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DSDSD - Provenance Research in Gray Systems Lab at Microsoft

dsdsd.da.cwi.nl · 409 words · saved by 1 readers

Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, provenance is instrumental for a wide range of enterprise applications, including governance, auditing, and observability. As provenance becomes more prevalent across enterprise applications, research and engineering need to work in tandem to define, develop, and optimize provenance functionality. To this end, at Microsoft’s Gray Systems Lab (GSL), we have identified provenance capture, provenance querying, and provenance-aware applications as key domains for provenance research and research engineering. In this talk, I will present selected projects we have been working on in GSL, along with key challenges and lessons learned, per research area. Regarding provenance capture, I will first present OneProvenance, an engine that captures dynamic, coarse-grained provenance from database logs efficiently and effectively; OneProvenance is c

Provenance Research in Gray Systems Lab at Microsoft Fotis Psallidas Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, provenance is instrumental for a wide range of enterprise applications, including governance, auditing, and observability. As provenance becomes more prevalent across enterprise applications, research and engineering need to work in tandem to define, develop, and optimize provenance functionality. To this end, at Microsoft’s Gray Systems Lab (GSL), we have identified provena

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