Abstract
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 user in the loop. Experimentation Engine saves time finding the workflow that satisfies the user requirements and provides accurate, useful and trustworthy results.
| Original language | English |
|---|---|
| Title of host publication | CAIN 2024 |
| Subtitle of host publication | Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 253-255 |
| Number of pages | 3 |
| ISBN (Electronic) | 9798400705915 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 3rd International Conference on AI Engineering, CAIN 2024, co-located with the 46th International Conference on Software Engineering, ICSE 2024 - Lisbon, Portugal Duration: 14 Apr 2024 → 15 Apr 2024 |
Conference
| Conference | 3rd International Conference on AI Engineering, CAIN 2024, co-located with the 46th International Conference on Software Engineering, ICSE 2024 |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 14/04/24 → 15/04/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Funding
| Funders | Funder number |
|---|---|
| ExtremeXP | |
| European Commission | 101093164 |
| European Union Horizon Programme | 101093164 |
Keywords
- data analytics
- experimentation
- human in the loop
- optimization
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