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Scientific workflows are increasingly gaining momentum as the new paradigm for modeling and enacting scientific experiments. The value of a workflow specification does not end once it is enacted. Indeed, workflow specifications encapsulate knowledge that documents scientific experiments, and are, therefore, worth preserving. Our experience suggests that workflow preservation is frequently hampered by the volatility of the constituent service operations when these operations are supplied by third-party providers. To deal with this issue, we propose a heuristic for locating substitutes that are able to replace unavailable service operations within workflows. The proposed method uses the data links connecting inputs and outputs of service operations in existing workflow specifications to locate operations with parameters compatible with those of the missing operations. Furthermore, it exploits provenance traces collected from past executions of workflows to ensure that candidate substitutes perform tasks similar to those of the missing operations. The effectiveness of the proposed method has been empirically assessed.
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Stored only when both papers are in the corpus — 16 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.