Each attributes value v [member of] V has a corresponding degree of appurtenance d(x, v) of the element x, to the set P, with respect to some given criteria.
Let x [member of] P be a generic student that is characterized by three attributes:
You also need to analyse what additional attributes might be required, and how those attributes will be populated when you take the existing data across from the old system.
Regarding Tables as attributes, a caveat here would be to use meaningful names for tables which will save any confusion when using tools such as SQL to interrogate data.
In this article, we propose three activities that, after assessing students' knowledge regarding measurable attributes, help them deepen their understanding of quantities by categorizing measurable attributes and unit of measurement.
In the first activity, students demonstrate their level of fluency and concurrently foster their vocabulary by matching measurable attributes with measuring units.
The purpose of this paper is to propose an approach to determining attribute weights based on integrating interval decision matrix and the experts' preference information on attributes in the formats of preference orderings, linguistic terms, interval numbers, and inequality constraints among them.
In order to facilitate describing the MADM problem with interval decision matrix and experts' preference information on attributes in the formats of preference orderings, linguistic terms, interval numbers, and inequality constraints among the attribute weights, the following assumptions and notations are adopted:
The purpose of splitting attributes into different blocks is to facilitate the introduction of a variety of flexible similarity metrics to identify redundant attributes or closest entities.
where sim(v_i, v_j) takes values in the interval [0, 1], with higher values indicating higher similarity between the given attributes. If it is higher than an established threshold, that means the given attributes are redundant.
, which applies users' attributes to find suitable friends and establishes social relationships with strangers via a multihop trust chain.
(1) The friend-recommendation application server broadcasts three pieces of information to all users: (a) the attributes users can submit; (b) the attribute groups; (c) the part of keys used to decrypt users' information, each of which corresponds to one attribute group's value and hence different attribute groups' values have different keys.
This kind of problem can be solved using the modern techniques like the generation of attributes derived from seismic data, which could be connected physically to the reservoir properties(Khan et al.,1986).
After computation, these properties are then interpolated to the whole seismic volume with the help of impedance and seismic attributes. After the establishment of relationship between petrophysical properties and attributes derived from seismic, the reservoir properties like fluid content, lithology and productive zones boundaries could be predicted (Curia, 2009).
proposed a KP-ABE scheme  which can achieve the revocation of users; however, this scheme is limited to be used if and only if the number of attributes
associated with ciphertext is just half of the whole attributes
in the system; therefore, the limit is too high which impedes its actual application.