During the last forty years, data management systems have grown in scale, complexity, and number of
installations. At the same time, administration of these systems has become very expensive with the
human factor dominating the total cost of ownership. Current trends like cloud computing make this
situation even more problematic for service providers who have to configure and manage thousands of
database nodes.
There has been a significant amount of research addressing this problem by providing autonomic or
self-* features in database systems to support complex administrative tasks like physical database
design, problem diagnosis, and performance tuning. However, new challenges arise from trends like
cloud and cluster computing, virtualization, and Software-as-a-Service (SaaS). A major challenge is
the need to scale self-management capabilities to the level of hundreds to thousands of nodes while
taking economic factors into account.
Autonomic, or self-managing, systems are a promising approach to achieve the goal of systems that
are easier to use and maintain. A system is considered to be autonomic if it possesses the
capabilities to be self-configuring, self-optimizing, self-healing and self-protecting. The aim of
the SMDB workshop is to provide a forum for researchers from both industry and academia to present
and discuss ideas related to self-management and self-organization in data management systems
ranging from classical databases to data stream engines to large-scale cloud environments that
utilize advanced AI, machine learning, and data mining and analysis.
We plan to follow the successful format of previous instances of this workshop: approximately 10
presentations of accepted papers, a keynote address by a well-known speaker and subject matter
expert in self-managing database systems, as well as a panel discussion involving experts from
industry and academia.
Times are displayed in PDT and UTC. Look up your local times: https://time.is/. | |||||||
PDT |
UTC |
Session Chair |
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7:00 |
14:00 |
Herodotos Herodotou |
Session 1 |
Opening and Introductions General Chairs: Panos K. Chrysanthis & Meichun Hsu |
10 min |
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Research Talk 1 |
Performance Models of Data Parallel DAG Workflows for Large Scale Data Analytics Juwei Shi (Microsoft)*; Jiaheng Lu (University of Helsinki) |
25 min |
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Research Talk 2 |
Adaptive Query Compilation in Graph Databases Alexander Baumstark (TU Ilmenau)*; Muhammad Attahir Jibril (TU Ilmenau); Kai-Uwe Sattler (TU Ilmenau) |
25 min |
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8:00 |
15:00 |
Break |
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8:10 |
15:10 |
Yingjun |
Session 2 |
Keynote 1 |
AI's Enormous Potential for Database Simplification Sam Lightstone, CTO AI Strategy, IBM Data and AI |
45 min |
|
Keynote 2 |
OtterTune: An Automatic Database Configuration Tuning Service Andy Pavlo, Associate Professor at Carnegie Mellon University, co-founder of OtterTune |
45 min |
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9:40 |
16:40 |
Break |
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9:50 |
16:50 |
Stefan Manegold |
Session 3 |
Keynote 3 |
Automatic Data Management and Storage Tiering with Oracle Database In-Memory Shasank Chavan, VP of Data and In-Memory Database Technologies at Oracle |
45 min |
|
Keynote 4 |
Architectural evolution of Amazon Redshift and its practical usage of Machine Learning. Ippokratis Pandis, Senior Principal Engineer at Amazon Web Services |
45 min |
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11:20 |
18:20 |
Break |
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11:30 |
18:30 |
Constantinos Costa |
Session 4 |
Research Talk 3 |
Improving Stream Load Balance through Shedding Nikos Katsipoulakis (Amazon Web Services)*; Alexandros Labrinidis (University of Pittsburgh); Panos Chrysanthis (University of Pittsburgh) |
25 min |
|
Research Talk 4 |
Towards a Benchmark for Learned Systems Laurent Bindschaedler (MIT)*; Andreas Kipf (MIT); Tim Kraska (MIT); Ryan Marcus (MIT); Umar Farooq Minhas (Microsoft Research) |
25 min |
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Closing General Chairs: Panos K. Chrysanthis & Meichun Hsu |
10 min |
Sam Lightstone is IBM Chief Technology Officer for AI, IBM Fellow and a Master Inventor in the IBM Data and AI group. He is also chair of the Data and AI Technical Team, the working group of IBM’s technical executives in the division. He has been the founder and co-founder of several large-scale initiatives including AI databases, next generation data warehousing, data virtualization, autonomic computing for data systems, serverless cloud SQL query, and cloud native database services. He co-founded the IEEE Data Engineering Workgroup on Self-Managing Database Systems. Sam has more than 65 patents issued and pending and has authored 4 books and over 30 papers. Sam’s books have been translated into Chinese, Japanese and Spanish. In his spare time he is an avid guitar player and fencer. His Twitter handle is "samlightstone".
Andy Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. His research interest is in database management systems, specifically main memory systems, self-driving / autonomous architectures, transaction processing systems, and large-scale data analytics. At CMU, he is a member of the Database Group and the Parallel Data Laboratory. He is also the co-founder and CEO of OtterTune