BlobShuffle reduces shuffling costs by over 40x in Kafka Streams by using object storage for batching with notifications, achieving sub-2s 95th-percentile latency and scaling beyond 2 GiB/s.
Empirical Software Engineering27(6) (Aug 2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes PM-EdgeMap formalism and demonstrates feasibility of real-time process mining on the edge via conformance checking case study for smart factories.
citing papers explorer
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BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams
BlobShuffle reduces shuffling costs by over 40x in Kafka Streams by using object storage for batching with notifications, achieving sub-2s 95th-percentile latency and scaling beyond 2 GiB/s.
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The PM-EdgeMap: Towards Real-Time Process Mining on the Edge-Cloud Continuum
Proposes PM-EdgeMap formalism and demonstrates feasibility of real-time process mining on the edge via conformance checking case study for smart factories.