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m-tables: Representing Missing Data

Authors Bruhathi Sundarmurthy, Paraschos Koutris, Willis Lang, Jeffrey Naughton, Val Tannen

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Bruhathi Sundarmurthy
Paraschos Koutris
Willis Lang
Jeffrey Naughton
Val Tannen

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Bruhathi Sundarmurthy, Paraschos Koutris, Willis Lang, Jeffrey Naughton, and Val Tannen. m-tables: Representing Missing Data. In 20th International Conference on Database Theory (ICDT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 68, pp. 21:1-21:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.ICDT.2017.21

Abstract

Representation systems have been widely used to capture different forms of incomplete data in various settings. However, existing representation systems are not expressive enough to handle the more complex scenarios of missing data that can occur in practice: these could vary from missing attribute values, missing a known number of tuples, or even missing an unknown number of tuples. In this work, we propose a new representation system called m-tables, that can represent many different types of missing data. We show that m-tables form a closed, complete and strong representation system under both set and bag semantics and are strictly more expressive than conditional tables under both the closed and open world assumptions. We further study the complexity of computing certain and possible answers in m-tables. Finally, we discuss how to "interpret" m-tables through a novel labeling scheme that marks a type of generalized tuples as certain or possible.

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Keywords
  • missing values
  • incomplete data
  • c tables
  • representation systems

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