Assessing Achievability of Queries and Constraints
Rada Chirkova, Jon Doyle, Juan L. Reutter

TL;DR
This paper introduces a framework for assessing whether sequences of data transformation procedures can produce data satisfying specific constraints or queries, focusing on the relational setting and abstracting procedures as black boxes.
Contribution
It formalizes the problem of verifying data transformations' effectiveness and decidability for certain classes of procedures and properties, including schema-altering procedures.
Findings
Decidability results for data transformation sequences under specific conditions.
Framework for modeling procedures with schema modifications.
Analysis of when data transformations guarantee property satisfaction.
Abstract
Assessing and improving the quality of data in data-intensive systems are fundamental challenges that have given rise to numerous applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data migration are nowadays reasonably well understood in isolation, not much attention has been given to the interplay between the tools that address issues in these areas. Our focus is on the problem of determining whether there exist sequences of data-transforming procedures that, when applied to the (untransformed) input data, would yield data satisfying the conditions required for performing the task in question. Our goal is to develop a framework that would address this problem, starting with the relational setting. In this paper we abstract data-processing tools as black-box procedures. This abstraction describes procedures by a specification…
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Taxonomy
TopicsData Quality and Management · Advanced Database Systems and Queries · Semantic Web and Ontologies
