in Industry Applications Software and Systems Reuse and Reusability Software Sustainability and Reuse Economics Software Maintenance and Life-Cycle Management Software Reliability, Robustness, and Dependability System Applications - Autonomous Vehicles, Business, Education, Engineering, Healthcare and Medical Informatics, the Internet of Things, Math, Military, Multimedia, NLP, Robotics, Science, Security, Social Networking, Space, Vision, etc.
由 Dou Sun 最后更新于

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