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Scalable progressive analytics on big data in the cloud
2013
Proceedings of the VLDB Endowment
Analytics over the increasing quantity of data stored in the Cloud has become very expensive, particularly due to the pay-as-you-go Cloud computation model. Data scientists typically manually extract samples of increasing data size (progressive samples) using domain-specific sampling strategies for exploratory querying. This provides them with user-control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across
doi:10.14778/2556549.2556557
fatcat:taqjyvpk7bggzov5aatoh5svrm