ICSE 2024 (series) / CAIN 2024 (series) / Doctoral Symposium /
Optimizing Data Analytics Workflows through User-driven Experimentation
Mon 15 Apr 2024 11:12 - 11:18 at Pequeno Auditório - Doctoral Symposium and Energy-Aware AI Engineering Chair(s): Justus Bogner, Silverio Martínez-Fernández
In the Big Data era, efficient data analytics workflows are imperative to extract useful and meaningful insights. Data analysts and scientists spend an inordinate amount of time finding the best workflow via trial and error to get accurate and meaningful results that meet their expectations. We propose an Experimentation Engine that selects and optimizes the best workflow variant through continuous experimentation and having the end user in the loop. Experimentation Engine saves time in finding the workflow that satisfies the user requirements and provides accurate, useful and trustworthy results.
Mon 15 AprDisplayed time zone: Lisbon change
Mon 15 Apr
Displayed time zone: Lisbon change
11:00 - 12:30 | Doctoral Symposium and Energy-Aware AI EngineeringDoctoral Symposium / Research and Experience Papers at Pequeno Auditório Chair(s): Justus Bogner Vrije Universiteit Amsterdam, Silverio Martínez-Fernández UPC-BarcelonaTech | ||
11:00 6mTalk | Software Design Decisions for Greener Machine Learning-based Systems Doctoral Symposium Santiago del Rey Universitat Politècnica de Catalunya (UPC) | ||
11:06 6mTalk | Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach Doctoral Symposium | ||
11:12 6mTalk | Optimizing Data Analytics Workflows through User-driven Experimentation Doctoral Symposium Keerthiga Rajenthiram Vrije Universiteit Amsterdam | ||
11:18 6mTalk | Component-based Approach to Software Engineering of Machine Learning-enabled Systems Doctoral Symposium Vladislav Indykov Chalmers | University of Gothenburg | ||
11:24 6mTalk | Threat Modeling of ML-intensive Systems: Research Proposal Doctoral Symposium Felix Viktor Jedrzejewski Blekinge Institute of Technology | ||
11:30 6mTalk | Continuous Quality Assurance ML Pipelines under the AI Act Doctoral Symposium Matthias Wagner Lund University | ||
11:36 10mTalk | Green Runner: A tool for efficient deep learning component selection Research and Experience Papers Jai Kannan Applied Artificial Intelligence Institute, Deakin University, Scott Barnett Applied Artificial Intelligence Institute, Deakin University, Anj Simmons , Taylan Selvi Applied Artificial Intelligence Institute, Deakin University, Luís Cruz Delft University of Technology | ||
11:46 15mTalk | Engineering Carbon Emission-aware Machine Learning Pipelines Research and Experience Papers | ||
12:01 10mTalk | Identifying architectural design decisions for achieving green ML serving Research and Experience Papers Francisco Durán Universitat Politècnica De Catalunya - Barcelona Tech, Silverio Martínez-Fernández UPC-BarcelonaTech, Matias Martinez Universitat Politècnica de Catalunya (UPC), Patricia Lago Vrije Universiteit Amsterdam Pre-print | ||
12:11 10mTalk | Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures Research and Experience Papers Negar Alizadeh Universiteit Utrecht, Fernando Castor University of Twente and Federal University of Pernambuco | ||
12:21 9mLive Q&A | Energy: Q&A Session Research and Experience Papers |