Addressing the State Explosion Problem for Big Data Systems Formal Verication

dc.contributor.author Asteasuain, Fernando
dc.date.accessioned 2025-02-07T14:43:29Z
dc.date.available 2025-02-07T14:43:29Z
dc.date.issued 2023-10-12
dc.description.abstract The formal verification of BIG DATA systems remains as a challenging task to be addressed since a very large and complex state space describing the behavior of the system must be explored and verified. In particular, the state explosion problem arises as one of the most problematic issues to be faced against. Some approaches have leveraged on some architectural patterns used in BIG DATA system development, especially those focused on the MAP-REDUCE architecture. Taking this into consideration in this work we present VG-FVS, a new version of our framework FVS (Feather weight Visual Scenarios), which is specially developed to address the state explosion problem. This is achieved by integrating FVS with MaRDiGraS, a generic library which eases the state space exploration using a MAP-REDUCE software architecture. Empirical validation analyzing BIG DATA systems was carried on, showing promising results for our approach.
dc.identifier.citation Asteasuain, Fernando (2023). Addressing the State Explosion Problem for Big Data Systems Formal Verication. En: XXIX Congreso Argentino de Ciencias de la Computación - CACIC 2023. Compilación de Juan Manuel Fernández. Universidad Nacional de Luján. p. 279-288.
dc.identifier.uri https://repositorio.uai.edu.ar/handle/123456789/3447
dc.language.iso en
dc.publisher Universidad Nacional de Luján
dc.subject formal verification
dc.subject BIG DATA Systems
dc.subject state explosion
dc.title Addressing the State Explosion Problem for Big Data Systems Formal Verication
dc.type DOCUMENTOCONF
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Fernando Asteasuain (2023). “Addressing the State Explosion Problem for Big Data Systems Formal Verication”. XXIX Congreso Argentino de Ciencias de la Computación CACIC 2023. Universidad Nacional de Luján. Buenos Aires, Argentina. 9 al 12 de octubre de 2023.
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