In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values.
There are three main models of uncertain data in databases. In attribute uncertainty, each uncertain attribute in a tuple is subject to its own independent probability distribution. For example, if readings are taken of temperature and wind speed, each would be described by its own probability distribution, as knowing the reading for one measurement would not provide any information about the other.
In correlated uncertainty, multiple attributes may be described by a joint probability distribution. For example, if readings are taken of the position of an object, and the x- and y-coordinates stored, the probability of different values may depend on the distance from the recorded coordinates. As distance depends on both coordinates, it may be appropriate to use a joint distribution for these coordinates, as they are not independent.
In tuple uncertainty, all the attributes of a tuple are subject to a joint probability distribution. This covers the case of correlated uncertainty, but also includes the case where there is a probability of a tuple not belonging in the relevant relation, which is indicated by all the probabilities not summing to one. For example, assume we have the following tuple from a probabilistic database:
|(a, 0.4) | (b, 0.5)|
Then, the tuple has 10% chance of not existing in the database.
- Prabhakar, Sunil. ORION: Managing Uncertain (Sensor) Data.
- Volk, Habich; Clemens Utzny, Ralf Dittmann, Wolfgang Lehner. "Error-Aware Density-Based Clustering of Imprecise Measurement Values". Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007. IEEE.
- Rosentahl, Volk; Martin Hahmann, Dirk Habich,Wolfgang Lehner. "Clustering Uncertain Data With Possible Worlds". Proceedings of the 1st Workshop on Management and mining Of UNcertain Data in conjunction with the 25th International Conference on Data Engineering, 2009. IEEE.
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