Evaluate Database Management System Quality By Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) Methodolog

MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX ISSN: 1803-3814 (Printed), 2571-3701 (Online) https://doi.org/10.13164/mendel.202k.k.067 Evaluate Database Management System Quality By Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) Methodology Asmaa Jameel Al Nawaiseh1,  , Audi Albtoush2, Ra’Fat Al-Msie’deen1, Sabah Jamil Al Nawaiseh3 1Department of Software Engineering, Faculty of Information Technology, Mu´ tah University, Mu´ tah 61710, Karak, Jordan 2Faculty of IT, Mutah University, Mutah 61710, Karak, Jordan 3Educational Technology Department, Middle East University, Jordan [email protected]  , o[email protected], [email protected] Abstract Any organization that intends to use component-based software development, like outsourcing software, must first evaluate existing components against system requirements to find the best fit among many alternatives. As a result, there should be a mechanism to help with decision-making. Our proposed methodology tries to select the best alternative among available components, using the best decisionmaking approach. As an integrated method for order preference, the methodology in this paper uses two well-known criterion decision-making procedures, namely Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). By analyzing and selecting the optimal solution among a variety of Out Sourcing (OS) modules, the new model design makes the decision-making process easier. We evaluated two software attributes and predicted which was more effective. In this case, the advantage of utilizing AHP is that it allows the developer to evaluate the structure of the OS selection problem and calculate weights for the chosen criteria. After that, the SAW technique is used to calculate the alternatives ratings for OS components. The integration strategy used in our model and the resulting preference indication, which is produced as an explicit numeric value. Keywords: Outsourced Software, SAW, AHP, Usability, MCDM, Component, Attributes, Database. Received: 05 November 2022 Accepted: 27 November 2022 Online: 09 December 2022 Published: 20 December 2022 1 Introduction When employing Outsource Software (OS) in large systems, there are many benefits, such as the opportunity for quick delivery to end users and shared costs with other clients. Reusability of the final application due to the reuse of software components that have already been tested, and the ability to expand capacity [26].The evaluation and selection of acceptable items for systems that rely on OS products are critical to the overall system’s success [14]. Many stakeholders contribute to the system development life cycle, each with its own goals, viewpoints, and interests [20]. A business owner, for example, would be primarily concerned with meeting system requirements within a given financial plan and timeline. Analysts prefer that the product be created by requirements. The focus of quality assurance would be on the products and services delivered to customers [23,24]. Clients want a product that is simple to use, easy to understand, and works as expected. A project manager would design and manage the development process. As a result, a procedure should be put in place to help stakeholders make more decisions in system development [18]. Outsource software is used to construct a wide range of systems with different scenarios and operational environments all over the world. For reuse organizations, however, selecting a specific OS package from among several existing ones is a difficult task [21,12]. By comparing the relevant feature in Oracle 9i with SQL Server 2005, each of the sub-characteristics and attributes is evaluated. This step’s idea is to produce pairwise comparison judgment matrices, which will be used to calculate normalized weights. Various-criteria decision-making (MCDM) or numerous-criteria decision analysis (MCDA) is a sub-discipline of operations research that examines multiple conflicting elements in decision-making openly. The paper [23] established the Analytic Hierarchy Process (AHP) technique is probably the best-known and most widely-used model in decision-making [24]. The Simple Additive Weighting (SAW) approach is used to solve problems utilizing a multi-criteria decision-making process. It is a powerful outdated decision-making tool for identifying the priority among diverse criteria. The recently developed model, on the other hand, uses a hybrid approach to examine and select the best option from several OS components utilizing AHP and SAW techniques [12]. This paper proposes an evaluation process for selecting a suitable OS for an organization by develop67 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX ers and programmers. The evaluation process provides the knowledge required to determine which technique to utilize. As a result, selecting the suitable OS provides a high level of reusability as well as the required benefits. The study published by [22] is the beginning point for our research because it provides the common software quality parameters. The following is the evaluation discussion of the high-level characteristic ‘Usability’, along with its associated sub-characteristics [2]. The following is how the rest of the paper is organized: The Literature Review is covered in Section 2, the Analytical Hierarchy Process (AHP) technique is described in Section 3. The Simple Additive Weighting is described in Section 4. Section 5describes the qualities and how they are assigned to the relevant metrics. Finally, the last sections 6discuss the rationale for selecting the best alternative. 2 Review of Related Studies In this section, we will try to look at the previous studies on the subject of Outsource Software and MultiCriteria Decision Making Methods, then analyze some studies that talk about the outsource software for database systems. What are the most important elements and qualities that have been measured in previous studies of others and their importance? As well as to clarify the weaknesses in these studies and what methods of research they followed. In paper [4] proposed Outsourced Software, Requirements Engineering and Software Architecting (CARE/SA) for evaluating, matching, and selecting OS components. It indicates that each software component is represented by distinct attributes that include architectural, functional, and non-functional aspects. The paper did not use numbers to represent weight. As a result, the evaluation process produces no specific numerical value, which can be regarded as a constraint. In paper [10] proposed that Multi-Criteria Decision Making Methods assist decision-makers in solving the problem of selecting and evaluating software components, where the problem is defined as a collection of multiple criteria that must be considered. It provides an overview of Multi-Criteria Decision Making Methods such as the Analytic Hierarchy Process (AHP), Weighted Scoring Method (WSM), and Hybrid Knowledge-Based System (Hybrid Knowledge-Based System) (HKBS). They compared the three methods and concluded that HKBS is superior to AHP and WSM. Unfortunately, each comparison was conducted independently, and no attempt was made to integrate the three techniques. The paper [22] provides a brief overview of evolutionary techniques. It also derives a hierarchical decomposition method from impact factors to derive goals. It introduces the off-the-shelf-option (OSTO) method for software component selection, which compares the scores and costs associated with each alternative, as well as their relative comparison. It discusses various factors to consider when selecting reusable software components. It also provides evaluation criteria based on various classifications such as functional requirements, product quality attributes, strategic concerns, architecture, and domain compatibility. It presents the outcomes of two case studies conducted using the OSTO method. The component with the highest quality assurance score is chosen for consideration. The limitation of this approach is that it can be extremely sensitive to bias or personnel experience. The OTSO method does not specify which method or model is used to estimate the reuse cost of OS software components. Whichever approach is used, the OTSO method broadens the final OS software evaluation by allowing other factors that may influence the decision to be considered. In paper [11] created an interactive model using AHP to aid in the selection of Web-based learning object software. Furthermore, [4] used the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to evaluate and select the best operating system in terms of organizational factors and strategic performance metrics. The above-mentioned contributions were limited, they used traditional Multiple Criteria Decision-Making, namely AHP and TOPSIS, with no integration. According to [6] method is one of the popular MCDM techniques that researchers are more and more using to solve real-world problems. Several scholars have reviewed MCDM techniques in recent years in fields such as service quality, transportation, and economics. Furthermore, [6] research focuses on the assessment of wastewater treatment alternatives. The fuzzy VIKOR method is proposed for determining the best wastewater treatment option. A case study from Istanbul is used to demonstrate the computational procedure. The proposed FUZZY-VIKOR has one level of criteria for alternatives and no integration with AHP, whereas our research work utilizes three levels and facilitates the integration of AHP and VIKOR [17]. In paper [8] carried out a study to contrast the database management systems Oracle and Microsoft SQL Server. Among the most well-known DBMSs are Microsoft SQL Server, Oracle, IBM Db2, MySQL, and SQLite. The performance of Microsoft SQL Server and Oracle server is compared in the study based on many criteria including theoretical variances, fundamental traits, hardware and software specifications, security, and query execution time [9]. The comparison’s objective is to highlight the benefits and drawbacks of the observed DBMSs so that developers and other computer professionals may determine which database is more dependable and make the best decision when creating various computer applications [7]. 68 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX Al_Nawaiseh-g2igHX,gE valuate Database Management System Quality By Analytic Hierarchy Process (AHP) and ... 3 The Analytical Hierarchy Process (AHP) Methodology In paper [23] created the AHP, which is probably the most well-known and widely used decision-making model. It is an effective decision-making tool for determining the relative importance of various criteria. The AHP consists of six basic steps [3][25]. Step 1: AHP breaks a difficult decision problem into multiple sub-problems that are organized hierarchically. The root of the problem is represented by the goal, and the characteristics are decomposed into several nested sub-levels, representing the process of breaking down the criteria into sub-criteria. Step 2: Create a decision matrix based on [23] ninepoint scale. The decision-maker evaluates the priority score using the basic 1-9 scale. In this context, see Table 1. The numbers 2,4,6,and 8 are intermediate importance values. The decision matrix includes evaluations of each alternative to the decision criteria. If the decision-making problem has Ncriteria and alternatives. Step 3: The goal is to establish their relative prioriTable 1: The scale of Relative Importance Intensity of Importance Definition 1 Equal Importance 3 Moderate importance 5 Strong importance ties about each of the elements at the next level up, the third step entails comparing the hierarchy’s elements in pairs. The Pairwise comparison-matrix, based on the [23] one-to-nine scale, has the following format, where wi represents the weight value of the criteria: Decision-Matrix Pair-Comparison-Matrix   d11 d12 ... d1n d21 d22 ... d2n . . . . . . . . . . . . dm1dm2. dmn  =  w1/w1w1/w2... w1/wn w2/w1w2/w2... w2/wn . . . . . . . . . . . . wn/w1wn/wn ... wm/wn   If nis the number of criteria, the number of pairwise comparisons between them is n(n−1)/2. Each (aij) value in the left-hand matrix is matched with the corresponding. (wi/wj) value in the right-hand matrix. Each pairwise, aij =wi/wj, is computed in the following way: (wi/wj) value in the right-hand matrix. Each pairwise, aij =wi/wj, is computed in the following way: In all cases, wi/wj = 1/aji, except when I=j, in which case wi/wj = 1. In the comparison matrix, and represents the degree of preference of the ith criterion over the jth criterion. It appears that the weight determination of criteria is more reliable when using pairwise comparisons than when obtaining them directly because it is easier to compare two attributes than to assign an overall weight: Step 4: Verify the consistency of judgments across the Consistency Index (CI) and the Consistency Ratio (CR)CI = (λmax −N)/(N−1) [19]. Where λmax is the Eigenvalue corresponding to the matrix of pair-wise comparisons and Nis the number of elements being compared, Consistency ratio (CR) is defined by: CR = CI / RCI where (RCI) is a random consistency index defined in Table 2. A value of CR less than 0.1 is generally acceptable; otherwise, the pair-wise comparisons should be revised to reduce incoherence [1]. Table 2: Random Consistency Index Number of Criteria Consistency Ratio Index 1 0 2 0 3 0.58 4 0.90 5 1.12 6 1.24 7 1.32 8 1.41 9 1.45 10 1.49 Step 5: Normalization of the comparison matrix is required. As a result, the total number of entries in the corresponding column must divide by each element. As a result, a normalized matrix with a vector sum of all elements is obtained. Step 6: The eigenvalues of this matrix must be computed to determine the relative weights of the criteria. The relative weights calculated in the third step should satisfy the following formula:A∗W=maximum Where Ais the Pairwise-comparison-matrix, Wis the weight, and max is the maximum number of eigenvalues. Step 7: If there are elements higher up the hierarchy, the weight vector is computed by multiplying each element (weight coefficient) by its parent at the higher level; this process is repeated until the top of the hierarchy is reached. The option with the highest weight coefficient value should be chosen as the best option. 3.1 Simple Additive Weighting (SAW) Method This method is based on the premise that the chosen alternative should have the greatest utility. It can be applied to a list of projects that have had their various independent attributes scored. It necessitates two major steps. To begin, relative weights must be assigned to each attribute, which is typically normalized so that their sum equals one (1.0)[16]. Second, the intra-attribute values are normalized, which means that regardless of the scale of the various attributes, they should all be converted to a comparable scale within the decision matrix. The following are the final steps in using SAW methods: 69 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX •Determine alternatives, Ai •Determine the criteria that will be used as a reference in decision-making, Cj •Provide the compatibility-rating value of each alternative on each criterion •Determine the level of importance or preference weights (W) for each criterion W= [W1W2W3. . . Wj]. Create a table rating the suitability of each alternative on each criterion. Making the decision-matrix (X) is formed from the rating table that matches each alternative on each criterion. Value of each alternative x(Ai) on each criteria (Cj) are already determined, where, i= 1,2...m and j= 1,2...n. X=   x11 x12 ... x1j . . .. . .. . .. . . xi1xi2... xij    Normalized decision-matrix Xby calculating the value of the performance value rating (rij) of alternative Ai on criteria Cj. Xij =     Xij maxi(Xij)) mini(Xij)) Xij Results of normalized performance value rating (rij) matrix normalized form (R). R=       r11 r12 ... ... ... r1i : : : : : : ri1ri2... ... ... r1i       The proposed methodology is intended to make the use of Multiple Criteria Decision Making (MCDM) techniques as efficient as possible [15]. Two different techniques, AHP and SAW, are combined to rank alternative software based on criteria. The reason for employing the well-known AHP technique is to structure the problem’s decision hierarchy. Finally, one of the most efficient MCDM techniques, such as SAW, is used to rank the alternatives [25]. The following are the main steps of the proposed integrated methodology to be developed by decision-makers for the database software selection problem: Step 1: Define criteria and sub-criteria that have the greatest effect on the database software. Step 2: Construct a hierarchy decision model for the database software. Step 3: Determine the comparison matrix for each level by using the AHP technique. Step 4: Determine the global weight by normalizing the local weight. Step 5: Check the CR Step 6: Use the SAW technique to assess the alternatives. Step 7: Select the best Database software alternative. The illustrates of the proposed integrated methodology to evaluate and select the database as shown in the Figure 1. Figure 1: Proposed Integrated Methodology to Evaluate and Select Database Software 3.2 Defining the Attributes and Assigning their Appropriate Metrics The decision on database software is critical in any business’s long-term planning. The contribution proposes an evaluation process for selecting the appropriate OS component, such as database software, in an organization chosen by a group of developers. The evaluation process provides the knowledge required to confirm the choice of a specific method, and without such knowledge, the benefits will be compromised. Thus, selecting the appropriate operating system results in a high degree of reusability and the desired benefits. Our proposed framework, as described in, [22], is useful for its integrated approach to quality. Each highlevel feature of a database software product is accompanied by a set of sub-features. A sub-characteristic is represented further by sets of software quality attributes. As shown in Figure 2, this chain of software quality attributes can be classified into three levels. At the highest level, the so-called ’characteristic’ from the perspective of a customer or stakeholder, such as ’Usability.’ At the second level, there are the so-called ’Sub-characteristics’ or quality factors from the perspective of customers or stakeholders, such as ’Learnability,’ ’Operability,’ and ’Understandability.’ and ‘Complexity’ [5]. At the third level are the quality criteria (attributes), which represent technical concepts. At the fourth level, the ‘Metric’ measures the quality criteria (attributes) of the database software product. The evaluation discussion of the high-level ‘Usability’ characteristic, as well as their associated 70 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX Al_Nawaiseh-g2igHX,gE valuate Database Management System Quality By Analytic Hierarchy Process (AHP) and ... Figure 2: The framework of OS Quality Attributes sub-characteristics, is provided below. The ability of a software product to be understood, learned, used, and appealing to the user when used under specified conditions is referred to as its usability. The term ’usability’ refers to a set of characteristics that influence the effort required for use as well as the individual evaluation of such use by a stated or implied set of users. Furthermore, ‘usability’ refers to the amount of effort required to learn, operate, prepare input, and interpret output from a program [2]. In an operating system, the majority of component stakeholders are application developers and designers who must create applications with them, and end-users that interact with OS. Thus, a component’s usability should be interpreted as to its ability to be used by the application developer and designer when building a new software product. According to [27], the sub-characteristics of ‘Usability’ are ‘Learnability,’ ‘Operability,’ ‘Understandability,’ and ‘Complexity.’ Learnability: The software product can enable the user to learn its application (ISO9126/IEC, 2001). Learnability requires attention to the needs of the beginner and untrained users that have no previous experience with the software or similar software; there are a set of attributes, which aim to measure the time needed to learn the system, like usage, or configuration. Herein, Learnability attributes will be decomposed into the following, according to ISO9126 [3] There is a set of attributes that try to measure the time needed to master some specific task (such as usage or configuration). Herein, ‘Learnability’ attributes will be decomposed into the following: Time to Use: Attribute measures the average time needed for a user to learn how to correctly use the OS component. Time to Configure: Attribute measures the average time needed to configure the software. Understandability: the ability of a software product to allow a user to grasp whether the program is appropriate for a certain task and under what conditions it can be used,[3], This property refers to the component’s documentation, demos, and tutorials. As a result, we have categorized the properties that help a component’s ’Understandability’ and hence influence its ’Learnability’ under these characteristics. Herein, the ‘Understandability’ attributes will be decomposed into the following: Documentation: This category includes end-user documentation. Attribute assesses the completeness, clarity, and usefulness of user documentation. Documentation for computers. Attributes indicate whether the components provide any kind of documentation that component tools can use to understand their services (eg. User Manual, ERM or DFD). Training: This indicates whether a training course for the software is available. Support: This evaluates the vendor’s level of support through surveys, the web, discussion groups, interviews, and news. ‘Operability’ refers to a software product’s ability to allow the user to operate and control it [22], or the ease with which a program can be operated [3]. The ’Operability’ attributes will be decomposed here into the following: Effort for Operating: indicates the level of effort needed to properly operate the software component. Administrability: indicates the level of effort needed to properly administer the software component. Complexity: measuring the complexity of using and integrating the component into the final system. Herein, the ‘Complexity’ attributes will be decomposed into the Required Interface: the number of interfaces that the OS component requires from other components to operate. The new framework avoids some of the limitations found in other existing frameworks. The new framework disregards quality characteristics that don’t apply to OS components and replaces them with new ones that are. The same new framework has been improved further by identifying new attributes for the framework’s quality sub-characteristics and defining metrics rules to measure the quality of these new attributes for the quality sub-characteristics in the framework, and defining metrics rules to measure the quality of these new attributes. The Figure 2shows the breakdown of the attributes along with their associated metrics and criteria. In this contribution, the framework is tested using the Integrated AHP-SAW Methodology to evaluate and choose the best operating system database product from Oracle 9i and SQL Server 2005. Database Management System, Internet Application Server 9i, Report Builder, Java Database Connection, Application Program Interface, OS Product, Crystal Report, and OS Product. Web Portal is among the Oracle 9iDatabase Components. SQL Server 2005 Database Components include the following: Database Management System, MS ADO, COM object, OS product VB.NET & VB script, OS product Internet Information Services, OS product MS Index, OS product MS Collaboration Data Object for NT as interface towards an SMTP server, and OS product Crystal Report is shown in Table3. Using the Saaty scaling-table, and the AHP six steps, a weight value is assigned for each of the characteris71 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX Table 3: Characteristics and Sub-characteristics of Usability Goal (Level-0) Characteristics (Level-1) SubCharacteristics (Level-2) Attributes (Level-3) Choosing The Best ( Database Software) Usability Operability Understandability Learnability Complexity Effort for Operating. Administrability. Documentation. Training User Support Time to Use Time-toConfigure. User interface tics, namely: ‘Learnability’, ‘Understandability’, ‘Operability’, and ‘Complexity’.. The outcome is shown in Table4, Table5, Table6and Table7Respectively. Table 4: Pairwise Comparisons Judgment for the Sub-Characteristics According to Usability (U),Learnability (L),Understandability (UN),Operability (O),Complexity (C)and Priority (p) ULUN O C P Learnability1.0 2.0 5.0 5.6 0.52 Understandability 0.50 1.0 2.50 2.80 0.27 Operability 0.20 0.40 1.0 1.12 0.11 Complexity 0.18 0.30 0.89 1.0 0.10 CR =0.013 PPriority = 1.0 Table 5: Pairwise Comparisons Judgment for the SubCharacteristics According to ’Time to Use (TU)’ , ’Time to Configure (TC)’ and Priority (P) Learnability TU TC P Time to Use 1.0 2.0 0.67 Time to Configure 0.5 1.0 0.33 CR = 0.0 PPriority = 1.0 Table 6: Pairwise Comparisons Judgment for the SubCharacteristics According to ’Documentation’ (D), ’Training’ (T), and ’Support’ (S) and Priority (P) UnderstandabilityD T S P Documentation 1.0 2.0 2.0 0.50 Training 0.50 1.0 1.0 0.25 Support 0.50 1.0 1.0 0.25 CR =0.0 PPriority = 1.0 Table 7: Pairwise Comparisons Judgment for the SubCharacteristics According to ’Effort for Operating’ (EO)and ’Administrability’(A) and Priority(P) Operability EO A P Effort for Operating 1.0 0.5 0.33 Administrability 2.0 1.0 0.67 CR = 0.007 PPriority = 1.0 About ’Complexity’, a weight value one is assigned to the attribute ’Required Interface’ because the ’Complexity’ sub-characteristic is decomposed into one attribute. 4 Weights Generation Methods with SAW Approach in the AHP The SAW method is used to rank the various database software options. The two database components, Oracle 9i and SQL Server 2005 are used to illustrate this step of our technique. The overall weights of each criterion and Sub-Criteria (Learnability, Understandability, Opreatability, and Complexity). Time to use, Time to configure, Documentation, Training, Support, Effort for operating, Administrability, and Required interface) are calculated by AHP and can thus be used as input to the SAW method, as shown in Table 8. As an outcome, using the scale in Table 1, decision-makers are asked to evaluate the alternatives based on each sub-criterion. as illustrated in Table 9and Table 10, below. Table 8: Input Values of the SAW Analysis TU TC D T S EO A RI Oracle 9i 58788685 SQL Server 2005 8 7 5 9 6 8 6 6 Weight 0.3484 0.1716 0.135 0.0675 0.0675 0.0363 0.0737 0.10 72 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX Al_Nawaiseh-g2igHX,gE valuate Database Management System Quality By Analytic Hierarchy Process (AHP) and ... Table 9: The Normalized Sub-Criteria Weightings Weight (W) , Sub-Criteria(SC) , Weight(W) and Level Two(LT) Criteria W SC W LT Learnability 0.52 Time to Use 0.67 0.3484 Time to Configure 0.33 0.1716 Understandability 0.27 Documentation 0.50 0.1350 Training 0.25 0.0675 Support 0.25 0.0675 Operability 0.11 Effort for Operating 0.33 0.0363 Administrability 0.67 0.0737 Complexity 0.10 Required Interface 1.0 0.1000 PWeight =1.0 PLevel Two =1.0 Table 10: The Normalized Sub-Criteria ’Weightings’ Time to Use (TU), Time to Configure (TC), Documentation (D), Training (T), Support (S), Effort for Operating (EO), Administrability (A) and Required Interface (RI) TU TC D T S EO A RI Oracle 9i 5 8 7 8 8 6 8 5 SQL Server 2005 8 7 5 9 6 8 6 6 Weight 0.3484 0.1716 0.135 0.0675 0.0675 0.0363 0.0737 0.10 The second step is to calculate xij/Max(xij) for each column associated with Oracle 9i and SQL Server, as illustrated in Table 11, below. 5 Results and Discussion The result of the preference value (Vi) for Oracle 9i is the summation (P) of multiplying each element on the first row (R1j) by the corresponding weight in the third row (W3j)As shown in Figure 3. In the same way, the result of the preference value (V i) for SQL Server 2005 is the summation (P) of multiplying each element in the second row (R2j) by the corresponding weight in the third row (W3j). The following formula 3 generalizes the computational concept for any number of OS components, as described in Section 4above. Figure 3: Final Software Attributes. V i =Pwkjrij i = 1. . . n, j = 1. . . m, Where: (k) is the last row number in the matrix, (n)The number of OS components under the evaluation process, and (m).The total number of the corresponding characteristics and sub-characteristics. After computing the normalized priority weights for each Pairwise Comparison Judgment Method (PCJM) of the Integrated AHP-SAW Methodology, the solution to the database selection issue is synthesized. The normalized local priority weights of the characteristics, sub-characteristics, and attributes are combined to generate the global composite priority weights, as indicated in Section VII above. Accordingly, for Oracle 9i, the formula will be applied as follows: V1 = Pw3jrij i = 1, j = 1. . . 8 V1 = 0.828 On the other hand, for SQL Server 2005, the formula will be applied as follows: V2 = Pw3jrij i= 2, j = 1. . . 8V2=0.779 As can be seen, Oracle 9i is the clear victor of this assessment procedure and hence the best OS database component. As a result, by applying the hybrid technique of AHP and SAW and creating a numeric preference value that helps decisionmaking, our methodology overcomes the restriction of prior work as described in the Literature Review. The proposed methodology’s determination is to locate the best database software component among those with access to commercial off-the-shelf (POS) systems by utilizing a suitable decision-making procedure. After testing the aggregations on various process parameters under various conditions, as shown in Sections 3.2 and 3above. 6 Conclusions Our contribution presents an approach application based on a hybrid multi-criteria decision-making process.For order preference, Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) is used. Even though our testing sample only in73 MENDEL — Soft Computing Journal, Volume 23, No.gk, .2+2K#2` 202k, Brno, Czech RepublicX Table 11: Calculation of xij/Max(xij) for Each Column TU TC D T S EO A RI Oracle 9i 0.625 1.0 0.889 1.0 1.0 0.75 1.0 0.625 SQL Server 2005 0.889 0.778 0.556 1.0 0.667 0.889 0.667 0.667 Weight 0.3484 0.1716 0.135 0.0675 0.0675 0.0363 0.0737 0.10 cluded two OS components, the proposed methodology can be applied to any other software selection problem involving multiple OS components and multiple and conflicting criteria [13]; [25]. Furthermore, the hybrid concept in our model, as well as the fact that the preference indication is computed as an explicit numeric value, facilitates decision-making and overcomes the limitations mentioned in the Literature Review Section. MCDM methodologies for further work include Elimination Et Choix Traduisant la Realties’ (elimination and choice expressing reality – (ELECTRE)), Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), Viekriterijumsko Kompromisno Rangiranje (VIKOR), and (TOPSIS). Each of these techniques can be integrated with AHP and used to conduct a comparison based on ‘Usability’ in the same way that this research work has. We think that evaluating and investigating the various outcomes will yield valuable advice for organizational decisionmakers. Furthermore, investigating AHP in a fuzzy environment might be a fascinating area of research [15]. 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