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Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers

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Abstract

Certain answers are a principled method for coping with uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Thus, users frequently resort to less principled approaches to resolve uncertainty. In this paper, we propose Uncertainty Annotated Databases (UA-DBs), which combine an under- and over-approximation of certain answers to achieve the reliability of certain answers, with the performance of a classical database system. Furthermore, in contrast to prior work on certain answers, UA-DBs achieve a higher utility by including some (explicitly marked) answers that are not certain. UA-DBs are based on incomplete K-relations, which we introduce to generalize the classical set-based notion of incomplete databases and certain answers to a much larger class of data models. Using an implementation of our approach, we demonstrate experimentally that it efficiently produces tight approximations of certain answers that are of high utility.

bibtex

@inproceedings{FH19,
  author = {Feng, Su and Huber, Aaron and Glavic, Boris and Kennedy, Oliver},
  booktitle = {Proceedings of the 44th International Conference on Management of Data},
  keywords = {UADB; Vizier},
  longversionurl = {https://arxiv.org/pdf/1904.00234},
  pages = {1313-1330},
  pdfurl = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/FH19.pdf},
  projects = {Vizier},
  slideurl = {https://www.slideshare.net/lordPretzel/2019-sigmod-uncertainty-annotated-databases-a-lightweight-approach-for-approximating-certain-answers},
  title = {Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers},
  venueshort = {SIGMOD},
  year = {2019}
}

Reference

Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers Su Feng, Aaron Huber, Boris Glavic and Oliver Kennedy Proceedings of the 44th International Conference on Management of Data (2019), pp. 1313–1330.