@inproceedings{ding2019quickinsights,
author = {Ding, Rui and Han, Shi and Xu, Yong and Zhang, Haidong and Zhang, Dongmei},
title = {QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data},
booktitle = {Proceedings of the 2019 ACM International Conference on Management of Data (SIGMOD'19 industrial track)},
year = {2019},
month = {June},
abstract = {Discovering interesting data patterns is a common and important analytical need in data analysis and exploration, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as well as general mining frameworks to derive them effectively and efficiently. We present a novel technique QuickInsights, which quickly and automatically discovers interesting patterns from multi-dimensional data. QuickInsights proposes a unified formulation of interesting patterns, called insights, and designs a systematic mining framework to discover high-quality insights efficiently. We demonstrate the effectiveness and efficiency of QuickInsights through our evaluation on 447 real datasets as well as user studies on both expert users and non-expert users. QuickInsights is released in Microsoft Power BI.},
url = {http://approjects.co.za/?big=en-us/research/publication/quickinsights-quick-and-automatic-discovery-of-insights-from-multi-dimensional-data/},
}