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DaMoN 2026: International Workshop on Data Management on New Hardware

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截稿日期:
2026-02-20 Extended
通知日期:
2026-03-30
会议日期:
2026-06-01
会议地点:
Bengaluru, India
届数:
22
ICORE: C   浏览: 726   关注: 0   参加: 0

会伴指数 (CP-I)

53.3 / 100
全站第 1,027 名 / 共 5,687 个会议 · 前 19%

数据挖掘与数据库 第 53 / 337

学术认可 (35%)
58
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
84
社区关注 (10%)
8
资料公开度 (15%)
35

用到的输入: 收录等级：ICORE C · 有据可查的届次：22 · 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 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 2026-09-27

征稿

DaMoN 2026 (International Workshop on Data Management on New Hardware) is a ICORE C conference held in Bengaluru, India on 2026-06-01. The paper submission deadline is 2026-02-20 (extended). Acceptance notifications are sent on 2026-03-30.

This one-day workshop aims to bring together researchers interested in optimizing database performance on modern computing infrastructure by designing new data management techniques and tools. Topics of Interest The continued evolution of computing hardware and infrastructure imposes new challenges and bottlenecks to program performance. As a result, traditional database architectures fail to utilize hardware resources efficiently. Multi-core CPUs, various accelerators (GPUs, FPGAs, etc.), as well as new memory and storage technologies and interconnects provide great opportunities for optimizing database performance. Consequently, exploiting the characteristics of modern hardware has become an essential topic of database systems research. The goal is to make database systems adapt automatically to sophisticated hardware characteristics, thus maximizing performance transparently for applications. To achieve this goal, the data management community needs interdisciplinary collaboration with researchers from computer architecture, compilers, operating systems, and storage. This involves rethinking traditional data structures, query processing algorithms, and database software architectures to adapt to the advances in the underlying hardware infrastructure. We seek submissions bridging database systems to computer architecture, compilers, and operating systems. We also invite submissions for papers on hardware/software co-design for modern data-intensive workloads (including, but not limited to machine learning training and inference, graph analytics, and similar tasks). As these workloads continue to grow in scale and complexity, innovative co-design approaches that tightly integrate hardware architectures and software systems are crucial to achieving breakthroughs in performance, energy efficiency, and scalability. In particular, submissions covering topics from the following non-exclusive list are encouraged: database algorithms and data structures on modern hardware cost models and query optimization for novel hierarchical memory systems hardware systems for query processing data management using co-processors novel application of new storage technologies to data management query processing using computing power in network/memory/storage systems database architectures for low-power computing and embedded devices database architectures on modern tile-based hardware performance analysis of database workloads on modern hardware compiler and operating systems advances to improve database performance new benchmarks for micro-architectural evaluation of database workloads taking advantage of modern network/storage/memory capabilities for data processing hardware/software co-design for modern data-intensive workloads
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