会议信息

BDA 2020: International Conference on Big Data Analytics

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截稿日期:
2020-07-06 已截稿
通知日期:
2020-09-14
会议日期:
2020-12-15
会议地点:
Sonipat, India
届数:
8
浏览: 20434   关注: 2   参加: 1
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会伴指数 (CP-I)

45.0 / 100
全站第 2,967 名 / 共 5,759 个会议 · 前 52%

数据挖掘与数据库 第 176 / 339

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%)
59
社区关注 (10%)
19
资料公开度 (15%)
25

用到的输入: 有据可查的届次：8 · 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 1.2 · 算于 2026-10-23

征稿

BDA 2020 (International Conference on Big Data Analytics) is an academic conference held in Sonipat, India on 2020-12-15. The paper submission deadline is 2020-07-06. Acceptance notifications are sent on 2020-09-14.

The 8th International Conference on Big Data Analytics (BDA 2020) will be held during December 15-18, 2020 at Ashoka University, India. The conference will be organized by Ashoka University. BDA 2020 provides an international forum for researchers and industry practitioners to share their original research results, practical experiences and thoughts on big data from different perspectives including storage models, data access, computing paradigms, analytics, information sharing and privacy, redesigning mining algorithms, open issues and future research trends. BDA 2020 invites original, technically sound, high-quality research papers proposing novel solutions addressing the problems related to big data analytics as well as case studies and practical experiences with big data. Major topics of interest to the conference include, but are not limited to: Analytics as a Service Architectural Design for Big Data Big Data Analytics for Governance Conceptual/cognitive/programming Models for Big data analytics Clustering of Big Data Data Fusion and Multi Modal Analytics Data Models for Big Data Analyticse Domain-specific Analytics Index Structures for Big Data Analytics Interaction Design for Exploratory Analytics Machine Learning techniques for Big Data Large-scale recommendation systems and graph analysis Model Discovery from Big Data NoSQL and non-standard Data Models Physical Data Organization for Big Data Predictive Modelling Rule Mining from Big Data Scalability and Performance issues Security, privacy and legal issues specific to big data Semantics of Big Data Streaming Data Analytics Summarization and Materialized views Topic Modelling Unstructured and Semi-structured Data Mining
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