Workload aware data partitioning

dc.contributor.authorMay, Norman
dc.contributor.authorBoehm, Alexander
dc.contributor.authorMoerkotte, Guido
dc.contributor.authorBrendle, Michael
dc.contributor.authorValiyev, Mahammad
dc.contributor.authorWeber, Nick
dc.contributor.authorSchulze, Robert
dc.contributor.authorGrossniklaus, Michael
dc.date.accessioned2022-02-21T07:40:53Z
dc.date.available2022-02-21T07:40:53Z
dc.date.issued2022eng
dc.description.abstractTechniques and solutions are described for partitioning data among different types of computer-readable storage media, such as between RAM and disk-based storage. A measured workload can be used to estimate data access for one or more possible partition arrangements. The partitions arrangements can be automatically enumerated. Scores for the partition arrangements can be calculated, where a score can indicate how efficiently a partition arrangement places frequently accessed data into storage specified for frequently-accessed data and placed infrequently accessed data into storage specified for infrequently accessed data.eng
dc.description.versionpublishedde
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/56602
dc.language.isoengeng
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dc.subject.ddc004eng
dc.titleWorkload aware data partitioningeng
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@misc{May2022Workl-56602,
  year={2022},
  title={Workload aware data partitioning},
  url={https://patents.google.com/patent/US20220019589A1/en},
  author={May, Norman and Boehm, Alexander and Moerkotte, Guido and Brendle, Michael and Valiyev, Mahammad and Weber, Nick and Schulze, Robert and Grossniklaus, Michael}
}
kops.citation.iso690MAY, Norman, Alexander BOEHM, Guido MOERKOTTE, Michael BRENDLE, Mahammad VALIYEV, Nick WEBER, Robert SCHULZE, Michael GROSSNIKLAUS, 2022. Workload aware data partitioningdeu
kops.citation.iso690MAY, Norman, Alexander BOEHM, Guido MOERKOTTE, Michael BRENDLE, Mahammad VALIYEV, Nick WEBER, Robert SCHULZE, Michael GROSSNIKLAUS, 2022. Workload aware data partitioningeng
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