Research on Engineering Software Data Formats Conversion Network

Transcription

1 2606 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER 2012 Research on Engneerng Software Data Formats Converson Network Wenbn Zhao School of Instrument Scence and Engneerng, Southeast Unversty, Nanng, Jangsu, , Chna E-mal: Zhengxu Zhao School of Informaton Scence and Technology, Shazhuang Tedao Unversty, Shazhuang, Hebe, , Chna E-mal: Abstract Dsperse engneerng nformaton consttutes a doman of complex relatonshp, nto whch researchng wth complex network could contrbute sgnfcantly to ts organzaton and preservaton. A complex network whch takes engneerng software data formats as nodes s constructed and ts statstcal characterstcs, such as drected edge or undrected edge, the node number, the edge number, average path length, clusterng coeffcent and node degree dstrbuton, etc, are analyzed n ths paper. A Vorono dagram based complex network vsualzaton and retreval method s provded. Accordng to statstcal characterstcs of constructed network, vsualzaton method calculates network nodes poston n a two-dmensonal plane and dvdes ths plane nto Vorono dagram by feature nodes of network nodes clusterng and network nodes. Retreval method restrcts compared nodes to nodes n Vorono doman of feature nodes whch are more related to query node, the number of compared nodes s reduced n retreval process. The experment result ndcates applyng ths method nto data formats converson path retreval can ensure retreval precson and mprove retreval effcency, thus provde relable bass for mgratng numerous dsperse engneerng nformaton. Index Terms nformaton preservaton, data mgraton, format converson, complex network, Vorono dagram I. INTRODUCTION Along wth the rapd development of computer and network technology, nformaton ncreases more quckly than demand for nformaton [1][2]. In engneerng felds, such as automoble, shpbuldng, aerospace, mltary technology, etc., for mantenance, troubleshootng and retroft n product lfetme, orgnal desgn data and engneerng nformaton n producton process must be persevered for a long perods of tme. At present, accordng to STEP ISO Standard, the nformaton of desgn and manufacture was stored n threedmensonal CAD model by some nformaton systems, such as CAD, CAE, CAM, PDM, etc. [3]. But these systems lfetme s generally shorter than ther products Copyrght credt: Ths work was supported by Natonal Natural Scence Foundaton of Chna (No ), correspondng author ZHAO Zheng-xu. lfetme, some years later most of systems wll no longer exst. So the use of nformaton wll encounter the compatblty problem between data and systems. On the other hand, take ASD-STAN s LOTAR proect for example, t manly consders how to cope wth the ncrease of nformaton quantty and dsperstveness n enterprse management, product desgn and producton process, meanwhle n order to meet varous requrements of dfferent users n dfferent perods, t manly researches the generaton and long-term preservaton of engneerng nformaton. The manufacturng enterprses accumulate numerous engneerng models of smlar threedmensonal CAD model every year, these models are wrte-protected and stored wth ther own data formats. To ensure the long-term preservaton and data relablty, these models must be regularly nspected, mgrated and transformed. But these processes not only have compatblty problem, but also encounter effcency problem of nformaton process. For these two problems, there are no feasble soluton and technology both at home and abroad. To realze compatblty of engneerng nformaton formats, engneerng software data formats converson should be consdered. A complex network whch takes engneerng software data formats as nodes s constructed and ts statstcal characterstcs whch nclude drected edge or undrected edge, the node number, the edge number, average path length, clusterng coeffcent and node degree dstrbuton, etc, are analyzed n ths paper. A Vorono dagram based complex network vsualzaton and retreval method s provded. Accordng to statstcal characterstcs of constructed network, vsualzaton method calculates network nodes poston n a two-dmensonal plane and dvdes ths plane nto Vorono dagram by feature nodes of network nodes clusterng and network nodes. Retreval method restrcts compared nodes to nodes n Vorono doman of feature nodes whch are more related to query node, the number of compared nodes s reduced n retreval process. The experment result ndcates applyng ths method nto data formats converson path retreval can ensure retreval precson and mprove retreval effcency, thus provde relable bass for mgratng numerous dsperse engneerng nformaton. do: /sw

2 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER II. RELATED WORK A. Engneerng Software Data Formats Converson Engneerng nformaton have some characterstcs such as complex form and scattered content, so structure relevance and data compatblty drectly nfluence the management and use of dgtal engneerng nformaton resources. Especally n the long-term safety preservaton and relable nvoke, there are some problems such as compatblty between nformaton and system, effcency of nformaton nspecton, mgraton and converson. Usng complex network research nto the complcated relatonshp between engneerng nformaton could contrbute sgnfcantly to ts organzaton and preservaton. Zhao studed complex relatonshp between data formats wth small world model, establshed a data formats converson network graph whch takes softwaresupported data formats as nodes and software as edges, and analyzed statstcal characterstcs of complex network, such as average path length, clusterng coeffcent and node degree dstrbuton [4-7]. B. Vorono Dagram Vorono dagram s one of typcal problems n computatonal geometry and one of basc data structures about space neghborng relaton. It was put forward by G.F.Vorono, Russan mathematcan when he researched the neghborng problem. Vorono dagram s one of data structures close to natural phenomenon, so t s appled to some felds whch are related to geometrcal nformaton, such as GIS, geometrcal reconstructon, mage processng and pattern recognton, physcs, chemstry, etc. [8][9][18]. But there are few researches n some felds whch are unrelated to geometrcal nformaton, such as nformaton space partton. René and Stanslav analyzed the research statue of nformaton space partton, and ponted out two mportant evaluaton crterons, ncludng the poston of nodes and the rato of area weght [10]. Chen and Schuffels, etc provded an ET-map method whch use dfferent poston and color to represent web pages [11]. Andrews and Kenrech, etc put forward an InfoSky method whch takes news artcles as 2-dmendonal nformaton space, and use weghted Vorono dagram to dvde ths space [12]. An adaptve weghted Vorono graph partton method s proposed and used to dvde the web nformaton space [10]. III. COMPLEX NETWORK AND VORONOI DIAGRAM A. Complex Network 1) Presentaton of Complex Network Network can be expressed as graph mathematcally, so complex network also can be descrbed wth language and symbol n graph theory. It can be defned as a graph G= (V, E) whch s compose of the node set V and the edge set E, where the number of nodes s N= V, the number of edge s M= E. Each edge n E s corresponded to a par of nodes n V[13]. Accordng to the dfference of network edge defnton, network can be defned as drected network graph and undrected network graph, weghted network graph and un-weghted network graph, as well as smple graph and complete graph n graph theory. Complex network manly researches the relatonshp between mcroscopc characterstcs of nodes and edges n the network and macroscopc propertes. 2) Average Path Length The dstance between two nodes n network s the edge number of the shortest path whch connects them. The dameter of network s the maxmum of dstance between any two nodes. Average path length s defned as the average of dstances between all nodes, also called characterstc path length. 1 L = 1 d ( 1) < nn 2 Average path length descrbes the separaton degree between nodes n network. In complex network, most real networks own smaller average path length, t s smallworld property. 3) Clusterng Coeffcent Clusterng coeffcent s used to measure the aggregaton of nodes n network. Suppose node v s connected wth k nodes by k edges. There can be maxmum k (k-1)/2 edges between k nodes, and the actual number of edges s EI. Clusterng coeffcent C of node v s defned as the rato of network edges actual number to ts maxmum. E C = k ( k 1)/2 Clusterng coeffcent C of the whole network s the average of all nodes clusterng coeffcents, and 0 C 1. Clusterng coeffcent represents dstance relatonshp between all nodes n the whole network. 4) Degree and Degree Dstrbuton The degree of node s defned as the number of edges whch connect ths node n graph theory. In drected network the degree of node s dvded nto out-degree and n-degree. Out-degree s the number of edges whch s drected from ths node to other nodes, n-degree s the number of edges whch s drected from other nodes to ths node. In drected network the degree of node equal to the sum of out-degree and n-degree. The average of all nodes degree s defned as the average degree of network, recorded as <k>. The dstrbuton of nodes degree s descrbed wth functon p (k), ts meanng s the probablty that an arbtrary node s connected wth k edges, and equal to the rato of nodes number whch s connected by k edges to nodes total number. In order to avod error caused by smaller network, P (k) s used to represent the cumulatve dstrbuton functon of p (k). PK ( ) = pk ( ') k' = k (1) (2) (3)

3 2608 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER 2012 P (k) represents the probablty dstrbuton of nodes whose degree are not less than k. Degree s a basc parameter whch descrbe structural characterstc of network nodes, and reflects macroscopc statstcal property of network system. There are some statstcal characterstcs n complex networks, such as the connectvty of network, the relevance of nodes, modularty, etc. B. Vorono Dagram Let p and q are two dscrete ponts n plane, L s the perpendcular bsector of lne pq and dvdes the plane nto two parts L p and L q. Ponts n the L p satsfy d (x, p)< d (x, q), where d s Eucldean dstance. L p s expressed as dom (p, q)={x R 2 d (x, p)<d (x, q)}, and L q s expressed as dom (q, p), as shown n Fgure 1. Fgure 3. Vorono Dagram. p L L p L q Fgure 1. Dagram of L p and L q. q IV. ENGINEERING SOFTWARE DATA FORMATS CONVERSION NETWORK Engneerng software data formats converson network s a complex network whch takes engneerng software data formats as nodes and s based on the converson relatonshp between data formats. It s defned as G= (V, E), whch expresses the topologcal relatonshp between data formats. V={v =1, 2,, n} s the node set of engneerng software data formats, If data format v V can be converted to data format v V, a drected edge e (v, v ) s created n network graph. Matrx W s the adacency matrx of network graph G. If a drected edge exsts between v and v, w =1, otherwse w =0. TABLE I. ENGINEERING SOFTWARE AND THE NUMBER OF DATA FORMATS p Fgure 2. Vorono polygon of p. Let S be a set of n ponts n plane, S = { p 1, p 2,..., p n }. reg (p )= dom (p, p ), where p S -{ p }, reg (p ) s more close to p than other ponts, and s the ntersecton of n-1 half plane. It s a convex polygon that has no more than n-1 edge, whch s called p s Vorono doman or Vorono cell[8], As shown n Fgure 2. Each pont n set S may be correspondng to a Vorono doman, the dagram of n Vorono domans s called Vorono dagram, V (S), as shown n Fgure 3. The vertex and edges are respectvely called Vorono vertex and Vorono edge. Software Data Data Software Formats Formats AdobePhotoshop 124 MATLAB 44 CorelDRAW 122 McrosoftVso 42 Pro/E 106 McrosoftOffce Access 40 Macromeda PLSQL 93 Dreamweaver Developer 32 McrosoftOffce Mcrosoft 85 PowerPont VsualBasc 25 SoldWorks 75 MIS 21 CATIA 68 WnRAR 15 ACDSee 68 ANSYS 13 Geomagc 67 VRPlatform 13 Nero 66 Vrtools 12 Mcrosoft VsualC++ 65 Queat3D 12 3dsMax 62 CAJVewer 12 UGSNX 61 LabVIEW 10 McrosoftOffce Mcrosoft 60 Excel SQLServer 10 Macromeda Flash 60 EndNote 10 AdobeFlash 59 AdobeReader 8 McrosoftOffce Word 53 FoxtReader 3 MacromedA Freworks 53 Notepad 2 AutoCAD 48 Ths paper selects two-dmensonal and threedmensonal graphc desgn software, engneerng

4 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER smulaton software and data document processng software and statstcs ther nput and output formats. Table 1 shows engneerng software and the number of ther data formats. Accordng to statstcal engneerng software and data format converson relatonshp, engneerng software data format converson network s constructed, as shown n Fgure 4. In the network constructng process, only software and data formats are consdered, data storage duraton, software lfetme and data format term are consdered as deal condton, namely at the same tme. Fgure 5. Relatonshp between Average Path Length and Nodes' Number Fgure 4. Engneerng Software Data Formats Converson Network Ths paper analyzes engneerng software data format converson network by the statstcal characterstcs of complex network, such as average path length, clusterng coeffcent and degree dstrbuton. Table 2 show the number of nodes, average path length, clusterng coeffcent and network dameter of engneerng software data format converson network. TABLE II. STATISTICAL CHARACTERISTICS OF ENGINEERING SOFTWARE DATA FORMATS CONVERSION NETWORK The number of nodes Average Path Length Clusterng Coeffcent Dameter Fgure 5 show the relatonshp between average path length and the number of nodes. The ncrease of become gradually slow, t ndcates engneerng software data format converson network follows small world property, average path length grows at logarthm type or lower logarthm type wth network sze or number of network nodes. Fgure 6. Relatonshp between Clusterng Coeffcent and Nodes' Number Fgure 6 show the relatonshp between clusterng coeffcent and nodes' number. When the number of nodes ncreases gradually, clusterng coeffcent of network fluctuates n small-scale. Therefore, clusterng coeffcent s rrelevant to network scale, and t s consstent wth WS model and Newman model. Fgure 7 show the probablty dstrbuton of nodes degree. Nodes, whose degree s less than or equal to 180, are account for about 90% of the total number of nodes. The average of nodes degree s The degree of nodes can express the mportance of nodes n a sense. In ths experment, bmp, pg, gf, pcx and tf have hgher mportance. In order to take nto account the mportance of these nodes, the cumulatve probablty dstrbuton s used to descrbe nodes degree, as shown n the Fgure 8. Nodes whch have hgher degree are less, but they are the key node of network. Fgure 7. Dstrbuton Curve of Nodes Degree

5 2610 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER 2012 In Fgure 8, the cumulatve degree of nodes n engneerng software data format converson network follows exponental dstrbuton. Through curve fttng, the result s as shown n Fgure 9. Nodes cumulatve degree follows exponental dstrbuton curve P (k)=0.7502e k. Fgure 8. Dstrbuton Curve of Nodes Cumulatve Degree Fgure 9. Fttng Curve of Nodes Cumulatve Degree In sem-logarthmc coordnate, the dstrbuton of these nodes cumulatve degree s approxmately consdered as a straght lne, but n double-logarthmc coordnate, t s not consdered as a straght lne. So engneerng software data format converson network follows exponental dstrbuton, but does not follow power-law dstrbuton, so t does not belong to the scalefree network. V. VORONOI DIAGRAM BASED COMPLEX NETWORK VISUALIZATION AND RETRIEVAL A. Vorono Dagram based Complex Network Vsualzaton Method 1) SPH Layout Algorthm Vorono dagram based complex network vsualzaton needs to determne the poston of network nodes n the two-dmensonal plane, and dvde ths plane nto Vorono dagram. Wth SPH layout algorthm, vsualzaton method arranges network nodes by the statstcal characterstcs of complex network and determnes ther poston n the two-dmensonal plane wth pont-by-pont nserton method. SPH algorthm s a wdely used mesh-free method. It has been used n many felds of research, ncludng astrophyscs, ballstcs, volcanology, and oceanography. Its basc dea s that the flud s dvded nto a set of dscrete partcles. These partcles have a spatal dstance, over whch ther propertes are "smoothed" by a kernel functon. Ths means that the physcal quantty of any partcle can be obtaned by summng the relevant propertes of all the partcles whch le wthn the range of the kernel[14]. In ths paper, the nodes of complex network are consdered as a seres of partcles whch own qualty, speed and energy, ther dstrbuton s calculated n a two-dmensonal plane. Dscrete SPH functon s as follow: n 1 ux ( ) = [ ux ( ) + wx ( x, h)] N = 1 Where, w (x-x, h) s the nterpolaton kernel functon of the relevance between node x and x as well as control factor h, h determnes the sze of the kernel functon s nfluence doman. B-splnt functon s adopted as the kernel functon. 2 3 d + d d 1 3 (, ) = 2(1 ),1/2 1 3 (1 6 6 ), 0 1 / 2 wdh d d π h 0, d 1 where, d = x x /2. After the poston coordnates of network nodes are worked out, there may be one problem whch these coordnates are too large or too small, so these coordnates can be transformed to an approprate range by dsplay effect. 2) Feature node Feature node s a network node that can represent network nodes clusterng n Vorono dagram based complex network vsualzaton, and t s key node n Vorono dagram based node-space model retreval. Let S s a set of n network nodes, S={o 1, o 2,, o n }. V s a set of m network nodes' clusterng, V={V 1, V 2,, V m }, V s a clusterng n clusterng set, the number of network nodes n V clusterng s k, network nodes n V clusterng s V ={o 1, o 2,, o k }. If 1 ' C, V V ' = and m k n =. x s the mean-value pont of network nodes gm dstrbuton, feature node of clusterng V s defned as CR = argmn= 1,..., k ( Dst( o, xgm )), namely clusterng s feature node s network node whch s nearest to ts mean-value pont. For a set wth m ponts {x 1, x 2,, x m }, x R n, the mean-value pont s defned as: x = argmn x y gm m n y R = 1 The calculaton of the mean-value pont has no explct formula, so ths paper takes t as optmzaton problem and solve t by partcle swarm optmzaton algorthm, PSO[15]. PSO algorthm s basc dea s that a group of partcles, whch have no volume and qualty, are randomly ntalzed, each partcle s consdered as a feasble soluton of the optmzaton problem, and s controlled by a ftness functon whch has been set n 2 (4) (5) (6)

6 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER advance. The ftness functon whch solves the meanvalue pont s defned as: m f ( y) = x y = 1 2 (7) Each partcle moves n feasble soluton space, and ts drecton and dstance are determned by a speed varable. Generally these partcles follow currently optmal partcle and the optmal soluton can be acheved by searchng. Wth PSO algorthm the mean-value pont of clusterng s calculated, thereby the feature node of clusterng s determned. 3) Vsualzaton Method Vorono dagram based complex network vsualzaton method ncludes the followng steps: (1) Accordng to engneerng software data format converson network, ts statstcal characterstcs are calculated, such as drected or undrected, the number of nodes, the number of edges, average degree, average path length, clusterng coeffcent and so on; (2) Network nodes are ranged by node degree n descendng order and ther postons are determned wth SPH layout algorthm n the two-dmensonal plane; (3) Accordng to the dstrbuton of network nodes, the mean-value ponts and feature nodes of network nodes clusterng are determned; (4) The two-dmensonal plane s dvded nto Vorono dagram by Vorono nodes as whch feature nodes are taken. Accordng to Vorono dagram, engneerng softwate data format converson network can be analyzed from several angles. Fgure 10 show Vorono dagram of engneerng software data format converson network. In fgure 10, data formats are dvded nto several clusterng, ths shows that the proposed vsualzaton method s effectve. The dstrbuton of some clusterng s denser, and the dstrbuton of other clusterng s sparser. Ths s because sparse clusterng contans more data formats whch can be nterconverted, and conversely data formats are less. In Vorono dagram the neghborng relatonshp between data formats can drectly reflect ther converson relatonshp. Data formats whch can be converted nto more data formats locate at center of ther dstrbuton, otherwse data formats locate at edge of ther dstrbuton. a) Feature Nodes based Vorono Dagram b) Network Nodes based Vorono Dagram Fgure 10. Vorono Dagram of Engneerng Software Data Formats Converson Network B. Vorono Dagram based Complex Network Retreval Method 1) Path Retreval For the long-term safety preservaton of engneerng nformaton, a problem how to realze the converson between data formats n nformaton mgraton, namely format converson path retreval, must be consdered. At present the presented path retreval algorthm has varous types, for example exhauston search strategy algorthm, heurstc search strateges based branch defnton method, the greedy algorthm, clmbng method and smulated annealng method, etc. Dkstra algorthm, whch s n 1959 by E.D. Dkstra proposed [16], s a shortest path algorthm based on greed strategy. Its basc dea s that an path tree s constructed by ncreasng path length, thus the shortest path from root node to all other nodes s acheved. Engneerng software data format converson network s a graph G (V, E) whch has n nodes and m edge, the startng node s s, the weght of edge w (x, y) s postve real number. If node x s an arbtrary node of network graph, and P s a path from s to x, the path P s weght w (P) s defned as the sum of all edge s weght n path P. The shortest path problem s that path P 0 of mnmum weght s acheved from all paths from s to x, namely w (P 0 )=mn{w (P)}. Each node s set a label (d, p ), where d s the sum of shortest path s weght from s to, and p s the former node of the shortest path from s to. the termnal node s e, the steps of achevng shortest path from s to e are as follows. 1) The label of the startng node s s set as d =0, p s empty, and the labels of other nodes are set as d =, p s empty. The startng node s marked as k=s and other nodes are unmarked. 2) If unmark node s drectly connected to marked node k, the dstance from node k to node s checkng, and d =mn{d, d +l k }, where, l k s the drect dstance form node k to node. 3) The mnmum dstance d s selected from d, d =mn{d unmarked node }, and node s chosen as a node of the shortest path and s marked.

7 2612 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER ) Check whether all nodes are marked. If marked, then ext, otherwse n=, turn to step 2. The above Dkstra algorthm s one of the classc algorthms whch solve the shortest path problem between two nodes of network n graph theory and t s adopted to retreve the shortest path of data format converson n engneerng software data format converson network. Fg. 11 shows the retreval result of shortest path n data format converson. d (b) Network Nodes of Alternatve Node d s Vorono Doman Fgure 12. Alternatve Nodes Vorono Doman and Network Nodes Fgure 11. Shortest Path of Data Format Converson between *.cel and *.tdf 2) Retreval Method a Vorono dagram based complex network retreval method s presented, whch can mprove path retreval effcency by reducng the number of compared nodes. Ths method s as follows: (1) Feature nodes are selected from network nodes clusterng, wth PSO algorthm the mean-value ponts are calculated and network nodes whch are the nearest to the mean-value pont are taken as feature nodes; (2) Query node s compared wth feature nodes. Accordng to dstance between them, feature nodes are ranged n descendng order, the former h feature nodes are selected as alternatve nodes; (3) Wth Vorono dagram based RNN algorthm[17], network nodes n alternatve nodes Vorono doman are determned. Fgure 12 show alternatve node Vorono doman and ts network nodes; (4) Query node s compared wth network nodes n alternatve nodes Vorono doman, the most related network nodes are taken as retreval result. d (a) Alternatve Node d s Vorono Doman Vorono dagram based complex network retreval method need not compare query node wth all network nodes, so retreval task can be accomplshed n smaller compare scope. Compared wth lnear scannng retreval method, ths method can save tme that query node s compared wth network nodes, thus t s more effcent retreval method. At present, the Precson-Recall rato s commonly used to measure the effectveness of retreval method n retreval felds. In ths paper the Precson-Recall rato s used to compare Vorono dagram based node-space model retreval method wth lnear scannng retreval method. Fgure 13 shows the Precson-Recall rato of two methods, two curves are almost concded, so ther retreval performances are almost same. Fgure 13. Comparson of Two Methods' Precson-Recall Rato Curves The retreval effcency of Vorono dagram based complex network retreval method and lnear scannng retreval method s comparatvely expermented, the former ncreases 46.23% than the latter. VI. CONCLUSION Engneerng nformaton have some characterstcs such as complex form and scattered content, so structure relevance and data compatblty drectly nfluence the management and use of dgtal engneerng nformaton resources. Especally n the long-term safety preservaton and relable nvoke, there are some problems such as compatblty between nformaton and system, effcency of nformaton nspecton, mgraton and converson. Usng complex network research nto the complcated relatonshp between engneerng nformaton could contrbute sgnfcantly to ts organzaton and preservaton. A complex network whch takes

8 JOURNAL OF SOFTWARE, VOL. 7, NO. 11, NOVEMBER engneerng software data formats as nodes s constructed and ts statstcal characterstcs whch nclude drected edge or undrected edge, the node number, the edge number, average path length, clusterng coeffcent and node degree dstrbuton, etc., are analysed n ths paper. A Vorono dagram based complex network vsualzaton and retreval method s provded. Accordng to statstcal characterstcs of constructed network, vsualzaton method calculates network nodes postons n a twodmensonal plane and dvdes ths plane nto Vorono dagram by feature nodes of network nodes clusterng and network nodes. Retreval method restrcts compared nodes to nodes n Vorono doman of feature nodes whch are more related to query node, the number of compared nodes s reduced n retreval process. The experment result ndcates applyng ths method nto data formats converson path retreval can ensure retreval precson and mprove retreval effcency, thus provde relable bass for mgratng numerous dsperse engneerng nformaton. ACKNOWLEDGMENT Ths work was funded by the Natonal Natural Scence Foundaton of Chna under Grant No REFERENCES [1] Natonal Insttute of Standards and Technology, Long term knowledge retenton (LTKR): Archval and representaton standards, Gathersburg: NIST, http: // edge.cs.drexel.edu/ltkr/, [2] Ma Zhanghua, Informaton Organzaton, Beng: Tsngha Unversty Press, 2003, pp [3] Natonal Insttute of Standards and Technology, The Role of ISO (STEP) n long term data retenton: Long Term Knowledge Retenton Workshop, Gathersburg: NIST, [4] Zhengxu Zhao, Lee Zhuo Zhao, Small-world phenomenon: toward an analytcal model for data exchange n Product Lfecycle Management, Internatonal Journal of Internet Manufacturng and Servces, 2008, 1 (3): [5] Zhengxu Zhao, Wenbn Zhao, Engneerng data formats: Vsualzaton, converson and mgraton, 2nd Internatonal Conference Intellgent Control and Informaton Processng (ICICIP), 2011, [6] Zhengxu Zhao, Jun Feng, Zhhua Zhang, Complexty Analyss on Engneerng Software Data Format Converson Networks, Complex Systems and Complexty Scence, 2010, 7 (1): [7] Zhao Zhengxu, Long Ru, Guo Yang, Lu Jaa, Research nto Small World Effect n Engneerng Software, Journal of Shazhuang Tedao Unversty (Natural Scence), 2010, 23 (3): 1-6. [8] Franz Aurenhammer, Vorono Dagrams-A Survey of a Fundamental Geometrc, Data Structure, ACM Computng Surveys, vol. 23, no. 3, [9] R. Zhu, Intellgent Rate Control for Supportng Real-tme Traffc n WLAN Mesh Networks, Journal of Network and Computer Applcatons, vol. 34, no. 5, pp , [10] René Retsma, Stanslav Trubn, Informaton space parttonng usng adaptve Vorono dagrams, Informaton Vsualzaton, 2007, 6 (2): [11] Chen H, Schuffels C, Orwg R, Internet categorzaton and search: a self-organzng approach, Journal of Vsual Communcaton and Image Representaton, 1996, 7: [12] Andrews K, Kenrech W, Sabol V, Becker J, Droschl G, Kappe F, Grantzer M, Auer P, Tochtermann K, The InfoSky Vsual Explorer: explotng herarchcal structure and document smlartes, Informaton Vsualzaton, 2002, 1: [13] Wang Xaofan, L Xang, Chen Guanrong, Complex Network Theory and ts Applcaton, Beng: Tsngha Unversty Press, [14] Lu G. R, Lu M.B, Smoothed Partcle Hydrodynamcs: a meshfree partcle method, Sngapore: World Scentfc, [15] Kennedy J, Eberhart R, Partcle swarm optmzaton, Proceedngs of IEEE Internatonal Conference on Neural Networks, Washngton DC: IEEE Computer Socety Press, pp , [16] E.W. Dkstra, A note on two problems n connexon wth graphs, Numersche Mathematk, 1959, 1: [17] Ioana Stano, Mrek Redewald, Dvyakant Agrawal, Amr E Abbad, Dscovery of nfluence sets n frequently updated databases, In Proc. Int. Conf. on Very Large Databases (VLDB), pp , [18] G.F. Vorono, Nouvelles applcatons des parameters contnus à la théore de forms quadratques, Journal für de rene und angewandte Mathematk, 1908, 134: Wen-bn Zhao receved hs B.Sc. and M.Sc. degrees n Computer Scence from the Shazhuang Tedao Unversty, Chna, n 2007 and 2010, respectvely. Hs research nterests nclude vrtual realty and vsualzaton technology. E-mal: zhaowb @gmal.com Zheng-xu Zhao receved hs PhD n Appled Computng from Staffordshre Unversty, CNAA, UK, n He had been Professor and Char n Computer Integrated Manufacturng Systems n School of Computng at the Unversty of Derby, UK, snce Hs current research nterests nclude computer graphcs, vrtual envronment, software engneerng and knowledge retenton. E-mal: zhaozx@stdu.edu.cn