# Explaining Aggregates for Exploratory Analytics

arXiv: https://arxiv.org/abs/1812.11346 · HTML: https://mlsanity.com/paper/1812.11346/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [Fast Statistical Leverage Score Approximation in Kernel Ridge Regression](https://mlsanity.com/paper/2103.05238/) (2021)
- [Black-Box Batch Active Learning for Regression](https://mlsanity.com/paper/2302.08981/) (2023)
- [Accumulation of Sub-Sampling Matrices with Applications to Statistical Computation](https://mlsanity.com/paper/2103.04031/) (2021)
- [Explaining Inference Queries with Bayesian Optimization](https://mlsanity.com/paper/2102.05308/) (2021)
- [Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification](https://mlsanity.com/paper/2211.14545/) (2022)

## Abstract (arXiv)

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis. We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA's novel AQ explanations are represented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads.