A cost-benefit decision model: analysis, comparison amd selection of data management

Abstract

This paper describes a general cost-benefit decision model that is applicable to the evaluation, comparison, and selection of alternative products with a multiplicity of features, such as complex computer systems. The application of this model is explained and illustrated using the selection of data management systems as an example.The model has the following features: (1) it is mathematically based on an extended continuous logic and a theory of complex criteria; (2) the decision-making procedure is very general yet systematic, well-structured, and quantitative; (3) the technique is based on a comprehensive cost analysis and an elaborate analysis of benefits expressed in terms of the decision maker's preferences. The decision methodology, when applied to the problem of selecting a data management system, takes into consideration the life cycle of a DMS and the objectives and goals for the new systems under evaluation. It allows the cost and preference analyses to be carried out separately using two different models. The model for preference analysis makes use of comprehensive performance (or preference) parameters and allows what we call a "logic scoring of preferences" using continuous values between zero and one, to express the degree with which candidate systems satisfy stated requirements. It aggregates preference parameters based on their relative weights and logical relationships to compute a global performance (preference) score for each system. The cost model incorporates an aggregation of costs which may be estimated over different time horizons and discounted at appropriate discount rates. A procedure to establish an overall ranking of alternative systems based on their global preference scores and global costs is also discussed.

Bipolarity and Multipolarity in Aggregation Structures

“…It is useful to note that the canonical aggregation structures in both Figure 1 and Figure 3 use 'bipolarity inside bipolarity' (or 'multipolarity') identifying four categories of attributes: (1) mandatory desired, (2) optional desired, (3) mandatory undesired, and (4) optional undesired. Thus, bipolarity, tripolarity [19] (mandatory/desired/optional, shown in Figure 4), and generally 'multipolarity' reflect situations where we have two or more logically dissimilar clusters of attributes and each cluster contains attributes that have a similar logical impact on the overall suitability score. …”

Efficient GCD functions in SQL

“…As an illustration of the method, a couple of special cases for the value r are useful, more specifically in the case of n = 2 (as defined by [9]), shown in Table 1.…”

“…These values were calculated by relating a quantity c ∈ [0, 1], called the conjunction degree, to r, for certain values of n. Variable c indicates the degree of conjunctivity of an aggregate function [9], where c = 0 and c = 1 will respectively result in a disjunction (i.e., the maximum) and conjunction (i.e., the minimum).…”

“…Queries that implement this grammar are transformed in the native language of a target database, enabling a user to execute complex fuzzy queries. More importantly, optimizations are suggested and implemented that take advantage of Table 2: GCD functions [9] the properties of the required aggregation technique (in this paper, GCD is considered).…”

Service Selection Based on Non-functional Properties

“…The second r value is statically defined as CA; CA acts as a filter to weed out the services not satisfying one or more hard criteria. Note that DAC and CA are two of the typically offered LSP GCD operators, and details can be found in [11], but are immaterial here. Behaviours of the conjunctive partial absorption function are such that the global preference value (denoted by GP) will be 0 when any of the critical preferences are not satisfied, in which case the service will be discarded.…”