Alex Klibisz created FLINK-9167:
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Summary: Approximate KNN with Incremental Insertion
Key: FLINK-9167
URL:
https://issues.apache.org/jira/browse/FLINK-9167 Project: Flink
Issue Type: New Feature
Components: Machine Learning Library
Reporter: Alex Klibisz
I'm new to Flink, and I'm curious about an extension of approximate KNN to support incremental insertion to the index.
Consider the case where you build an index from a training set of vectors. As your application runs, you ingest a stream of new vectors (e.g. users posting new content). For every new vector, you compute its neighbors against the existing index. Then you immediately insert the new vector to the index such that it can be returned for subsequent queries.
Perhaps this is possible with current components of Flink, or maybe another streaming tool already has a comparable implementation? If so, I would appreciate any pointers or links to examples.
If it's not available, is there interest in implementing such a feature? If so, I would be interested in making an attempt.
I appreciate any tips or insight. Thanks!
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