Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

• URL: http://arxiv.org/abs/2307.04495v1
• Date: Mon, 10 Jul 2023 11:33:46 GMT
• Title: Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML
• Authors: Simon Raedler, Juergen Mangler, Stefanie Rinderle-Ma
• Abstract summary: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.

Related papers

• Extraction of Research Objectives, Machine Learning Model Names, and Dataset Names from Academic Papers and Analysis of Their Interrelationships Using LLM and Network Analysis [0.0]
  This study proposes a methodology extracting tasks, machine learning methods, and dataset names from scientific papers. The proposed method's expression extraction performance, when using Llama3, achieves an F-score exceeding 0.8 across various categories. Benchmarking results on financial domain papers have demonstrated the effectiveness of this method.
  arXiv  Detail & Related papers  (2024-08-22T03:10:52Z)
• Informed Meta-Learning [55.2480439325792]
  Meta-learning and informed ML stand out as two approaches for incorporating prior knowledge into ML pipelines. We formalise a hybrid paradigm, informed meta-learning, facilitating the incorporation of priors from unstructured knowledge representations. We demonstrate the potential benefits of informed meta-learning in improving data efficiency, robustness to observational noise and task distribution shifts.
  arXiv  Detail & Related papers  (2024-02-25T15:08:37Z)
• Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review [1.3812010983144802]
  This paper presents a systematic review on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining. We provide a comprehensive overview of the current methodologies and their applications across various application domains. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for process analytics.
  arXiv  Detail & Related papers  (2023-12-29T12:43:43Z)
• Code Generation for Machine Learning using Model-Driven Engineering and SysML [0.0]
  This work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation.
  arXiv  Detail & Related papers  (2023-07-10T15:00:20Z)
• Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration [0.0]
  Decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe this issue to the widely held view on advanced machine learning algorithms as "black boxes" We develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence.
  arXiv  Detail & Related papers  (2023-06-20T15:11:26Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Modular approach to data preprocessing in ALOHA and application to a smart industry use case [0.0]
  The paper addresses a modular approach, integrated into the ALOHA tool flow, to support the data preprocessing and transformation pipeline. To demonstrate the effectiveness of the approach, we present some experimental results related to a keyword spotting use case.
  arXiv  Detail & Related papers  (2021-02-02T06:48:51Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.