[dbjapan] 【6/20投稿締切延長】CFP: Distributed Infrastructure, Systems, Programming and AI (DISPA2020)

[ISHIZAKI Kazuaki <kiszk [at] acm.org>]

日本データベース学会の皆様

日本IBMの石崎です。
重複受信の場合にはご容赦ください。

投稿締切を6/20まで2週間延長しましたので、国際会議 VLDB2020 の併設ワークショップ
Distributed Infrastructure, Systems, Programming and AI (DISPA)のCFPを再投稿いたします。
最長6ページで、今から書いても間に合います。ぜひ投稿をご検討ください。

Keynoteは、PFNの秋葉様、Wisconsin-Madison大のProf. Venkataraman、に行っていただけることとなりました。

分散環境での、DB、インフラ、AI、プログラミング、またはそれらを
横断する、幅広いトピックに関する研究の投稿を募集しています。

詳細は下記のようになっております。
アカデミアのみならずインダストリーからの投稿も絶賛募集しております。
新たにはじまった Scalable Data Science Track(https://wp.sigmod.org/?p=3033)
投稿のための、最初のステップとしてもご考慮いただけると幸いです。


Workshop site: https://sites.google.com/site/dispa2020
Submission deadline   Saturday June 20th, 2020, 23:59 AoE
Notification of Acceptance: Friday, 17rd July, 2020, 23:59 AoE

* Overview
The goal of the Distributed Infrastructure Systems, Programming and AI (DISPA) workshop is to bring together researchers and practitioners involved with distributed systems for databases, programmings, and machine learning. Distributed processing has become widespread due to the trend of increasing data volumes, but these research communities remain fairly disjoint. Moreover, industry trends such as public cloud computing are further changing the design of distributed systems, favoring highly elastic and multi-tenant system designs, but there are limited exposures to these challenges in research. We aim to enable an exchange of ideas among researchers and practitioners in this field where we will lead to novel distributed system designs.
There have been many exciting changes in the distributed system fields in recent years, including
- the wide adoption of new open source programming tools for massively distributed computation (e.g. PyTorch and Apache Spark)
- the rise of public cloud computing, “cloud-native” data management and computation systems such as BigQuery and AWS Aurora
- the use of machine learning to manage and optimize distributed systems
- new algorithmic advances in deep learning, approximate query processing, information retrieval, and other areas.
This workshop also provides good opportunities to discuss your work to be submitted to Scalable Data Science track at PVLDB.
For any questions regarding the DISPA workshop, please send email to dispa2020-q [at] googlegroups.com.

* Topics of Interested
The DISPA workshop aims to bring together researchers and practitioners designing distributed systems for databases, programming frameworks, and machine learning. The suggested topics of interest for the DISPA workshop include, but are not limited to:
- Distributed data management and query processing
- Distributed machine learning
- Distributed systems for information retrieval
- Cloud computing and “cloud-native” system designs
- Programming models for distributed computing
- Code and runtime optimizations for distributed applications
- Machine learning for managing and optimizing distributed systems
- Management and operation of distributed systems
- Approximate algorithms for query processing and machine learning at scale
- Workload characterization and performance evaluation
- Experience with production systems operating at massive scale
- New use cases for distributed computing

* Submission Guidelines
The DISPA workshop follows the VLDB formatting guideline and the submission guideline. We use CMT system to handle all submissions at https://cmt3.research.microsoft.com/DISPA2020.
Unlike the VLDB submission guideline, the paper length for a paper is up to 6 pages, excluding references.  The accepted paper may have up to four extra pages if authors want, excluding reference.
At least one author of every accepted paper is expected to attend the workshop and give an oral presentation about the paper.

* Workshop Co-Chairs
- Kazuaki Ishizaki, IBM Research - Tokyo
- Barzan Mozafari, University Of Michigan, Ann Arbor
- Matei Zaharia, Stanford University & Databricks

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Kazuaki Ishizaki, IBM Research - Tokyo
Mail : kiszk [at] acm.org