Abstract
This paper extends traditional Functional Dependencies (FDs) to Threshold Functional Dependencies (TFDs) for Time Series Database according to the characteristics of attribute values changing rapidly by time from sensors. In contrast to the unique-to-same pattern in relational schema, TFDs allow determined attribute value within a certain range rather than a clear value when corresponding to the same deciding party. We find that TFDs capable of not only detecting errors resulting from attribute value out-of-bounds in one tuple horizontally, but also from a column of single attribute among several tuples vertically. And we focus more on the former in this article. We draw a clear line between FDs and TFDs because they have some intersection. And we classify TFDs for convenience of research. We provide an inference system for classified TFDs analogous to Armstrong’s axioms, prove its soundness and completeness and explain their differences and connections. We perform some experiments to show effects of TFDs which make some contributions to data quality for Time Series Database.
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Ji, M., Wei, X., Miao, D. (2020). Threshold Functional Dependencies for Time Series Data. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_14
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DOI: https://doi.org/10.1007/978-3-030-59413-8_14
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