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A distributed data exchange engine for polystores

dc.contributor.authorKaitoua, Abdulrahman
dc.contributor.authorRabl, Tilmann
dc.contributor.authorMarkl, Volker
dc.date.accessioned2021-06-21T09:38:45Z
dc.date.available2021-06-21T09:38:45Z
dc.date.issued2020
dc.description.abstractThere is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary. Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines’ instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.en
dc.identifier.doi10.1515/itit-2019-0037
dc.identifier.pissn2196-7032
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36567
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 62, No. 3-4
dc.subjectDistributed systems
dc.subjectdata migration
dc.subjectdata transformation
dc.subjectbig data engine
dc.subjectdata integration
dc.titleA distributed data exchange engine for polystoresen
dc.typeText/Journal Article
gi.citation.endPage156
gi.citation.publisherPlaceBerlin
gi.citation.startPage145

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