21 citations · 29 across the 5 of their papers we have counts for
5 papers
SHARQ: Explainability Framework for Association Rules on Relational Data
Hadar Ben-Efraim, Susan B. Davidson, Amit Somech
Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is diffi…
LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration
Tavor Lipman, Tova Milo, Amit Somech +2
Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to beco…
FEDEX: An Explainability Framework for Data Exploration Steps
Daniel Deutch, Amir Gilad, Tova Milo +2
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this pa…
SubStrat: A Subset-Based Strategy for Faster AutoML
Teddy Lazebnik, Amit Somech, Abraham Itzhak Weinberg
Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction…
Selecting Sub-tables for Data Exploration
Kathy Razmadze, Yael Amsterdamer, Amit Somech +2
We present a framework for creating small, informative sub-tables of large data tables to facilitate the first step of data science: data exploration. Given a large data table tabl…