An Approach to Discover Dependencies between Service Operations*

Transcription

1 36 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER 2008 An Approach to Dscover Dependences between Servce Operatons* Shuyng Yan Research Center for Grd and Servce Computng Insttute of Computng Technology Chnese Academy of Scences Bejng Chna Emal: yanshuyng@software.ct.ac.cn Jng Wang Chen Lu Research Center for Grd and Servce Computng Insttute of Computng Technology Chnese Academy of Scences Bejng Chna Emal: wangjng@ct.ac.cn luchen@software.ct.ac.cn Le Lu College of Appled Scences Bejng Unversty of Technology Bejng Chna Emal: lulu_lele@bjut.edu.cn Abstract Servce composton s emergng as an mportant paradgm for constructng dstrbuted applcatons by combnng and reusng ndependently developed component servces. One ey ssue of servce composton s how to dentfy relevant servce operatons so as to compose servces rapdly and correctly. A promsng approach to smplfyng the search of relevant servce operatons n servce composton les n the dscovery of the dependences between servce operatons. However the dscovery of operaton dependences s not a trval tas but a challenge. We propose an approach to dscover operaton dependences n a personal problem solvng envronment. Our approach combnes the semantc matchng of nputs and outputs nterfaces between servce operatons and the analyss of process cases to dentfy dependences. The man contrbutons of the approach are: 1) It can be used to dentfy the drecton of the dependences. 2) It provdes the method to measure the strength of dependences. 3) Nonconflct property and non-redundancy property of dscovered dependences are guaranteed based on a dependency graph. Moreover we expermentally demonstrate the effcacy of our approach by testng t under three typcal bonformatcs scenaros. Index Terms operaton dependency servce composton process cases analyss semantc matchng I. INTRODUCTION Recent advances n networs nformaton and computaton grds and WWW have resulted n the prolferaton of a multtude of physcally dstrbuted and autonomously developed Web servces. Servce composton s emergng as an mportant paradgm for *Ths wor s supported by Natural Scence Foundaton of Chna under Grand No Natonal Basc Research Program (973) of Chna under Grant No. 2007CB and Natonal H-Tech Research and Development Program (863) of Chna under Grand No. 2006AA12Z202. constructng dstrbuted applcatons by combnng and reusng ndependently developed component servces. One ey ssue of servce composton s how to dentfy relevant servce operatons so as to compose servces rapdly and correctly. A promsng approach to smplfyng the search of relevant servce operatons n servce composton les n the dscovery of the dependences between servce operatons. Snce servce operatons cannot be consdered solated tass. Generally they depend on each other. It s valuable to dscover the dependences especally wthn a maze of nterdependent servce operatons. However the dscovery of operaton dependences s not a trval tas but a challenge. Many researchers have sgnfcant nterest n usng dependences for servce composton [2]. However most exstng wors assume the pre-exstence of manually-constructed operaton dependences. In complex and fast evolvng envronments t s practcally unfeasble to obtan the dependency nformaton and eep the nformaton up-to-date manually. So t s necessary to detect accurate and up-to-date operaton dependences n an automatc manner. In ths paper we propose an approach to dscover operaton dependences n a personal problem solvng envronment. It consders not only the semantc matchng of operaton nterfaces but also the nvong order among operatons by mnng of process cases. The man contrbutons of the approach are: 1) It can be used to dentfy the drecton of the dependences. 2) It provdes the method to measure the strength of dependences. 3) Non-conflct property and non-redundancy property of dscovered dependences are guaranteed n terms of a dependency graph. And correspondng algorthms are presented to preserve the dependency graph's consstency. Moreover we have mplemented the technque of dependences dscovery and have appled t n a prototypcal scentfc system VINCA4Scence. It helps

2 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER to ensure the correctness of composton and mprove the performance. The remander of the paper s organzed as follows: Secton 2 ntroduces operaton dependency. Secton 3 presents the detals of our approach for the dscovery of operaton dependences. Secton 4 descrbes and dscusses the expermental result. Secton 5 compares our wor wth other related wor. Fnally we conclude n secton 6. II. OPERATION DEPENDENCY Operaton dependency relatonshps [7] are specfed as constrants on the occurrence and order of a par of servce operatons. It ndcates that the occurrence of one servce operaton may cause the occurrence of other operaton. The dependency relatonshps between two servce operatons consst of two parts: occurrence patterns and order patterns as shown n Fg. 1.. DISCOVERY OF OPERATION DEPENDENCY One of the most pressng needs s the ablty to dscover the dependences wthn a maze of nterdependent servce operatons. Dscovery of operaton dependences refers to the process checng whether servce operatons can actually wor together. The approach we tae to automate the dentfcaton of dependences manly ncludes two man phrases: 1) Acquston of operaton dependency; 2) Conflct detecton and redundancy chec. In the followng sectons we wll gve the detals. A. Acquston of Operaton Dependency Acquston of operaton dependency s to fnd drect dependences and ndrect dependences. It s composed of two aspects: semantc matchng of operaton nterfaces and analyss of process cases. We have ntroduced the approach of semantc matchng of operaton nterfaces n our earler publcaton [7]. In [7] semantc match degree represents the semantc match degree between the parameters of operatons. It can also be measured by smlarty matchng between the outputs or nputs of source operaton and nputs of target operaton. ) Analyss of Process Cases Our approach for dscoverng the possble dependences from process cases proceeds n two consecutve steps: 1) Constructon of the frequency table. We use adjacency matrx to analyze drect dependency relatonshps and derve ndrect dependency relatonshps by transtve closure algorthm over adjacency matrx. 2) Constructon of the dependency table. It s based on the metrcs of frequency table. The tems n the dependency table are analyzed wth measures based on Pearson's Correlaton Coeffcent. 1)Constructon of Frequency Table Fgure 1. Dependency relatonshps between two servce operatons There are four nds of occurrence patterns: always exst drectly: a servce must occur adjacent to a gven servce. always exst ndrectly: a servce always occurs wthn a scope but not adjacent to a gven servce. sometmes exst: a servce sometmes occurs wthn a scope. always absent: a servce can't occur wthn a scope. There are two nds of order patterns: before(): the occurrence pattern must hold up to the occurrence of a gven servce. after(): the occurrence pattern must hold after the occurrence of a gven servce. Then we can get ten nds of dependency relatonshps between two operatons as Fg. 1 shows. The measure of reuse rate starts wth the constructon of frequency table. For servce operaton S and T the followng nformaton s abstracted out of the process cases: #S: the overall frequency of S drectly occurs &S: the overall frequency of S ndrectly occurs #T: the overall frequency of T drectly occurs &T: the overall frequency of T ndrectly occurs S>T: the frequency of S drectly followed by T S<T: the frequency of S drectly preceded T S>>>T: the frequency of S ndrectly followed by T S<<<T: the frequency of S ndrectly preceded T An example of frequency table s gven n Table 1. The frequency table can be updated ncrementally when a new valuable process case s created. It s helpful for the dynamc evoluton of operaton dependences. Metrcs 1) through 4) s easy to acheve. Metrcs 5) and 6) can be computed by adjacency matrx (drectly dependency matrx). Metrcs 7) and 8) can be computed by transtve

3 38 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER 2008 TABLE FREQUENCY TABLE closure over adjacency matrx. The detals wll be ntroduced as follows. Constructon of Adjacency Matrx Fnd each explct drect dependency between two servce operatons. We use adjacency matrx D n m to represent drect dependences. In ths matrx each servce operaton s represented by a column and a row. If a servce operaton op s dependent on another servce operaton op j then D[j] = 1. More formally the values of all elements n D n m=(d j ) nm are defned as follows: 1 fop op j dj 0 otherwse AM s the zero-one matrx of ths relaton where d j = 1 f there s a drected path from op to op j and 0 otherwse. In the cases that the drect dependency relatonshps matrx (D) s nown to be sparse.e. only a small subset of the elements of the correspondng array are non zero then a sparse array mplementaton can be used. We can utlze the storage technque of sparse matrx. It can be stored n a compact representaton format n the trple table. The above array would be represented usng the form (jvalue). Nowadays there are a number of web servce operatons that have smlar or dentcal functonaltes. In order to dscover dependences correctly the factor should be consdered. Frstly t s necessary to group functon smlar servce operatons together. We have devoted the researches on ths aspect [8]. Then we mae use of the aggregaton result to merge the same or smlar tems. Suppose that AS={as 1 as 2...as n } s the set of aggregated servce. And each aggregated servce can be assocated wth a set of servce operatons wth smlar functonalty C as ={op 1 op 2...op m }. Let C S be the smlar tems set that ncludes operaton S and CT be the row smlar tems set that ncludes operaton T. Suppose M col M j be the count of non-zero elements n row and be the count of non-zero elements n column j. If S = op and T = op j we can get the followng values by the analyss of D: row col M # T M j op C s S T d j Example opc s op jct op j C T S T d j op jct opc S We tae the process fragments from A to F shown n Fg. 2 as an example. Fgure 2. A Snapshot of Bombyx mor Genome Assembly Experment Then we can construct the adjacent matrx as: Utlzng the result of servce aggregaton we can merge the smlar tems. We can add 6th column to 4th column and 6th row to 4th row. As a result we can get the followng matrx:

4 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER Utlzng the result of servce aggregaton we can merge the smlar tems and then get the followng matrx: Then we can get that #RectfyReadsByWordDepth = 2 #GroupReadsbyDepth = 2 S>T = S<T = 2. Computng of Transtve Closure Based on Adjacency Matrx Accordng to the descrpton n the above secton the drect dependences can be represented n an adjacency matrx. Because of the transtvty of dependences we can further to gather all ndrect dependences n the system by calculatng the transtve closure of dependences. The algorthm of calculatng the transtve closure s Warshall's algorthm. In the general case the transtve closure t(r) of relaton R can be calculated as: t( R) R R 1 1 R R R R 1 It can be rewrtten as: t( R) Where depcts the length of maxnum path. Then we can get the set {R =2...}. The termnaton pont dependes on the "depth" of the relaton.e. on the count of vertces of the long path. Thus the computng process may termnates before reachng >. It s easy to proven. Then we can get the ndrect dependence relatonshps matrx: l1 R ID ( D ) [01] l2 depcts that the dependency degree decreases wth ncreasng path length between S and T. Then we utlze the aggregaton result to merge the same or smlar tems. By the analyss of ndrect dependency relatonshps matrx (ID) we can get the followng values: row col & S M l & T M lj op C s l 1 op j C T l1 l l S T ( d j ) S T ( d j ) Example opc s op j CT l 1 op jct opc S l1 From the adjacent matrx (D) mentoned above we can get the ndrect dependence relatonshps matrx by computng ts transtve closures: 2) Constructon of Dependency Table Usng the metrcs n the frequency table we can nfer the drect and ndrect dependences from process cases. The result s shown n Table 2. Each cell n the dependency table can be measured based on Correlaton Coeffcent. Correlaton Coeffcent ( ) [5] s the computatonal form of Pearson's Correlaton Coeffcent for bnary varables. The measure of Correlaton Coeffcent ( ) s P00P11 P01P10 ( A B) P P P P 1 Where and P P j P j j P j For the other cases we can use the analogous formula to measure. Fnally we can get the result shown n Table 3. The aforementoned formula s often used to analyze the correlaton of bnary varables and the dependency drecton s not taen nto account. Then we specfy the formula for each nd of dependences. For example the reuse rate of servce operaton S drectly followed by servce operaton T can be measured by the followng formula. ' ' ' ' P00P11 P01P10 ( S T ) ' ' ' ' P0 P1 P 0P 1 Where ' S T P11 P( T S) ' S T P10 P( T S) ' # T S T P01 P( T S) P ' 00 P( T S) TABLE CONTINGENCY TABLE SS set S S SS set S S (# T S T). SSset S S ) Combnaton of Two Approaches In order to assst end-users to dscover drect and ndrect operaton dependences correctly t s necessary

5 40 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER 2008 TABLE DEPENDENCY TABLE to combne the two approaches mentoned above together. Because there are some problems to dscover the dependences by usng ether of the approaches. On one hand semantc matchng approach can only be used to fnd a few of drect dependences. And even f there exsts semantc matchng of operatons t does not mean there really exsts a dependency. Moreover the strength of semantc match degree can't reflect the strength of operaton dependency correctly. On the other hand process cases analyss approach can fnd both drect and ndrect dependences but the process cases are not complete. Some dependences can't be nferred from process cases. Further there may be errors n the process cases. In order to reflect the dependency relatonshps between two servces we frstly ntroduce the noton of composablty degree. Composablty degree reflects the strength of dependency that s the degree to whch the operatons can be jonted. Here composablty degree ncludes two fundamental factors: semantc match degree and reuse rate. The semantc match degree has been ntroduced n the above secton. Reuse rate can be acqured from the generated dependency table. It s evaluated by mnng the assocaton rules of servce operatons culled from process cases. The composablty degree s defned as w 1 semantcmatchdegree + w 2 reuserate. To measure the composablty degree we assocate a weght to each factor. A composer can assgn each weght by estmatng the mportance of the correspondng factor from hs own pont of vew. If there exsts the drect dependency between S and T both of the weghts must hold the followng constrants: w 1 >0 w 2 >0 w 1 +w 2 =1. Or else f there exsts the ndrect dependency between S and T the weghts must hold the followng constrants: w 1 =0 w 2 =1. Then we can use the calculated composablty degree of each nd of dependency to update the constructed dependency table. A hgher composablty degree means a hgher lelhood there s a dependency. Fnally we use the decson tree algorthm C4.5 whch s most wdely used n practce to date to nduce the decson rules. Mang use of the decson rules we can predcate the dependences between two operatons. B. Conflct Detecton and Redundancy Chec The approach llustrated n the prevous step can produce some dentfed dependences between pars of operatons. And each dependency s labeled wth a composablty degree that expresses the strength of the nferred dependency. However the dentfed dependences may be ncorrect. The correctness of dependences s formalzed n terms of two propertes: conflct-free property and nonredundancy property. The conflct-free property ensures that the satsfacton of constrants mposed by each dependency may not volate the constrants mposed by other dependences. If a gven set of dependences has conflcts we cannot gve any assurance about the correctness of composton. The non-redundancy property ensures that no dependency specfed n a set of dependences s extraneous. The presence of extraneous dependences n a set of dependences unnecessarly slows down the performance. A dependency d x s sad to conflct wth another dependency d y f the constrants mposed by d x volate the constrants mposed by d y. For example consder the dependences a b and b c. If there s a new dependency a c then t conflcts wth the exsted dependences. Snce we can nfer a c by dependences a b andb c. There are two nds of dependency conflcts: 1) Drect vs. Indrect conflcts. For nstance consder the dependences a b b c then dependency a c s conflctng wth them. Because the ndrect dependency can be referred from a b and b c. Then there s drect versus ndrect conflcts. 2) Absent vs. Exst conflcts. Consder the dependences a b and b c then dependency a c conflcts wth them. For example f there s a new dependency a c then t s redundant for the exsted dependences. Snce we can nfer a c by dependences a b andb c.

6 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER TABLE THE RESULT OF DISCOVERING DEPENDENCIES ONLY WITH SEMANTIC MATCHING TABLE THE RESULT OF DISCOVERING DEPENDENCIES ONLY WITH PROCESS CASES ANALYSIS TABLE THE RESULT OF DISCOVERING DEPENDENCIES WITH SEMANTIC MATCHING AND PROCESS CASES ANALYSIS Hence t s necessary to detect dependency conflcts and chec dependency redundancy. There are two nds of dependency redundancy: 1) Transtve redundancy. As we all nown that dependences of same type have the transtve propertes. If a dependency can be nferred by transtve deducton. 2) Implct redundancy. For dfferent types of dependences they also can contan mplct dependences. For example the dependences a b and b c whch mply the exstence of a c. If a c exsts then there wll be an mplct redundancy. Then we propose the algorthm to automatcally detect the conflcts and chec the redundances resultng from these dependences by usng the structure of dependency graph. A dependency graph s a drected acyclc graph n whch nodes represent servce operaton and labeled drected edges represent some dentfed dependences between pars of operatons. Each edge of the graph s labeled wth some nformaton (e.g. the type of dependency semantc match degree and reuse degree) that expresses the confdence level of the nferred dependency. Ths wll enable the users to get assurance about the correctness of the dependences. The algorthm ncludes four man steps: Step 1: Merge the dependences whch have the same source operaton and target operaton. There are multple nds of dependences between the same operaton par. The dependences can be merged. As a result we can get a composte dependency. Durng the process of mergng the conflcts between dependences should be dealt wth. There are no conflcts among the dependency relatonshps. For two dependences between the same operaton par t needs to chec two nds of conflcts: Drect vs. Indrect conflcts and Absent vs. Exst conflcts. The process can guarantee the unqueness property of dependences between an operaton par. If there are conflcts then notfy the users. Or else go to step 2. Step 2: Generate ntal dependency graph based on the "drect" dependency. Each "drect" dependency s d depcted as an edge of the formop op where j op s the source operaton op j s the target operaton d s the type of dependency s the semantc match degree s the reuse rate. Step 3: Generate the transtve closure based on the ntal dependency graph. Then we can detect the conflcts of dependences. For each type of dependency conflcts the procedure of detectng conflct could be performed n any order snce detectng for each type of conflcts s ndependent. We can also chec the transtve redundancy. The procedure us repeated untl no more new edges can be generated. Step 4: Generate the mplct dependency based on the ntal dependency graph. The procedure s also teratve. For each par of operatons chec whether new edges could be derved by exstng edges. The newly derved edges may mply more new mplct edges. They wll be checed n the next round. We can use the derved result to detect the conflcts and chec the mplct redundances.

7 42 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER EXPERIMENTAL RESULT AND EVALUATION A. Experment Data In order to dscover the operaton dependences by semantc matchng of operaton and analyss of process cases we have collected about 100 servce operatons and 250 exsted bologcal process cases. The servce operatons are collected from mygrd EBI DDBJ NCBI Bejng Hua Da Insttute et al. The process cases are culled from several tools and organzatons e.g. taverna of mygrd DDBJ CNS Lfe Scence Dept of Barcelona Supercomputng Center Bonformatcs and Informaton technologes Laboratory of the Unversty of Malaga IBM Lfe Scence Center of Excellent n Tawan Bejng Hua Da Insttute et al. B. Method We performed prelmnary experments to evaluate the qualty of our algorthm of dscoverng operaton dependences n terms of three ey dmensons: precson recall and F-measure. Precson measures the fracton of relevant tems among the predcted dependences. Precson evaluates the accuracy of the dependences by comparng relevant tems ncluded n the predcted dependences aganst the whole predcton results. Recall s a measure of the percentage of relevant tems ncluded n the predcted dependences and the tems that the experts have defned. F-measure s a trade-off between precson and recall. The metrcs can be measured by the followng formulas: Precson ( Relevant Predcted)/ Predcted Recall ( Relevant Predcted )/ Relevant F measure (2 PrecsonRecall)/( Precson Recall) We test our approach under three typcal bonformatcs scenaros: rce genome resequencng bombyx mor genome assemblng and phylogenetc analyss. Accordng to the three scenaros we dvded the operatons and process cases nto three categores. For each category there are operatons respectvely. There are ntersectons among operatons. Then we classfy the process cases nto three groups wth process cases respectvely accordng to the operatons. There are also ntersectons among process cases. For each category we compare our approach to dscover operaton dependences by semantc matchng and analyss of process cases wth two other approaches: dscoverng dependences wth semantcs matchng and dscoverng dependences wth process cases analyss. C. Results The result of dscoverng dependences by dfferent approaches are shown n Table 4 Table 5 and Table 6. Each table shows precson recall and F-measure results under three scenaros respectvely. D. Dscusson and Concluson From the results we can see that the approach to dscover dependences only by semantc matchng has a low precson. A detaled analyss of low precson shows that t dues to two man reasons. Frst some operatons are semantcally matchng but they are semantcally smlar or equvalent operatons and not dependent operatons. Second some operatons can be jonted however the executon orders of operatons are ncorrect or conflcted. Moreover the approach s not sutable for dentfyng ndrect dependences. The approach to dscover dependences only by analyss of process cases has a hgher precson than the approach by semantc matchng. Wth the avalable data set ncreasng the precson and recall become hgher. It can be used to dentfy both drect and ndrect dependences. It can help to reduce the search space and allevate users' efforts to fnd the correlated operatons. As llustrated n Table 5 there are 67 servce operatons n DS 3 whch wll result n 4489 (67 2 /2) dfferent pars. The approach by analyss of process case detects 80(drectly-after) 105(ndrectly-after) 81(drectlybefore) 131(ndrectly-before) dependences and 4274 unrelated pars. However due to the lmt of data set when the operatons ddn't occur n the process cases then the dependences among the operatons can't be dscovered. And the errors n the process cases may brng about the ncorrectness of dscovery of dependences. Our approach combnes the above two approaches. Our approach tres to avod the defcences of the approaches and tae the advantages of them. As Table 6 shows the precson recall and F-measure of our approach are hgher than one of two approaches. It can be used to dscover both drect and ndrect dependences..related WORKS A. Representaton of Dependences The presentaton of dependences dffers n varous ways due to the usage for dfferent purposes. A common descrpton format needs to represent the dependences n an unform way. Brown et al [2] and Basu et al [1] thn that the dependency can be modeled as a drected edge between nodes. Each edge s labeled wth a probablty that expresses the confdence level of the nferred dependency. As a result a probablstc dependency graph wll be constructed as concatenaton of all dentfed dependences. Ensel et al [3] argue that t s necessary to express the drecton of the dependences. Besde the drecton there are also some useful nformaton e.g. to express that some dependences must occur n a certan order or to attach values of strength or lelhood. From the above analyss we can see that the formal representaton of dependences manly ncludes some parts: 1) order constrants. The order constrants between two operatons are not just cause-and-effect. It needs a dverse expresson of order constrants. 2) drecton. It s regarded a serous defcency for not possble to express the drecton of the dependences. The drecton may be forward or bacward. 3) strength. The strength of a dependency ndcates the lelhood that an operaton s affected by another operaton.

8 JOURNAL OF SOFTWARE VOL. 3 NO. 9 DECEMBER B. Acquston of Dependences Ensel et al [3] have proposed an artfcal neural networ based technque so as to decde the exstence of a dependency between objects. Unfortunately the neural networ has to be traned n a supervsed manner a laborous process and delcate process mang the practcal applcaton of the technque expensve. However t can only detect the cause-and-effect dependency. Furthermore they haven't touched on how to measure the strength of dependences. To the best of our nowledge the study of the technque's performance has not been publshed. Some researchers devote to the approach based on semantc matchng to explore the dependences. Kuang et al [4] explan the mportance of mportng dependences nto servce dscovery and ntroduce two types of dependency between nterfaces of servces. However they ddn't consder the drectonalty the strength of dependences etc. whle acqurng the dependences. The acquston of dependences between servces s also closely related to log (worflow) mnng. Stenle et al [6] have developed non-ntrusve and scalable technques to dscover dependences between components of a dstrbuted system by mnng logs. However the approaches fal to detect the drecton of dependency and dstngush the drect and ndrect dependences. The measure of dependency strength has not been mentoned to say nothng of the propertes guarantee e.g. nonconflct property and non-redundancy property.. CONCLUSION In order to smplfyng the search of relevant servce operatons n servce compostons we put forward an approach to dscover operaton dependences n a personal problem solvng envronment. It combnes the semantc matchng of nputs and outputs nterfaces between servce operatons and the analyss of process cases to dentfy dependences. The man contrbutons of the approach are: 1) It can be used to dentfy the drecton of the dependences. 2) It provdes the method to measure the strength of dependences. 3) Non-conflct property and non-redundancy property of dscovered dependences are guaranteed based on a dependency graph. REFERENCES [1] S. Basu and F. Casat. Web servce dependency dscovery tool for soa management. In Proc. of the IEEE Internatonal Conference on Servces Computng (SCC 2007) July [2] A. Brown and G. Kar. An actve approach to characterzng dynamc dependences for problem determnaton n a dstrbuted envronment. In Proc. of the IFIP/IEEE Internatonal Symposum on Integrated Networ Management page 377C [3] C. Ensel. A scalable approach to automated servce dependency modelng n heterogeneous envronments. In Proc. Of the Ffth IEEE Internatonal Enterprse Dstrbuted Object Computng Conference (EDOC 01) pages September [4] L. Kuang and J. Wu. Explorng dependency between nterfaces n servce matchmang. In Proc. of the IEEE Internatonal Conference on Servces Computng (SCC 2007) pages July [5] H. Reynolds. The analyss of cross-classfcatons. The Free Press [6] M. Stenle and K. Aberer. Mappng movng landscapes by mnng mountans of logs: novel technques for dependency model generaton. In Proc. of the 32nd nternatonal conference on Very large data bases (VLDB 06) pages [7] S. Yan and Y. Han. Servce hyperln for exploratory servce composton. In Proc. of The IEEE Internatonal Conference on e-busness Engneerng (ICEBE 2007) pages October Shuyng Yan receved her MS degree n the School of Computng Scence and Technology from Shandong Unversty n She s presently a Ph.D. canddate n the Insttute of Computng Technology of the Chnese Academy of Scences. Her research nterests nclude software ntegraton and servce grd exploratory servce composton. Jng Wang receved her Ph.D degree from the Insttute of Computng Technology of the Chnese Academy of Scences. Her research nterests nclude software ntegraton and servce grd personal grd worflow. Chen Lu receved hs MS degree and Ph.D degree from the Insttute of Computng Technology of the Chnese Academy of Scences. Hs research nterests nclude ontology learnng and semantc web. Le Lu receved hs Ph.D degree from the Insttute of Computng Technology of the Chnese Academy of Scences n Hs research nterests nclude nowledge acquston and ontology learnng.