Similarity Search on Computational Notebooks

TL;DR

This paper introduces a novel framework for similarity search in computational notebooks, enabling efficient retrieval of relevant notebooks based on content similarity, which improves search accuracy and speed.

Contribution

It proposes set-based and graph-based similarity measures and develops optimization techniques like caching and indexing for effective notebook retrieval.

Findings

Graph-based similarity achieves high accuracy.

Optimization techniques significantly speed up search.

Framework effectively prunes irrelevant candidates.

Abstract

Computational notebook software such as Jupyter Notebook is popular for data science tasks. Numerous computational notebooks are available on the Web and reusable; however, searching for computational notebooks manually is a tedious task, and so far, there are no tools to search for computational notebooks effectively and efficiently. In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search. Given contents (i.e., source codes, tabular data, libraries, and outputs formats) in computational notebooks as a query, the similarity search problem aims to find top-k computational notebooks with the most similar contents. We define two similarity measures; set-based and graph-based similarities. Set-based similarity handles each content independently, while graph-based similarity captures the relationships between contents. Our framework can effectively prune the candidates of computational notebooks that should not be in the top-k results. Furthermore, we develop optimization techniques such as caching and indexing to accelerate the search. Experiments using Kaggle notebooks show that our method, in particular graph-based similarity, can achieve high accuracy and high efficiency.