PANDA22: The First International Workshop on Performance-Data Analytics and Data-Management International Conference on Performance Engineering Beijing (Virtual), China, April 9, 2022 |
Conference website | https://panda-workshop.github.io/ |
Submission link | https://easychair.org/conferences/?conf=panda22 |
Abstract registration deadline | January 31, 2022 |
Submission deadline | January 31, 2022 |
Workshop on Performance-Data Analytics and Data-Management (PANDA)
The field of data analytics/science has grown significantly in recent years as a means to make sense of the vast amount of available data. It has permeated every aspect of computer science and engineering and is heavily involved in business decision-making. In the field of performance engineering, data analytics is for instance used for performance prediction and hence as a baseline instrument for controlling and improving the behavior of a system.
Efficient and successful performance engineering depends on the definition of meaningful experiments, the choice of correct evaluation methodologies, the use of the correct algorithms, and also includes sophisticated data management. Only the combination of all these aspects leads to high reproducibility and reliable results.
This workshop seeks to bring together researchers and practitioners from the domains of performance engineering, data analytics, data management, and system automation to foster the growth of an active community to advance the methods for automated performance engineering. We further seek to extend the number of available Open Access data sets of systems performance data.
Topics of interest for the workshop include, but are not limited to:
- Streamlining the data science and data management process (DataOps)
- Methods for assessing and ensuring the quality of benchmarking/performance measurements data
- Approaches for Cross-layer (from hardware to software) performance analytics
- Advanced Data Management Strategies
- Performance Forecasting and Anomaly Detection
- Methods for Data / Time Series Imputation
- Data Analytics Methods for Performance Benchmarking
- From benchmarking data to recommendations