ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation

Keerthiga Rajenthiram; Milad Abdullah; Ilias Gerostathopoulos; Petr Hnetynka; Tomas Bures

doi:10.1109/ACSOS-C63493.2024.00058

ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation

Keerthiga Rajenthiram^*
, Milad Abdullah
, Ilias Gerostathopoulos^*
, Petr Hnetynka
, Tomas Bures

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

With the exponential growth of data analytics workflows, optimizing them has become crucial. In particular, there is a need to go beyond what the AutoML tools currently offer by considering different aspects of users (domain experts and data scientists) in the optimization process. Responding to this need, we propose a tool framework comprising an Experimentation Engine and a Domain-Specific Language. Together, they allow users to specify not only what needs to be optimized, but also how: in which steps, and with the desired degree of user involvement. The sources and demo video of our tool framework are available at https://github.com/ExtremeXP-VU/ExpEngine-ACSOS2024.

Original language	English
Title of host publication	2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
Subtitle of host publication	[Proceedings]
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	182-183
Number of pages	2
ISBN (Electronic)	9798350389760
ISBN (Print)	9798350389777
DOIs	https://doi.org/10.1109/ACSOS-C63493.2024.00058
Publication status	Published - 2024
Event	5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024 - Aarhus, Denmark Duration: 16 Sept 2024 → 20 Sept 2024

Conference

Conference	5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024
Country/Territory	Denmark
City	Aarhus
Period	16/09/24 → 20/09/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work is partially supported by the EU project ExtremeXP under grant agreement 101093164, and by the Charles University Grant Agency projects 408622 and 269723.

Funders	Funder number
European Commission	101093164
Grantová Agentura, Univerzita Karlova	408622, 269723

Keywords

adaptation
data analytics
experimentation
human in the loop
optimization

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10.1109/ACSOS-C63493.2024.00058

ExpEngine_A_Tool_for_Data_Analytics_Workflow_Optimization_Through_User-Driven_ExperimentationFinal published version, 517 KBLicence: Article 25fa Dutch Copyright Act

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Cite this

Rajenthiram, K., Abdullah, M., Gerostathopoulos, I., Hnetynka, P., & Bures, T. (2024). ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation. In 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C): [Proceedings] (pp. 182-183). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSOS-C63493.2024.00058

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doi = "10.1109/ACSOS-C63493.2024.00058",

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Rajenthiram, K, Abdullah, M, Gerostathopoulos, I, Hnetynka, P & Bures, T 2024, ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation. in 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C): [Proceedings]. Institute of Electrical and Electronics Engineers Inc., pp. 182-183, 5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024, Aarhus, Denmark, 16/09/24. https://doi.org/10.1109/ACSOS-C63493.2024.00058

ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation. / Rajenthiram, Keerthiga; Abdullah, Milad; Gerostathopoulos, Ilias et al.
2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C): [Proceedings]. Institute of Electrical and Electronics Engineers Inc., 2024. p. 182-183.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Bures, Tomas

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AB - With the exponential growth of data analytics workflows, optimizing them has become crucial. In particular, there is a need to go beyond what the AutoML tools currently offer by considering different aspects of users (domain experts and data scientists) in the optimization process. Responding to this need, we propose a tool framework comprising an Experimentation Engine and a Domain-Specific Language. Together, they allow users to specify not only what needs to be optimized, but also how: in which steps, and with the desired degree of user involvement. The sources and demo video of our tool framework are available at https://github.com/ExtremeXP-VU/ExpEngine-ACSOS2024.

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KW - experimentation

KW - human in the loop

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Rajenthiram K, Abdullah M, Gerostathopoulos I, Hnetynka P, Bures T. ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation. In 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C): [Proceedings]. Institute of Electrical and Electronics Engineers Inc. 2024. p. 182-183 Epub 2024 Dec 2. doi: 10.1109/ACSOS-C63493.2024.00058

ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation

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