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SUMMARY:CS MSc Thesis Presentation 27 October 2025
DESCRIPTION:Contact: birger.swahn@cs.lth.se\n\nMonday\, 27 October there wi
 ll be a master thesis presentation in Computer Science at Lund University\
 , Faculty of Engineering.The presentation will take place in E:4130 (Lucas
 ).Note to potential opponents: Register as an opponent to the presentation
  of your choice by sending an email to the examiner for that presentation 
 (firstname.lastname@cs.lth.se). Do not forget to specify the presentation 
 you register for! Note that the number of opponents may be limited (often 
 to two)\, so you might be forced to choose another presentation if you reg
 ister too late. Registrations are individual\, just as the oppositions are
 ! More instructions for opponents are found here on the LTH thesis project
  page.13:00-14:00 in E:4130 (Lucas) N.B. Change of time and placePresenter
 : Christopher KällströmTitle: Data Quality and Quanity for Machine Learn
 ing at the European Spallation SourceExaminer: Per RunesonSupervisor: Fred
 rik Edman (LTH)\, Karin Rathsman (ESS)\, Timo Korhonen (ESS)This thesis in
 vestigates data governance challenges and solution strategies for preparin
 g the European Spallation Source (ESS) control-system data infrastructure 
 for machine learning (ML)-driven analytics. ESS generates millions of proc
 ess variables daily\, yet current practices emphasize engineering-driven c
 ollection over analytical readiness. Through a literature review and stake
 holder interviews\, three research questions are addressed (i) identifying
  key challenges in data quality\, metadata\, and retrieval (ii) proposing 
 governance\, architectural\, and tooling strategies (iii) evaluating their
  applicability in ESS test environments. Findings reveal ambiguous ownersh
 ip\, metadata incompleteness\, oversampling\, and retrieval inefficiencies
  as core barriers to ML readiness. Solution candidates include clarified g
 overnance roles\, standardized metadata and configuration policies\, adapt
 ive archiving\, and improved technical configurations. The evaluation high
 lights both organizational and technical pathways to transition ESS from a
 d hoc data accumulation toward a curated\, machine-learning-ready data eco
 system. This contributes practical recommendations for ESS and advances re
 search on data governance in large-scale scientific infrastructures.&nbsp\
 ;\n\nMore information about the event: https://www.cs.lth.se/en/calendar/c
 s-msc-thesis-presentation-27-october-2025
DTSTART;TZID=GMT:20251027T120000
DTEND;TZID=GMT:20251027T130000
LOCATION:E:4130 (Lucas)
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