Planning meets Data Cleansing
Open accessAbstract
One of the motivations for research in data quality is to automatically identify cleansing activities, namely a sequence of actions able to cleanse a dirty dataset, which today are often developed manually by domain-experts. Here we explore the idea that AI Planning can contribute to identify data inconsistencies and automatically fix them. To this end, we formalise the concept of cost-optimal Universal Cleanser — a collection of cleansing actions for each data inconsistency — as a planning problem. We present then a motivating government application in which it has be used.
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Methods to automatically identify appropriate data cleaning activities for an incomplete or biased dataset are still emerging, but could be achieved with frameworks such as MLClean, HoloClean, Activeclean and Universal Cleanser.
“Methods to automatically identify the appropriate data cleaning activities for an incomplete or biased dataset are still emerging, but could be achieved with the use of frameworks such as MLClean (that removes data poisoning to help training accurate and fair models [100]) and tools such as like HoloClean, Activeclean [98,99] and Universal Cleanser [160].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [160]
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- Authors
- Boselli, R., Cesarini, M., Mercorio, F., & Mezzanzanica, M.
- Affiliations
- University of Milano-Bicocca (IT)
- Venue
- Proceedings of the International Conference on Automated Planning and Scheduling
- Published
- 2014-05-11
- Volume
- 24
- Pages
- 439-443
- ISSN
- 2334-0835 (ISSN-L)
- 2334-0843
- OpenAlex
- W2240123196
- Cited by
- 24
- References
- 23
- Citations by year
- 2026: 1 · 2023: 1 · 2022: 1 · 2020: 2 · 2019: 2 · 2018: 1 · 2017: 5 · 2016: 2 · 2015: 5 · 2014: 4
- FWCI
- 8.61
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International Conference on Automated Planning and Scheduling (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- data cleaning, data quality, automated planning, data inconsistency
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