BWhat changed
Integrated a dynamic key-preserving embedding table with admission/expiry filtering, streaming feature–label joins and incremental synchronization between training and serving parameter servers.
Scientific scope
Integrated a dynamic key-preserving embedding table with admission/expiry filtering, streaming feature–label joins and incremental synchronization between training and serving parameter servers. Uses cuckoo hashing, TensorFlow, Kafka and Flink rather than inventing those components. Collisionless lookup does not retain every ID: filtering and expiry discard some features. Sparse/dense synchronization can be inconsistent and delayed labels require storage; empirical reliability/freshness trade-offs are not a universal fault-tolerance or unbiasedness proof.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Uses cuckoo hashing, TensorFlow, Kafka and Flink rather than inventing those components. Collisionless lookup does not retain every ID: filtering and expiry discard some features. Sparse/dense synchronization can be inconsistent and delayed labels require storage; empirical reliability/freshness trade-offs are not a universal fault-tolerance or unbiasedness proof.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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