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Selecting Enterprise Architecture Framework Based on the ...

  1. 1. Selecting Enterprise Architecture Framework Based on the Group Evidential Reasoning Approach F. Zandi Faculty of Engineering & Technology, Alzahra University, Iran Abstract Enterprise Architecture (EA) frameworks are methods used in enterprise architecture modeling. These frameworks provide a structured and systematic approach to designing systems. This enterprise modeling helps ensure interoperability of systems and helps control the cost of developing systems. On the other hand, business success is viewed through , the appropriate selection of EA framework. Therefore, this paper proposes a new method to rank the EA frameworks based on the group evidential reasoning (ER) approach in multi-attribute group decision making. Then, the best EA framework is selected using a mathematical programming. The implementation of the proposed method is shown using a case study. Keywords: Multiple Attribute Group Decision Making, Evidential Reasoning approach, Enterprise Architecture Framework 1. Introduction EA integrates strategic, business, and technology planning across the enterprise, as well as providing standards and configuration management capabilities that support the ongoing transition from current to future architectures. EA documents an enterprise's strategic goals, business processes, and technology solutions. On the other hand, the appropriate selection of EA framework can help to lower the risk of EA implementation failure by providing a clear view of current and future technology operating environments and ways in which the EA framework can (or cannot) help to meet strategic goals and business requirements. During the past few years, the selection of EA frameworks has garnered considerable attention from both practitioners and academics in the fields of information systems and business management. Fayad et al [4] have introduced some attributes for selecting a EA framework. United States Chief Information Officer council has described some guidelines and attributes for selecting EA framework. Tang and Han [2] studied and compared the EA frameworks analytically. Although, the selection of EA framework refers to making group decision in the presence of multiple, usually conflicting, attribute and the amount of time needed for making a group decision has a big drawback. But, mathematical approach aspects of selection of EA framework have gained less attention. There are many methods available for solving multi attribute group decision making (MAGDM) problems as reviewed by Hwang and Yoon [6]. As part of the effort to deal with MAGDM problems with uncertainties and subjectivity, the ER has been devised, developed, and finally implemented into window based software called Intelligent Decision System by Yang and his collaborators in a time span of more than 15 years and it is the latest development in MAGDM area. The ER approach uses a extended decision matrix, in which each attribute of an alternative is described by a distributed assessment using a belief structure. The advantages of using a distributed assessment include that it can model precise data and meanwhile capture various types of uncertainties such as probabilities and vagueness in subjective judgments. Also, the ER approach is the only method so far capable of handling MAGDM problems with uncertainties and hybrid nature. Therefore, the aim of this research is to provide insight into the selection of EA framework based on the group ER approach that enables enterprise to gain the best EA framework. This paper is organized into four sections. The following section deals with the introduction of the proposed method followed by a five-step process. This method is then illustrated by a case study. The fourth section summarizes and concludes the paper. 1
  2. 2. 2. The proposed method for ranking EA frameworks Suppose that there are k frameworks, O j , and L attributes, Ai . Let us first define notations of the ER approach: [7] L : Number of attributes K : Number of EA frameworks N Number of evaluation grades H n β n,i : Degree of belief that the attribute Ai is assessed to the evaluation grade H n wi : Weight of the attribute Ai mn ,i : Basic probability mass representing the belief degree to which attribute i is assessed to the evaluation grade H n mH ,i : The remaining belief for attribute i unassigned to any individual grade H n ~ m : The incompleteness of the attribute i H ,i mH ,i : The relative importance of the attribute i u ( H n ) : Utility of the evaluation grade H n U (O j ) : Utility of the EA framework O j S ( Ai (O j )) : The attribute Ai is assessed to the grade H n to a degree of β n,i ×100% for the EA framework O j mn ,I ( i +1) : combined probability masses β n : The combined degrees of belief Steps of proposed method are follows: STEP1 (Calculate the weight of each decision making committee member): The weight of each decision making committee member can be obtained through applying group decision-making techniques such as NGT or Delphi. [6] STEP2 (Calculate the weight of decision making attributes): All of the frameworks and the entire attribute using in ranking all of the frameworks by all of the committee members are determined through applying group decision-making techniques. For example, the following attributes can be considered for selecting EA frameworks: [2] - Management expectation - Support integration of multiple application EA frameworks and legacy components - Compatibility needed with another Agency or joint policy - Compatibility needed with national EA framework - Availability of existing architecture products - Priorities, intended uses and desired level of detail. - Resource and schedule constraints - Mature run-time functionality - Support for extensibility, tailor ability, and customizability, flexibility and scalability - Support for role object pattern and ease of use - Support for separation of concerns - EA Framework Openness - EA Framework supportive tools - make use of standard terms - Employ processes and mechanisms that support systems evolution 2
  3. 3. - Provide consistent standards to document architecture specifications for planning - Ensure development and architecture standards are maintained The pair wise comparison matrix of the attributes can be expressed as follows:  n12 nij   1 ...   n21 n ji   n21  1 ... ...  D =  n12 (1)    ... ... ... ...   n ji ... ... 1   nij    In matrix (1), each element describes the relative importance of one attribute over another. Where: nij : Number of decision makers who preferred Ai to A j Then, using equation (2), the maximum λ can be calculated as: D′ = Det ( D − λI ) = 0 (2) Therefore, the weight vector of decision making attributes, W = [ w1, w2 ,..., wn ] , can be obtained via: D′ * W = 0 )3) STEP3 (Aggregate the committee members' individual matrix): Each element of the Individual ER decision matrixes can be represented as: S ( Ai (O j )) = {( H n , β n ,i (O j )), n = 1,2...., N } i = 1,2,..., L j = 1,2,..., K (4) In order to have weighted individual matrixes, individual matrixes (4) can be multiplied with its associated the weight of each decision making committee member calculated in step1. Then, using cardinal approach, we have the collective matrix, C, which is the aggregation of the committee members' individual matrixes: [6] STEP4 (Rank the EA frameworks): Using equation (6), the probability mass assigned to the whole set of grades which is unassigned to any ~ individual grade H n , is decomposed into two parts, mH ,i and mH ,i [1]. ~ m H ,i = m H ,i + m H ,i (6) Where: N ~ mH ,i = 1- wi , mH ,i = wi (1- ∑β n =1 n ,i ) (7) L 0 ≤ wi ≤ 1 , ∑w i =1 i =1 :Equation (6) can be rewritten as 3
  4. 4. N N mn ,i = wi β n ,i , m H ,i = 1 − wi ∑β n =1 n ,i = 1 − ∑ m n ,i n =1 i = 1,2,..., L (8) Therefore, the combined probability mass can be generated by: mn , I ( i +1) = k I ( i +1) (mn , I ( i ) .mn ,i +1 + mH ,I ( i ) .mn ,i +1 + mn ,I ( i ) .mH ,i +1 ) (9) Where: ~ m H , I ( i ) = m H , I ( i ) + mH , I ( i ) (10) ~ ~ ~ ~ ~ mH ,I ( i +1) = k I ( i +1) (mH , I ( i ) .mH ,i +1 + mH ,I (i ) .mH ,i +1 + mH ,I ( i ) .mH ,i +1 ) (11) mH ,I ( i +1) = k I ( i +1) (mH , I ( i ) .mH ,i +1 ) (12) N N k I ( i +1) = [1 − ∑∑ mt ,I ( i ) m j ,i +1 ]−1 t =1 i =1 j ≠t (13) Thus, the aggregated degree of beliefs can be expressed as: βn = m n,I(L) n = 1,2,..., N 1 − mH , I ( L ) (14) ~ β H = mH , I ( L ) (15) 1 − mH , I ( L ) Finally, using equations (5) and (14), the average weighted assessment degrees (utilities) of EA framework j can be obtained as: N u( O j ) = ∑ u( H n =1 n )β n (16) Now, the EA frameworks can be ranked based on utility function (16). This proposed method can be implemented using IDS software. STEP5 (construct mathematical programming): To select the best EA framework, It can be constructed the following mathematical programming: n Max Z = ∑ U (O )X j =1 j j (model P) St: n ∑a j =1 ij X j ≤ bi i = 1,2,..., m n ∑j =1 Xj=1 X j =0, 1 Where: aij and bi represent the required resource for each EA framework and the maximum of resource, respectively. 3. Case study 4
  5. 5. In the Institute of Energy and Hydro Technology (IEHT), the institute decision making committee members have distinguished the most important attributes for selecting EA framework as follows: 1. EA Framework maturity 2. Ease of use 3. EA Framework completeness 4. EA Framework openness 5. EA Framework supportive tools 6. Compatibility with the business priorities and scope The suggested EA frameworks have been shown in the table 1. Also, the managing board of the :institute has two constraints .a- The enterprise architecture process planning period must be less than 6 months .b- The maximum enterprise architecture planning process cost is $80000 Table 1: Given data The suggested EA frameworks ZACHMAN FEAF TEAF TOGAF DODAF The enterprise architecture process 5 6 4 3 6 planning period Enterprise architecture planning process 50000 60000 45000 40000 75000 cost Solution: Step 1: Assume that the weight of each decision making committee member is equal. Step 2: According to the pair wise comparison of attributes, we have: 1 6 5/7 6 5 6 1/ 6 1 4/7 4/6 5 3 7/5 7/4 1 5 5 5/2 =D 1/ 6 6/4 1/ 5 1 4/3 6 1/ 5 1/ 5 1/ 5 3/ 4 1 4/3 1/ 6 1/ 3 2/5 1/ 6 3/ 4 1 The largest eigenvalue of matrix D and its associated eigenvector as the weight vector of decision making attributes have been calculated by MATLAB software as follows: λmax =6.81 W = (0.07, 0.19, 0.11, 0.19, 0.11, 0.11) Step 3: Suppose that we have the following four evaluation grades: H= {slightly preferred, moderately preferred, preferred, greatly preferred} = {0, 0.3333, 0.6666, 1} Step 4: Using IDS software, the five EA frameworks utilities are according to the table 2: Table 2: the utilities and rank of EA frameworks Rank EA Framework Utility number 5
  6. 6. 1 FEAF 0.7667 1 ZACHMAN 0.7467 3 TOGAF 0.6467 4 TEAF 0.5000 5 DODAF 0.4933 .Therefore, FEAF framework has the most utility :Step 5: To consider the mentioned constraints, zero-one linear programming is formulated as follows X DODAF Max Z = 0.7667 X FEAF + 0.7467 X ZACHMAN + 0.6467 X TOGAF + 0.5 X TEAF + 0.4933 :St 6 X FEAF +5 X ZACHMAN +3 X TOGAF +4 X TEAF +6 X DODAF ≤ 6 60000 X FEAF +50000 X ZACHMAN +40000 X TOGAF +45000 X TEAF +75000 X DODAF ≤ 80000 1 = X DODAF + X TEAF + X TOGAF + X ZACHMAN + X FEAF 0,1= X DODAF , X TEAF , X TOGAF , X ZACHMAN , X FEAF After solving the model by LINDO software, TEAF is considered as the best EA framework for IEHT. 4. Conclusions The researcher has presented a method to select the EA framework based on the group ER approach. This new approach has the following advantages: 1-It is constructed based on a well-known group ER approach which can be easily calculated using IDS software. 2- The best EA framework can be identified using the proposed method. 3-By comparing the traditional group decision making with the proposed method for selecting the EA framework, time saving and the better solutions can be concluded. The proposed method explained in this paper can be extended to IT project management. References [1] Xu, Dong-Ling and Yang, Jian-Bo, "Intelligent decision system based on the evidential reasoning approach and its applications", journal of telecommunication and Information Technology, 2005. [2] Tang, A., Han, J. and Chen, P., "A Comparative Analysis of Architecture frameworks," Technical Report, August, 2004. [3] Graham, G. and Hardaker, G. "Contractor Evaluation in the Aerospace Industry Using the Evidential Reasoning Approach," Journal of Research in Marketing & Entrepreneurship, P.P. 161-173., vol.3, 2001. [4] Fayad, M. and Hamu, D. "Enterprise Frameworks: Guidelines for selection," ACM Computing Survey, Vol. 31, No. 1, March 2000 [5] Goethals, F. and SAP, L. "An Overview of Enterprise Architecture Framework Deliverables," Retrieved from the site below: http://www.econ.kuleuven.ac.be/.../Goethals Overview existing frameworks. PDF [6] Hwang, C.L. and Lin, M.J. "Group Decision Making under Multiple Criteria, Lecture Notes in Economics and Mathematical Systems"; Springer, March, 1981. Hugnh, V. and Nakamori, Y., "Multiple-Attribute Decision Making under Uncertainty: The [7] .Evidential Reasoning Approach Revisited," IEEE Transactions on Systems, Vol. 36, No. 4, 2006 6

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