Databases

Advances in Probabilistic Databases for Uncertain by John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan

By John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)

This ebook covers a fast-growing subject in nice intensity and makes a speciality of the applied sciences and purposes of probabilistic facts administration. It goals to supply a unmarried account of present reviews in probabilistic information administration. the target of the publication is to supply the state-of-the-art details to researchers, practitioners, and graduate scholars of knowledge expertise of clever details processing, and even as serving the knowledge know-how specialist confronted with non-traditional functions that make the appliance of traditional ways tough or impossible.

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Distributional nodes, on the other hand, are used only for defining the probabilistic process that generates random documents (but they do not actually occur in those documents). As an example, Figure 1 shows a p-document P, where the distributional nodes are the ones represented by boxes with rounded corners (and denoted by v1 , v2 , and so on). The words ind and mux inside those boxes will be discussed later. Each distributional node specifies a probability distribution over subsets of its children; later on, we will define several types of distributional nodes (like ind and mux), where each type defines the way these distributions are encoded.

Probabilistic object bases. ACM Transactions on Database Systems 26(3), 264–312 (2001) 10. : Special issue on uncertain and probabilistic databases. The VLDB Journal 18(5), 987–988 (2009) 11. : A probabilistic object-oriented data model. Data & Knowledge Engineering 12(2), 143–166 (1994) 12. : ProbView: a flexible probabilistic database system. ACM Transactions on Database Systems 22(3), 419–469 (1997) 13. : Updating extended possibility-based fuzzy relational databases. International Journal of Intelligent Systems 22(3), 237–258 (2007) 14.

CLASS class-name WITH PROBABILITY degree INHERITS superclass1 WITH PROBABILITY degree1 … INHERITS superclassk WITH PROBABILITY degreek ATTRIBUTES Attribute1: DOMAIN dom1: TYPE OF type1 … Attributem: DOMAIN domm: TYPE OF typem Fuzzy Probabilistic Attribute: FUZZY DOMAIN: TYPE OF real METHODS … END 36 L. Yan and Z. , non fuzzy probabilistic attribute), it corresponds to a data type, which may be a simple type such as integer, real, Boolean and string, or may be a complex type such as set type and object type.

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