会議情報

PDSW 2026: International Parallel Data Systems Workshop

会議のウェブサイトを表示するにはログインしてください
無料登録で公式サイトの閲覧、締切のトラッキング、メールリマインダーが利用できます。
新規登録 ログイン
締切カウントダウンバッジを埋め込む
PDSW
このデータを API で取得
検索とランキング一覧は資格情報なしで利用できます。このページの詳細データには無料の API キーが必要です。詳しくは開発者向けガイドをご覧ください。
投稿締切日:
2026-07-31
通知日:
2026-09-04
開催日:
2026-11-15
開催地:
Chicago, Illinois, USA
開催回数:
11
閲覧: 861   フォロー: 0   参加: 0

会伴インデックス (CP-I)

47.3 / 100
全 5,693 件中 第 2,221 位 · 上位 39%

データマイニング・データベース 分野 337 件中 第 127 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
67
コミュニティの注目度 (10%)
12
公開情報の充実度 (15%)
35

使用した入力: 確認できる開催回数：11 · 過去 24 か月にこのページを開いた研究者：4 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-10-03

論文募集

PDSW 2026 (International Parallel Data Systems Workshop) is an academic conference held in Chicago, Illinois, USA on 2026-11-15. The paper submission deadline is 2026-07-31. Acceptance notifications are sent on 2026-09-04.

We are excited to announce the 11th International Parallel Data Systems Workshop (PDSW'26), to be held in conjunction with SC26: The International Conference for High Performance Computing, Networking, Storage, and Analysis, in Chicago, IL. PDSW'26 builds upon the rich legacy of its predecessor workshops, the Petascale Data Storage Workshop (PDSW, 2006–2015) and the Data Intensive Scalable Computing Systems (DISCS, 2012–2015) workshop. The increasing importance of efficient data storage and management continues to drive scientific productivity across traditional simulation-based HPC environments and emerging Cloud, AI/ML, and Big Data analysis frameworks. Challenges are compounded by the rapidly expanding volumes of experimental and observational data, the growing disparity between computational and storage hardware performance, and the rise of novel data-driven algorithms in machine learning. This workshop aims to advance research and development by addressing the most pressing challenges in large-scale data storage and processing. We invite the community to contribute original research manuscripts that introduce and evaluate novel algorithms or architectures, share significant scientific case studies or workloads, or assess the reproducibility of previously published work. We emphasize the importance of community collaboration for problem identification, workload capture, solution interoperability, standardization, and shared tools. Authors are encouraged to provide comprehensive experimental environment details (software versions, benchmark configurations, etc.) to promote transparency and facilitate collaborative progress. Topics of Interest: Scalable Architectures: Distributed data storage, archival, and virtualization. New Data Processing Models and Algorithms: Application of innovative data processing models and algorithms for parallel computing and analysis. Performance Analysis: Benchmarking, resource management, and workload studies. Cloud and Container-Based Models: Enabling cloud and container-based frameworks for large-scale data analysis. Storage Technologies: Adaptation to emerging hardware and computing models. Data Integrity: Techniques to ensure data integrity, availability, reliability, and fault tolerance. Programming Models and Frameworks: Big data solutions for data-intensive computing. Hybrid Cloud Data Processing: Integration of hybrid cloud and on-premise data processing. Cloud-Specific Opportunities: Data storage and transit opportunities specific to cloud computing. Storage System Programmability: Enhancing programmability in storage systems. Data Reduction Techniques: Filtering, compression, and reduction techniques for large-scale data. File and Metadata Management: Parallel file systems, metadata management at scale. In-Situ and In-Transit Processing: Integrating computation into the memory and storage hierarchy for in-situ and in-transit data processing. Alternative Storage Models: Object stores, key-value stores, and other data storage models. Productivity Tools: Tools for data-intensive computing, data mining, and knowledge discovery. Data Movement: Managing data movement between compute and data-intensive components. Cross-Cloud Data Management: Efficient data management across different cloud environments. AI-enhanced Systems: Storage system optimization and data analytics using machine learning. New Memory and Storage Systems: Innovative techniques and performance evaluation for new memory and storage systems. AI and Agentic related data management: tools and techniques necessary to support AI workloads and Agentic AI data analytics for online decision making.
最終更新：Admin Agent （）

関連会議

関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
International Journal of Parallel Programming0.9Springer0885-7458
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

コメント 0

まだコメントはありません。

コメントするにはログインしてください