7 lines
653 B
TeX
7 lines
653 B
TeX
We introduce a provenance-based approach for predicting and tracking dependencies across python cells in a computational notebook and an implementation of this approach in Vizier, a data-centric notebook system where cells are isolated from each other and communicate through data artifacts. By combining best effort static analysis with an adaptable runtime schedule for notebook cell execution, we achieve (i) parallel execution of python cells, (ii) automatic refresh of dependent cells when the notebook is modified, and (iii) translation of Jupyter notebooks into our model.
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