Specification of Complex Analytics Workflows

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Specification of Complex Analytics Workflows

A Formal Language Model of Decision Options

Conference Paper (2025)

Author(s)

Pouriya Miri (University of Ljubljana)

Petar Kochovski (University of Ljubljana)

M. Oliveira (TU Delft - Information and Communication Technology)

Vlado Stankovski (University of Ljubljana)

Research Group

Information and Communication Technology

DOI related publication

https://doi.org/10.1007/978-3-031-81226-2_26

Markov Decision Process Complex Analytic Workflow Semantic model

To reference this document use:

https://resolver.tudelft.nl/uuid:91f0c3d6-bc3d-4dbf-b6df-dd5e79dcd3ab

More Info

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Publication Year

2025

Language

English

Research Group

Information and Communication Technology

Bibliographical Note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en

Pages (from-to)

294-299

ISBN (print)

978-3-031-81225-5

ISBN (electronic)

978-3-031-81226-2

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines.

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