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Domain Specific Language (DSL) visualisation for Big Data Pipelines
KTH, School of Electrical Engineering and Computer Science (EECS).
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the grow of big data technologies, it has become challenging to design and manage complex data workflow, especially for non technical person. However, in order to understand and process these data the best way, we need to rely on domain expert who are often not familiar with tools available on the market. This thesis discovers the needs and describe the implementation of an easy to use tool to define and visualise data processing workflow. The research methodology includes the definition of customer requirements, architecture design, prototype development and user testing. The iterative approach used in this project ensure continuous improvement based on users feedback. The final solution then assessed using KPI metrics such as usability, integration, performances and support.

Abstract [sv]

Med den växande big data-tekniken har det blivit en utmaning att utforma och hantera komplexa dataarbetsflöden, särskilt för icke-tekniska personer. För att förstå och bearbeta dessa data på bästa sätt måste vi dock förlita oss på domänexperter som ofta inte är bekanta med de verktyg som finns tillgängliga på marknaden. Denna avhandling identifierar behoven och beskriver implementeringen av ett lättanvänt verktyg för att definiera och visualisera arbetsflödet för databehandling. Detta genom att abstrahera de tekniska krav som krävs av andra lösningar. Forskningsmetoden omfattar definition av kundkrav, arkitekturdesign, prototyputveckling och användartestning. Det iterativa tillvägagångssätt som används i detta projekt säkerställer kontinuerlig förbättring baserat på användarnas feedback. Den slutliga lösningen utvärderas sedan med hjälp av nyckeltal som användbarhet, integration, prestanda och support.

Place, publisher, year, edition, pages
2024. , p. 44
Series
TRITA-EECS-EX ; 2024:70
Keywords [en]
Data pipeline, DSL, DataCloud, Visualization
Keywords [sv]
Datapipeline, DSL, DataCloud, Visualisering
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-346244OAI: oai:DiVA.org:kth-346244DiVA, id: diva2:1856750
Supervisors
Examiners
Available from: 2024-05-20 Created: 2024-05-08 Last updated: 2024-05-20Bibliographically approved

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fulltext(1858 kB)130 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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