# Detecting coherent explorations in SQL workloads

## TL;DR

This paper introduces a method to analyze SQL workloads by identifying coherent exploration sequences within them, using feature extraction and empirical validation on real-world datasets.

## Contribution

It proposes a novel approach to detect meaningful exploration patterns in SQL workloads, especially for ad-hoc queries in data science platforms.

## Key findings

- Successfully separated SQL query sequences into meaningful explorations
- Validated approach on multiple query workloads
- Effective feature extraction for understanding SQL query behavior

## Abstract

This paper presents a proposal aiming at better understanding a workload of SQL queries and detecting coherent explorations hidden within the workload. In particular, our work investigates SQLShare [11], a database-as-a-service platform targeting scientists and data scientists with minimal database experience, whose workload was made available to the research community. According to the authors of [11], this workload is the only one containing primarily ad-hoc hand-written queries over user-uploaded datasets. We analyzed this workload by extracting features that characterize SQL queries and we show how to use these features to separate sequences of SQL queries into meaningful explorations. We ran several tests over various query workloads to validate empirically our approach.