Conference Information

DaMoN 2026: International Workshop on Data Management on New Hardware

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DaMoN
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Submission Date:
2026-02-20 Extended
Notification Date:
2026-03-30
Conference Date:
2026-06-01
Location:
Bengaluru, India
Years:
22
ICORE: C   Viewed: 722   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

53.3 / 100
Ranked #1,027 of 5,687 conferences · Top 19%

#53 of 337 in Data Mining & Databases

Academic recognition (35%)
58
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
84
Community attention (10%)
8
Public record completeness (15%)
35

Inputs used: Listed as ICORE C · Editions on record: 22 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-27

Call For Papers

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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