Set. algorithms based. 1. Introduction. System Diagram. based. Exploration. 2. Index

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1 ISSN (Prnt): ISSN (Onlne): IT outsourcng servce provder dynamc evaluaton model and algorthms based on Rough Set L Sh Sh 1,2 1 Internatonal School of Software, Wuhan Unversty, Wuhan, Chna 2 School of Informaton Management, Hube Unversty of economcs, Wuhan, Chna Abstract The tradtonal suppler evaluaton methods mostly belong to the statc evaluaton, and the actual nformaton servce provder selecton s a dynamc process, need to use dynamc evaluaton method to measure. In order to select the IT outsourcng servce provder, the tool whch uses complex scentfc management system-thnkng - Exploraton dagram, establshed the ndex system for the selecton of IT outsourcng. Based on t, the decson table of dynamc IT-Outsourcng servce provder selecton s made. The decson rule set of IT outsourcng servce provder predcton s obtaned by applyng rough set theory of the decson table attrbute reducton and value reducton. Fnally, a calculaton example of IT outsourcng servce provder selecton s llustrated,whch shows that the mentoned evaluaton method s feasble and effcent for dynamc IT outsourcng servce provder selecton and predcton. Thus, t supples reasonable analyss and polcy makng of IT outsourcng servce provder. Keywords: IT outsourcng; Servce evaluaton; Exploraton dagram; reducton; rules ganng 1. Introducton Wth the development of Informaton Technology and thnkng of outsourcng, enterprse and researcher pay more attenton on the IT outsourcng. Domestc and foregn scholars from dfferent angles on nformaton technology outsourcng are dscussed. The nformaton technology outsourcng s a new management method and t wll be an effectve way for all mddle and small scale enterprses to realze nformatonzaton. Currently, 60% of U.S. companes wth professonal IT outsourcng servces rapdly expand ther own busness [1-2]. However, IT outsourcng s a very complex busness processes and accompaned by a varety of rsks, results show that IT outsourcng success s stll to be mproved. Among them, the one of the greatest rsks s the selecton of servce provders. [3]. The tradtonal suppler evaluaton methods mostly belong to the statc evaluaton, and the actual nformaton servce provder selecton s a dynamc process, need to use dynamc evaluaton method to measure. There s no unfed framework of IT outsourcng servce provder evaluaton ndex system n the present study. And evaluaton s often focused on the current stuaton, unable to evaluate the future contnuous development of IT outsourcng servce provder. So, exploraton dagram tool s put forward n ths paper, t use the complex scentfc management system thnkng mode to apply IT outsourcng servce provder evaluaton, absorb ts systematc, modular and vsual thnkng ways of thnkng [4-5]. Through ths method, IT-outsourcng servce provder evaluaton of nfluence factors are found, and the evaluaton ndex system s establshed. IT outsourcng servce provder evaluaton about an enterprse s taken as an example to set up the IT outsourcng servce provder evaluaton decson table, the decson table s conducted by applyng rough set theory of the decson table attrbute reducton and value reducton. And IT outsourcng servce provder of the evaluaton results decson rule set s obtaned. Accordng to t, the dynamc IT outsourcng servce provder evaluaton results and development trend are gven. It provdes the bass for enterprses to select the sutable IT outsourcng servce provder. 2. Index System Dagram based on Exploraton Maor Exploraton dagram s the system thnkng tool, whch manly helps to solve the problem of how to conduct vsual thnkng. Exploraton dagram can help us to solve such knd of decson makng problems: advance unknown, such as new product development decsonmakng, nvestment proect evaluaton and decsonmakng, through the creaton of Exploraton dagram to fnd all the factors that affects or may affect the research topc, helpng to make decson. Exploraton dagram va the researchers to the whole envronment of observaton, accordng to own mastery of knowledge and nformaton, plus full magnaton, apply the pont of bgger envronment consderaton to created a map. The pcture presents all affects or may affect the Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 ISSN (Prnt): ISSN (Onlne): subect factors, at the same tme, t also reflects the relatonshp of factors, and ts formaton s a collectve creaton process. Exploraton dagram use ellpse to represent factors, ellptc bdrectonal arrow ndcates nteractve relatonshp, ruleless crcles represent the same factors, and lgature used to connect factors of the same class, a tal on the ellpse ndcates deletng factors. The process of drawng Exploraton dagram for Evaluaton of IT outsourcng servce provders s as follows: Step 1: Accordng to the needs of the research theme or decson-makng problems, the relevant experts convened to dscuss the research topc or decson-makng problems. Step 2: Moderator or the responsble rase questons n accordance wth the research topc or the queston of the decson-makng, allowng you to thnk.ths artcle manly dscusses how to evaluate and select IT outsourcng servce provders. Suppler capablte s Frm sze R&D capabltes Fnancal condton Input cost Marke t share Engneerng level Level of Servce Credt Level Tme to Resolve User Complant s Input staff Fg. 1: classfed exploraton dagram External Evaluaton Perfor mance Status PM qualty On-tme Delvery Rate Step 3: Each member gve full play to ther magnaton and express ther vews. Step 4: After they have fully express ther vews, everybody are guded to conduct the vsual thnk about all the proposed factors amed at these oval, startng from the overall, ntegrated the same, Elmnate redundant, connect the factors of the same class wth lgature, use the ruleless crcles to enclose the same type factors, plus a tal n the oval for the deleted factors. the classfed exploraton dagram s shown n fg.1. Step 5: To further the vsual thnkng, named each rregular crcle, Such as commodty prces, types of goods, commodtes performance, and Brand and commodty pcture nformaton can be classfed as commodty nformaton. The namng of ths category s only an ntal name, can eventually named after the analyss of the causal assocaton. Based on above, IT-outsourcng servce provder selecton ndex system s obtaned and shown n table 1. Table 1:IT-outsourcng suppler selecton ndex system Evaluat Factor Charac Specfc ndcators on target ndcators ter Quantt Suppler Frm sze (C1) atve capabltes R & D capabltes Qualtat (B1) (C2) ve Quantt IToutsourc Market share (C3) atve On-tme Delvery Quantt ng Level of Rate (C4) atve suppler Servce Tme to Resolve selecton Quantt (B2) User Complants (A) atve (C5) External Evaluaton (B3) Credt Level (C6) Performance Status (C7) Qualtat ve Qualtat ve 3. Applcaton of Rough Set Theory n IT Outsourcng Servce Provder Evaluaton 3.1 Rough Set Theory Rough set theory [6-7] s a new mathematcal tool to deal wth mprecse, ncomplete and nconsstent data. It can effectvely analyze each knd of ncomplete nformaton such as mprecse, nconsstent, not ntegrty and so on, but also dscovers the concealed knowledge and promulgates the latent rule accordng to analyzng and reasonng data. Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 ISSN (Prnt): ISSN (Onlne): In the rough set theory, computaton of approxmatons and edge and attrbutes reducton of decson table s mport part of them. Decson rules can be mned from gven data usng rough set theory. Comparng to others theores whch processes ndefnte and mprecse queston, the most remarkable dfference s t does not need outsde the data acquston whch provdes the queston to examne the nformaton, therefore t s qute obectve to the ndefnte descrpton or processng questons. Because ths theory has not been able to contan processng mprecse or ndefnte prmary data mechansm, so ths theory and the theory of probablty, the fuzzy mathematcs and the evdence theory and so on other theory whch processes ndefnte or mprecse questons are complementary. 3.2 The Concepts of Rules Ganng In ths secton we descrbe the concepts of rules ganng. Reducng the decson tables, searchng the mnmal attrbute subsets and gettng the succnct decson rules are not only the basc but also the toughest problems of Rough Set theory. The man dea of the theory of rough sets s to fnd out decson makng and classfcaton rules through knowledge reducton wthout changng the classfcaton capacty of the nformaton system. The basc thought that dscovers the classfed rule n the decson support system based on rough set theory as follows: Step1: the user proposes the duty of dscover. The user takes some or many attrbutes as the classfed polcymakng attrbutes n the database, accordng to dfferent values of these attrbutes, the data dvdes nto the dfferent category n the database, the duty of dscover s produces these dfferent determnaton rules. Step2: usng the algorthm based on rough set theory for ganng classfcaton rules. Some defntons are gven follows: Defnton1: Dscernblty Matrx [8-11].A mathematcan named Skowron n Warsaw Unversty proposes a dscernblty matrx. There s a nformaton system S U, A, V, F U = x x,..., s the unverse of = ( ), { } 1, 2 dscourse, A s the attrbute set, A = C D, C s the condton attrbute, D s the polcy-makng attrbute, a( x) s a value wth x on the attrbute a, the resoluton matrx s c ( ) = a A 0 1 : a a x n ( x ) a( x ) D( x ) D( x ) φ D( x ) = D( x ) ( x ) = a( x ) D( x ) D( x ) Defnton 2: Equal Class. Regardng an attrbute set B A n nformaton system S = ( U, A, V, F ), f t satsfes IND( B) = {( x, y) UXU a( x) = a( y), a B }, called dual relates that can t then equal relates IND( B) dstngush each other. B( x) obect x n t. express a equal class whch Defnton 3: CORE [9]. Opposte to attrbute set D, core s an attrbute set, whch s ntersect of reducton belongs to the attrbute set C, records s CORE (C, D). The core s these attrbutes wth ts group dvsble number s 1 n dscernblty matrx. Defnton 4: Reducton [12]: U s the unverse of IND R s dscourse, R s an equal relatonal race, r R, ( ) the ntersect on of equal relaton n R, f U / IND( R) equal to U IND( R { r} ) /, then r s may be canceled n R. Otherwse r s not be canceled n R. If any element n p ( P = R { r} ) s not beng canceled, then called P s the reducton of R. Defnton 5: Equal set descrpton: f an equal set named E, then we descrpt ts character by usng Des( E ) ( a = v) =. a A, v Va Defnton 6: Rules ganng [13]. Assume that the dvson ' of A n U s E, the dvson of A s Y. E Look upon as the classfcaton condton; Y looks upon as the classfcaton concluson. We may get classfed rule as follows: (1) If φ E, then we get r : Des( E ) Des( Y ) Y a. If E Y = E, then, the rule r s ascertaned. Rule confdence level s one ( cf = 1 ). b. If E Y E, then, the rule r sn t ascertaned. E Y Rule confdence level s cf = E (2) f E = φ, there sn t establsh rule. Y When the rule confdence level s one, ths knd of rule can smplfy. The rule reducton s that some attrbutes are deleted from condton attrbutes; the rule confdence level was stll one. s Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 ISSN (Prnt): ISSN (Onlne): Model of IT Outsourcng Servce Provder Evaluaton based on Exploraton Dagram and Rules Ganng IT outsourcng servce provder evaluaton results can be dvded nto good, general, bad. Accordng to the ndex system by applyng explore dagram, selectng evaluaton data of provder, usng rough set theory to nput space dmenson reducton, completng nput feature extracton work, t can acheve purpose: reduce the sze of the data processng. The more sample data, the more the rules the hgher relablty are. When the new suppler need to evaluaton, usng decson rule set, t can be evaluated. The whole processon s shown n Fg.2. Now we descrbe the algorthm based on rough set for rules ganng. Step 1: Foundaton data processng. In ths step, we need nput S = ( U, A, V, F ), then, accordng to gven classfcaton method, data are standardzed. Sample data New provder data New data standardzed Index system by exploraton dagram Standardze data Decson table Reduce attrbute and value Rule set of IT servce provder output predct Step 2: The smallest attrbutes set s obtaned. The algorthm as follows: Begn For =1 to n For = to n c /* ( c( ) M= [ ( )] ( x ) a( x ) D( x ) D( x ) D( x ) = D( x ) ( x ) = a( x ) D( x ) D( x ) a A : a = 0 1 a End for End for P={unon of sngle attrbute n M.} End φ ) */ Step 3: Rules ganng and reducng. When attrbutes are reduced, we get a new polcy-makng table. Frst, equal sets of condton attrbutes and equal sets of polcy attrbutes are obtaned. Second, accordng to defnton sx, we can gan rules and reduce rules. At last, the same conclusons of rules are unon. Fg. 2: The whole processon 4. Analyss of Example In ths secton, an example n IT-outsourcng servce provder selecton s ntroduced to confrm the algorthm s valdty. Suppose a company has ten IT-outsourcng servce provders to be evaluated, we use model n secton 3 to gan the rule and predct IT-outsourcng servce provders. Step 1: Foundaton data processng. Each column of the attrbute value s dvded nto three grades: 3-good, 2- general, 1-bad. The result s dvded nto three grades: 1- bad, 2-general, and 3-good. Table 2 s a dmenson untze polcy-makng table. Table 2: Polcy table ID C1 C2 C3 C4 C5 C6 C7 D Step 2: Obtanng the smallest attrbutes set. Accordng to defnton three and step two from secton 3, we use algorthm to calculate dscernblty matrx M, and obtan the smallest attrbutes set P. In order to smply calculaton, Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 ISSN (Prnt): ISSN (Onlne): we use the computer programmng to solve t. Program nterface s shown n Fg.3. We can get P = { c1, c2, c7}. Table 3 s Polcy-makng table of reducton. s 1. When the new suppler need to evaluaton, usng decson rule set, t can be evaluated. 5. Conclusons In the paper, accordng to the ndex system by applyng explore dagram, selectng evaluaton data of provder, usng rough set theory to nput space dmenson reducton, completng nput feature extracton work. An algorthm based on rough set for rules ganng s ntroduced to analyze and process data, mnmal decson-makng rules are proposed n IT outsourcng servce provder selecton. a calculaton example of IT outsourcng servce provder selecton s llustrated, whch shows that the mentoned evaluaton method s feasble and effcent for dynamc IT outsourcng servce provder selecton and predcton. Fg.3: Program nterface Table 3: Polcy-makng table of reducton ID C1 C2 C7 D Step 3: Rules ganng and reducng. Accordng to defnton 6 and step three from secton 3, we can get the last polcy. When cf = 1, polcy rules are below: (1) c1 = 1 c2 = 2 c7 = 2 f1 (2) c7 = 1 ( c1 = 3 c2 = 1 c7 = 2) f 2 (3) c2 = 3 ( c1 = 1 c7 = 3) f 3 The frst rule shows that, f one servce provder s frm sze s bad, and R&D capablty s general and performance status s general, we can deem ths provder s bad, and rule confdence level s 1. The second rule shows that, f one servce provder s performance status s bad, or frm sze s good, and R&D capablty s bad, and performance status s general, we can deem ths provder s general, and rule confdence level s 1. The thrd rule shows that, f one servce provder s frm sze s bad, and R&D capablty s good, or performance status s good, w can deem ths provder s good, and rule confdence level Acknowledgments The author wsh to thank Scence and Technology Research Funds of Educatonal Mnstry of Hube Provnce(Grant B ), under whch the present work was possble. References [1] Lacty, M., Khan, S., Yan, A., and Wllcocks, L. A Revew of the IT Outsourcng Emprcal Lterature and Future Research Drectons, Journal of Informaton Technology, Vol. 25, no.4, 2010, pp [2] Guofeng Tang, Bn Dan, Han Song,Xume Zhang, "Research on Incentve Mechansm of Applcaton Servces Outsourcng Under Asymmetrc Informaton", IJIPM: Internatonal Journal of Informaton Processng and Management, Vol. 3, No. 4, 2012, pp [3] Lacty M C, Wllcocks L P. Insde Informaton Technology Outsourcng: A State-of-the-Art Report, Oxford Insttute of Informaton Management, 2000 [4] Xu Xusong, Zheng Xaong. Qualtatve Analyss Tools: Probe Graph, Cycle Graph and Structure Graph, Technology Economcs, Vol. 29, no. 5, 2010, pp.1-6, [5] Xu Xusong, Zheng Xaong. Analyss Framework for Combnaton of Qualtatve and Quanttatve, Technology Economcs, Vol. 29, no. 4, 2010, pp.1-6. [6] Thanh Nguyen Ch, Yamada Koch. Document representaton and clusterng wth wordnet based smlarty rough set model[j]. Internatonal Journal of Computer Scence Issues, vol.8, no. 5, 2011, pp.1-8. [7] Pawlak Z. Rough classfcaton [J]. Internatonal Journal of Human-Computer Stdes, vol. 51, 1999,pp [8] Wang Jue, Mao Duo-qan, Zhou Yu-an. Rough set theory and ts applcaton: a survey. Pattern Recognton and Artfcal Intellgence, vol.9, no. 4,1996, pp [9] Hu X,Cercone N. Learnng n relatonal databases: a rough set approach. Internatonal Journal of Computatonal Intellgence, vol. 11, no. 2, 1995, pp Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

6 ISSN (Prnt): ISSN (Onlne): [10] Mao duoqan,wang ue.an Informaton Representaton of the Concepts and Operatons n Rough Set Theory[J]. Journal of software, vol. 2, no. 10, 1999, pp [11]L Sh, Hong Wang, Improvement of the Intellgent Decson-Makng Model based on AHP-Fuzzy Method [J]. Proceedngs of 2006 Internatonal Conference on Artfcal Intellgence, 2006, 8, pp [12]Lu Qng, Rough Set and Rough Reasonng[M].Beng: Scence Press,2005, pp [13] Acharya, D.P, Ezhlaras, L. A knowledge mnng model for rankng nsttutons usng rough computng wth orderng rules and formal concept analyss[j]. Internatonal Journal of Computer Scence Issues, vol.8, no. 2, 2011, pp L Sh s a lecturer n School of Informaton Management of HuBe Unversty of Economcs. Currently, she s a PhD student n nternatonal school of Software of WuHan Unversty. She has authored a number of dfferent ournal and paper. Her research nterests nclude servce scence, data mnng, and decson support. She s a member of CCF. Copyrght (c) 2013 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

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