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Knowledge Discovery from Data (KDD) has mostly focused on understanding the available data. Statistically-sound KDD shifts the goal to understanding the partially unknown, random Data Generating Process (DGP) process that generates the data. This shift is necessary to ensure that the results from data analysis constitute new knowledge about the DGP, as required by the practice of scientific research and by many industrial applications, to avoid costly false discoveries. In statistically-sound KDD, results obtained from the data are considered as hypotheses, and they must undergo statistical testing, before being deemed significant, i.e., informative about the DGP. The challenges include (1) how to subject the hypotheses to severe testing to make it hard for them to be deemed significant; (2) considering the simultaneous testing of multiple hypotheses as the default setting, not as an afterthought; (3) offering flexible statistical guarantees at different stages of the discovery process; and (4) achieving scalability along multiple axes, from the size of the data to the number and complexity of hypotheses to be tested. Success for Statistically-sound KDD as a field will be achieved with (1) the introduction of a rich collection of null models that are representative of the KDD tasks, and of the existing knowledge of the DGP by field experts; (2) the development of scalable algorithms for testing results for many KDD tasks on different data types; and (3) the availability of benchmark dataset generators that allow to thoroughly evaluate these algorithms.
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Synthetic data for learning-based knowledge discovery
Recent advances in deep learning have demonstrated the ability of learning-based methods to tackle very hard downstream tasks. Historically, this has been demonstrated in predictive tasks, while tasks more akin to the traditional KDD (Knowledge Discovery in Databases) pipeline have enjoyed proportionally fewer advances. Can learning-based approaches help with inherently hard problems within the KDD pipeline, such as how many patterns are in the data, what are different structures in the data, and how can we robustly extract those structures? In this vision paper, we argue for the need for synthetic data generators to empower cheaply-supervised learning-based solutions for knowledge discovery. We describe the general idea, early proof-of-concept results which speak to the viability of the paradigm, and we outline a number of exciting challenges that await, and a set of milestones for measuring success.
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- Award ID(s):
- 2112650
- PAR ID:
- 10591466
- Publisher / Repository:
- ACM Digital Library
- Date Published:
- Journal Name:
- ACM SIGKDD Explorations Newsletter
- Volume:
- 26
- Issue:
- 1
- ISSN:
- 1931-0145
- Page Range / eLocation ID:
- 19 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3682112.3682115
