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An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions
Corporate Technology, Siemens AG, 81739, Munich, Germany.
Corporate Technology, Siemens AG, 81739, Munich, Germany.
Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden.
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-7700-1816
2020 (Engelska)Ingår i: Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020, Proceedings / [ed] Maurizio Morisio; Marco Torchiano; Andreas Jedlitschka, Springer, 2020, s. 217-233Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.

Ort, förlag, år, upplaga, sidor
Springer, 2020. s. 217-233
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12562
Nyckelord [en]
Machine learning, Software analytics, Business intelligence
Nationell ämneskategori
Programvaruteknik
Identifikatorer
URN: urn:nbn:se:mau:diva-56803DOI: 10.1007/978-3-030-64148-1_14ISI: 000766320200014Scopus ID: 2-s2.0-85097649181ISBN: 978-3-030-64147-4 (tryckt)ISBN: 978-3-030-64148-1 (digital)OAI: oai:DiVA.org:mau-56803DiVA, id: diva2:1720441
Konferens
21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020
Tillgänglig från: 2022-12-19 Skapad: 2022-12-19 Senast uppdaterad: 2023-12-14Bibliografiskt granskad

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Olsson, Helena Holmström

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Totalt: 14 träffar
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