日本データベース学会

dbjapanメーリングリストアーカイブ(2020年)

[dbjapan] CFP: Distributed Infrastructure, Systems, Programming and AI (In conjunction with VLDB 2020)

  • To: dbjapan [at] dbsj.org
  • Subject: [dbjapan] CFP: Distributed Infrastructure, Systems, Programming and AI (In conjunction with VLDB 2020)
  • From: ISHIZAKI Kazuaki <kiszk [at] acm.org>
  • Date: Tue, 28 Apr 2020 10:37:28 +0900

日本データベース学会の皆様

日本IBMの石崎です

国際会議 VLDB2020 の併設ワークショップ
Distributed Infrastructure, Systems, Programming and AI (DISPA)
のCFPです。分散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ます。

詳細は下記のようになっており、日本時間の6/7が投稿締め切りです。
枚数は6ページと、気軽に投稿できるよう抑えています。
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。
ぜひとも、ご投稿をご検討いただけますようよろしくお願いいたします。


Workshop site: https://sites.google.com/site/dispa2020
Submission deadline   Saturday June 6th, 2020, 23:59 AoE
Notification of Acceptance: Friday, 3rd 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 will use EasyChair system to handle all
submissions.

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

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