Abstract
The rapid development of social networks, intelligent sensors, mobile solutions and Internet of things has led to the emergence of large data sets. Efficiently and effectively exploring these data sets is a challenging question, especially when the imperfectness of real-world needs to be taken into account. The objective of this paper is thus to propose several solutions for modelling, representing and implementing imperfect information within large fuzzy databases. More specifically, in this paper imperfect information is modelled through a series of generic fuzzy data types, uniformly represented by means of possibility distributions and implemented using the basic constructs of object databases. These solutions are particularly useful for exploring large fuzzy databases since they permit to minimize the space required to store imperfect information, and access efficiently and effectively these databases.
Original language | English |
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Pages (from-to) | 152-169 |
Number of pages | 18 |
Journal | Journal of Decision Systems |
Volume | 26 |
Issue number | 2 |
Early online date | 7 Nov 2016 |
DOIs | |
Publication status | Published - 8 Feb 2017 |
Keywords
- Large data-sets
- imperfect information
- uncertainty
- imprecision
- fuzziness
- incomplete data
- fuzzy attributes
- fuzzy database
- mapping rule