A distributed data exchange engine for
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||Abdulrahman Kaitoua, Tilmann Rabl, Volker Markl
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.
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