Dengmei Qiu
email: dmqiu@dhu.edu.cn

Wenjie Wang
email: wenjiew@dhu.edu.cn

A Combining Forecasting Modeling and Its Application

Keywords: Combination forecasting, Bayesian model, Supply chain 1

The supply chain coordination has abstracted more and more attention from industries and academics. This paper studies a Bayesian combination forecasting model to integrate multiple forecasting resources and coordinate forecasting process among partners in retail supply chain. The simulation results based on the retail sales data show the effectiveness of this Bayesian combination forecasting model to coordinate the collaborative forecasting process. This Bayesian combination forecasting model can improve demand forecasting accuracy of supply chain.

Introduction

With the development of information and network technology, various innovative supply chain solutions are created. Collaborative planning, forecasting and replenishment (CPFR), which is an retailing supply chain coordination innovation based on the network technology, has been adopted and implemented by many world-renowned retailers and manufacturers, such as Wal-Mart, Proctor & Gamble, etc.. CPFR concerns the collaboration where two or more parties in the supply chain jointly plan a number of promotional activities and work out synchronized forecasts, on the basis of which the production and replenishment processes are determined [START_REF] Liu | Information Integration of CPFR in Inbound Logistics of Automotive Manufactures Based on Internet of Things[END_REF]. The first CPFR project was piloted by Wal-Mart with its suppliers in 1995. The results of two-year project showed that CPFR could simultaneously reduce inventory levels and increase sales for both retailers and suppliers. Since its original application was initiated, CPFR has had many successful applications in North America, Europe and China [START_REF] Danese | Designing CPFR collaborations: insights from seven case studies[END_REF].

The collaborative forecasting plays an important part in CPFR implementation procedure. We will briefly review the CPFR concept and its implementation process at the beginning of this paper. And then, the collaborative forecasting process which is the core part of CPFR will be mainly discussed. As the basics phase of the implementation of CPFR, the collaborative forecasting process is the cornerstone to the success of CPFR projects. The collaborative forecasting process of CPFR requires a solid forecasting approach to synthesis information and knowledge from multiply parties in the supply chain. The combination forecasting method can combines forecasting models from different parties to smooth coordination in the supply chain and reduce forecasting discrepancies. Thus, considering the multiple forms of forecasting resources in the retail supply chain, the Bayesian combination forecasting method is applied for CPFR collaborative forecasting modeling with improved forecasting accuracy and supply chain collaboration performance in this paper.

Combining forecasts is a well-established procedure for improving forecasting accuracy which takes advantage of the availability of both multiple information and computing resources for data intensive forecasting [START_REF] Bunn | Forecasting with more than one model[END_REF]. Since Bates-Granger first proposed the combination forecasting method in 1969 [START_REF] Bates | Combination of Forecasts[END_REF], many kinds of combining methods have been developed [START_REF] Bunn | Forecasting with more than one model[END_REF]. Zeng et al [START_REF] Zeng | Combination forecasting based on Stein-rule estimation and error correction[END_REF] studied the combination forecasting model with error correction and changing weight coefficient. Hoogerheide et al [START_REF] Hoogerheide | Forecast Accuracy and Economic Gains from Bayesian Model Averaging Using Time-Varying Weights[END_REF] compared several Bayesian combination schemes in terms of forecast accuracy and economic gains. Bayesian combination methods use the distributional properties of the individual forecasts to construct the combination. The demand forecasting in retail supply chain is impacted by many factors such as product promotion or social development trend. And, subjective forecasting based on the expert experiments is often used in retail market forecasting. So, the Bayesian combination forecasting model is considered as the collaborative forecasting approach in retail supply chain coordination.

In the first part of this paper, the CPFR retail supply chain coordination and collaborative forecasting process are discussed briefly. In the second part of the paper, a Bayesian combination forecasting method is modeled to coordinate forecasting process in retail supply chain. Finally, the simulation for this model is completed based on the Carrefour sales data. The simulation results showed the effective of this Bayesian combination forecasting model in retail supply chain collaboration process.

Bayesian Combination Forecasting Modeling for Collaborative Forecasting Process

The combination method proposed by Bates and Branger in 1969 is normally called as optimal linear combination model or B-G method. The forecasting results

cn c c f f f  2 1 ,
are supposed as random variables with the covariance matrix ∑ in this model. Based on the minimizing the variance criteria (MV), the optimal forecasting results can be calculated as the following formula (1)

f W f W f T k n k k c     1 (1)
Here, weighting vector ) ..., ,

( 2 1 1 1 1 W W W W n T n n T n T        , ) 1 ,..., 1 , 1 ( 1  T n . ( 1 ) 1 1
The Bayesian combination forecasting model is developed based on the B-G method and uses the distributional properties of the individual forecasts to construct the combination. Supposed the Y is presenting the samples of actual demand. The forecasting results obtained from the different parties in the supply chain which are forecasted with m kinds of individual forecasting methods are presented by

m f f f  , , 2 1 (j=1,2,…,m). n i , , 2 , 1  
present different forecasting time periods. The Bayesian combination forecasting method makes use of the Bayesian rule to decide the optimal combination ways and weights of individual forecasting methods in combination model and get the combined forecasting results in different time periods

) , , 2 , 1 ( ˆn i f ic  
, which can be fully approximated to actual demand values.

Set

) , , (

2 1 m T i f f f z   , n i , , 2 , 1   ,
  I n i i n Y z f Y z z z f     ( 2
)
Here  is parameter vector, and ) , , , (

2 1 n T y y y Y  
is the vector of the actual demand samples on the different forecasting time periods.

According to the Bayesian rule, the probability density function that Y is specified as a vector value y is as follows:

          n i A i i i im i i i i im i i n i A i i i i n i i i i n i I dy y f y f f f f y f y f f f f dy y f y z f y f y z f z z z y f 1 2 1 2 1 1 1 2 1 ) ( ) , , ( ) ( ) , , ( ) ( ) ( ) ( ) ( ) , , , (  
 (3) Here ) , , 2 , 1 )( ( n i y f i  
is the prior probability distribution of i y , which presents the prior estimation or preference of decision maker to i y . i A is the definition set of i y .

If only one forecasting time period is considered as the general case and prior distribution )

( y f is uniform distribution form, that is 1 ) (  y f
, the formula (2) can be simplified as the following.

  A m m m dy y f f f f y f f f f f f f y f ) , , ( ) , , ( ) , , , ( 2 1 2
1 2 1    (4)
The forecasting error distribution of individual forecasting results ) , , (

2 1 m f f f 
is chosen as normal distribution or logarithm normal distribution in most case. In this paper, more general distribution of forecasting error is introduced through

Cox Box  conversion.          0 ln 0 1 ) (      Z Z Z (5)
Here, λ is conversion parameter Supposed ∑ is the covariance matrix of ) , , (

2 1 m f f f 
and Wi presents the weights of individual forecasting fi . Then, weights can be calculated as following formula based on the minimum error variance criteria:

    m T m m T m T W W W W ..., , 1 1 1 2 1 1 1 1        (6)
And, nonlinear combination forecasting formula can be obtained through Bayesian analysis as follows, which will be used to calculate the optimal combination forecasting results approximated to the actual values Y .

                               0 1 0 ˆ1 1 1 1 ) ( 1 2 2        m i i i m i i i m i s w i c f W f W e f f i (7) Here, , 1 1 1 1 1 1 1 2 m T m m T m S        and 1 1    n i it W
And, the optimal conversion parameter λ* can be calculated as the following formula. ) (

min arg                 t j ij n i j f W i Y     (8)
Here, f ij present the forecasting result on j time period by i individual forecasting methods. The weights W i can be calculated by formula [START_REF] Bates | Combination of Forecasts[END_REF]. The covariance matrix ∑ can be determined by prior value or estimated by past proximate samples values.

3.

Simulation

The simulation of the Bayesian combination forecasting model will be based on the sales data of one kind of biscuit product in Carrefour China in this paper. The detailed sales data of this biscuit product in 39 weeks in Carrefour China is showed in table 1. During the simulation process, the sales data from week 1 to week 28 are used to estimate parameters in Bayesian combination forecasting model creation. The sales data from week 29 to week 39 are used to compare with the forecasting results obtained from combination forecasting methods. Based on the Carrefour biscuit sale data and statics analysis, the Bayesian forecasting model can be created following three main steps, which includes proper individual forecasting methods selection, combination ways determination and optimal parameter estimation. The characteristics of the individual forecasts combining in the model has substantial implications on the overall forecasting performance of model, and thus it is very important to make a rigorous analysis on the individual forecast errors. The first step of combination modeling is to compare and select proper individual forecasting methods for combination. In general, different parties in the retail supply chain may use the different patterns of individual forecasting. So, the different patterns of individual forecasting methods, which include the simple moving average, the exponential smoothing, the trend extrapolation method, ARIMA (autoregressive integrated moving average) method and artificial neural network method, are applied to combine model to forecast Carrefour biscuit demand from week 29 to week 39. Through comparison study of forecasting results of each individual forecasting method, the best parameters of each individual forecasting method are estimated. The forecasting results of five different individual forecasting methods are indicated in Table 2. The F1 row data indicated the best result forecasted by the simple moving average method when moving period N equal to 3. The F2 row data indicated the best result forecasted by the exponential smoothing method when smoothing coefficient a equal to 0.6. The F3 row data indicated the best result forecasted by the two polynomial regression method (n=2). The F4 row data indicated the best result forecasted by the ARIMA method when parameter d equal to 1. The F5 row data indicated the best result forecasted by the artificial neural network method when there are three neurons and two hidden layers in the neural network.

After the five individual forecasting methods selected, the second step of combination modeling is to determine proper combination ways of these individual methods. With the Matlab simulation tool, the forecasting results are calculated using different combination ways of these individual forecasting methods. As it is widely accepted that only ``good'' forecasts should be included in a combination, strong differences in forecast error variances between the individual forecasts are not to be expected [START_REF] Menezes | Review of guidelines for the use of combined forecasts[END_REF]. Four individual methods (F1、F2、F4、F5) which have good forecasting performance is decided to be combined into Bayesian combination model.

The optimal conversion parameter λ * can be calculated according to the formula [START_REF] Zhang | Research on combination forecast of port cargo throughput based on time series and causality analysis[END_REF]. The sum of square of forecasting error is minimized when λ * = 6.9419. The optimal conversion parameter λ * could be simplified as λ * =7 in the Bayesian combination modeling. The forecasting error between forecasting result and actual demand are used as the evaluation standards of forecasting methods performance. There are many kinds of measure indexes of forecasting errors [START_REF] Zhang | Research on combination forecast of port cargo throughput based on time series and causality analysis[END_REF]. In this paper, four main measure indexes of forecasting error, which are the squares sum error (SSE), the mean square error (MSE), the mean absolute percentage error (MAPE) and the mean square percentage error (MSPE), are applied to comprehensively evaluate the forecasting accuracy of Bayesian combination forecasting model. The Bayesian combination models with different parameters λ are compared with the simple average method and optimal linear methods in the simulation based on Carrefour biscuit sale data. The forecasting errors of different combination forecasting methods are showed in the table 3. It can be found that the four measure indexes of forecasting error of optimal Bayesian combination method when λ * =7 are lower than those of simple combination methods. So, the optimal Bayesian combination forecasting model performs better than other combination models in retail collaborative forecasting process. This simulation research proved that the optimal Bayesian combination forecasting method is an effective approach to integrate and coordinate the forecasting process among partners in retail supply chain. Bayesian combination forecasting method can highly improve the demand forecasting accuracy of collaborative forecasting activity in the retail supply chain.

Conclusion

The collaborative planning, forecasting and replenishment framework provides the practical roadmap for retail supply chain coordination. The collaborative forecasting is taken as the core part CPFR solution implementation. A Bayesian combination forecasting method, which can combine individual forecasting methods from different parties in the retail supply chain, is modeled for CPFR collaborative forecasting process. The simulation results showed that forecasting discrepancies are reduced and collaborative forecasting accuracy is improved after integrating forecasting process with the optimal Bayesian combination forecasting model. It is turned out that the Bayesian combination forecasting method is an effective means for collaborative forecasting process in retail supply chain. The further research on collaborative forecasting methodology for supply chain coordination will be extended into different statistic features of product demand situation in the future.

Table 1

 1 The Sales Data of Biscuit in Carrefour Supermarket

	Week	1	2	3	4	5	6	7	8	9	10 11 12 13
	Demand 64 78 62 38 59 13 99 82 105 56 56 88 93
	Week	14 15 16 17 18 19 20 21 22 23 24 25 26
	Demand 100 110 135 124 105 86 117 172 185 169 192 195 156
	Week	27 28 29 30 31 32 33 34 35 36 37 38 39
	Demand 136 132 183 84 92 119 137 107 80 218 167 156 170

Table 2

 2 Forecasting Results of Five Individual Forecasting Methods

	Week	Actual Demand	F1	F2	F3	F4	F5

Table 3

 3 The Comparison of Bayesian Combination Models Forecasting Accuracy

	Combination	Forecasting	SSE	MSE	MAPE MSPE
	Methods Simple Average Method	2.64E+04	2.40E+03	0.3589 0.3725
	Optimal Linear Method	2.52E+04	2.29E+03	0.2734 0.3416
	Bayesian Combination λ*=7	1.92E+03	1.74E+02	0.0803 0.0561

Acknowledgments. This research was supported by a grant from the Shanghai Science Foundation Council (12ZR1400900) and the Chinese National Science Foundation Council (71172174).


Mengmeng Zhang

Junxian Liu

Aimin Luo
email: amluo@nudt.edu.cn

Xueshan Luo
email: luoxueshan@gmail.com

System Structure Risk Metric Method Based on Information Flow

Keywords: System Structure, Risk, Information Flow, GIG

The measurement of structure risk aims to analysis and evaluate the not occurred, potential, and the objectively exist risk in system structure. It is an essential way to validate system function and system quality. This paper proposes the risk metric model and algorithm based on information flow and analysis risk trend between traditional tree structure and network-centric structure.

Introduction

With the development of information and network technology, the environment's uncertainty, the mission's complexity and the system functions' diversity have made the traditional platform-centric, tree structure information systems become network-centric information systems. System structure is the sum of the various relationships between the various components in information system. System functions are the characteristics and capabilities represented by system unit and relationship. System structure reflects the functions of the system through connection and topology. The optimalization of system structure can improve the ability of information system. Structure risk is an important limiting factor in structure optimalization. The basic meaning of risk is uncertainty of loss. However, there isn't common concept applied to all areas . This paper argues that the risk of system structure refers to the risk probability and consequences of the risk event due to system specifications immaturity. System structure risk metric utilizes a certain method to calculate risk value by quantifying and integrated process .

The generating of information superiority in information systems rely on the producing, processing and utilizing of all kinds of information. When optimizing system structure, it should analysis the information flow in-depth. The system function can be abstracted as an orderly flow of intelligence, command and control and state information, including structure risk.

Background

Generally speaking, risk is the possibility of loss, injury, disadvantage or destruction, which is usually calculated through matrix or multiplication. While system structure risk consists of two levels of meaning: the first is the system performance risk; the second is the risk in the process of system structure migration.

Performance risk is the possibility that missions couldn't be completed because the stoppage of system units or relationships. System structure migration risk is mainly used to measure the influence of system migration failure or cost and schedule impacted than expected because of technological immaturity and uncertainty when system structure program development or system migration. The system structure migration risk includes technical risk, schedule risk and cost risk. Because this paper mainly considers the design phase of system structure, the system structure migration risk is not the main content of the study.

Complex network [START_REF] Barabási | Emergence of Scaling in Random Networks[END_REF] is a complex structure consisting of huge number of nodes and relationships, which is a new and important method to represent system structure. However, complex network mainly considers the structure ability represented by topology while ignoring role of nodes. OPDAR model is an extended model of complex network through the classification of nodes which express the system structure better.

Risk Metric Method Based on Information Flow

System structure risk metric utilizes a certain method to calculate risk value that form quantified and integrated process of system structure risk. Usually the system structure risk can be used as one of the necessary constraints to optimize a system structure, of course, also used as a separate target to evaluate system structure.

System structure information flow model

This paper adopts OPDAR information flow model in references [START_REF] Lan | A Method to Analyze the Timeliness of the Networked C4ISR System[END_REF] to describe the information system structure, in which exists four basic units, namely Observer, Processor, Decision and Actor, also the relationships between the system units.

There are three kinds of information flow through the combination of system units and relationships: Intelligence information flow, Command and Control information flow, Cooperation information flow. Each kind of information flow refers to a functional link that information generating, and utilizing.

Analyzing the information flow of Intelligence support and share reflect the system's ability to safeguard intelligence, analyzing the information flow of command and control reflect the system's ability of decision making, analyzing the information flow of feedback and cooperation reflect the system's ability of synchronization . And then the overall performance of the system structure can be obtained through information flow.

Information Flow Risk Model

In OPDAR model, the information flow is a link combined by relationships end to end in structure. From a risk perspective, the relationships and units in system structure in the information flow are all risk factors. Each node or arc in information flow corresponds a risk event, contains a certain probability of occurrence and consequence.

Supposing information flow

n n v e v e v e v f  2 2 1 1 0  is a simple path from system unit 0 v to system unit n v . The occurrence probability of risk event A i of unit i v is i p , and risk consequence is i c , n i   0 ; the occurrence probability of risk event B i of arc i e is i q , and risk consequence is i d , n i   0 . Thus the risk of n n v e v e v e v f  2 2 1 1 0  can be represented as follows.       n n n n n n n i i i i i i i c A P d P P c A P d P P c A P d P P c P f R ) , B ( ) B ( ) B B , A A ( ) , B ( ) B ( ) B B , A A ( ) , B ( ) B ( ) A ( ) A ( ) ( 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 0                   
Assuming the risk events corresponded to nodes and arcs in information flow occur or not are independent to each other, so

              n i i j j i j j i i i i i q p c p q d q c p f R 1 1 1 1 0 0 0 ) 1 ( ) 1 ( ) ) 1 ( ( ) ( (3.1)
Obviously, the key factor of the system structure risk is how to calculate the risk of each information flow, which have to rely on the system unit as well as the relationship in information flow.

In system design phase, it is difficult to calculate the failure rate of the system unit because the actual system units have not been finished. Therefore, when calculating the probability of system structure performance risk, we can assume system units themselves run without a fault and only consider fault caused by the accessing and supporting the structure.

Under the above assumption, regardless of the kind of system unit, the failure rate is identical, denoted

p , 1 0   p ; system relationship is denoted as q , 1 0   q .That in (3.1), p p p p n      1 0 , q q q q n      2 1 ,so               n i i j i j i i q p pc q qd pc f R 1 1 1 1 0 0 ) 1 ( ) 1 ( ) ) 1 ( ( ) ( (3.2) Which is          n i i i i i q p pc q qd pc f R 1 1 0 ) 1 ( ) 1 )( ) 1 ( ( ) ( (3.3) (3.
3) illustrates the key of information flow risk calculation is to determine the consequences of risk events occur, such as system units or relationships failure or interruption. In this paper, we use the "contribution" of unit(relationship) to repress the consequence of the unit's(relationship's) risk event occur, which consists the ratio between the number of information flows contained the unit(relationship) and all the information flows in structure and the rank in each information flow.

System Unit Risk Model

Assuming the set of intelligence information flow in system structure is

} , , 2 , 1 | { IS f IS i IS f N i f S    , the set of command and control information flow is } , , 2 , 1 | { CC f CC i CC f N i f S    , the set of cooperation information flow is } , , 2 , 1 | { CO f CO i CO f N i f S    . The weight of each kind of information flow is IS  , CC
 and CO  .

For each system unit

i node ) 1 ( n N i  
, denoting the number of information flow which contain this unit is

CC i  , IS i  and CO i  , denoting the system unit weight of each kind of information flow is CC  , IS
 and CO  .Assuming i node as i v , according to (3.3) , the "contribution" the system unit to the risk of the information flow is , the system unit's "contribution" for the system structure intelligence risk is

i i CO f CO i CO IS f IS i IS CC f CC i CC q p N N N p ) 1 ( ) 1 (                   (3.4)
Supposing CC i  , IS i  , CO i  is denoted as i v , given that 1 1 0    p and 1 1 0    q , so
                 IS i IS j j i CO f CO i CO IS f IS i IS CC f CC i CC IS f IS i IS q p N N N N p node Risk          1 ) , ( ) 1 )( 1 ( ) ( (3.5) If the rank of i node in command and control information flow is ) , ( j i CC  , CC i j    1
, the system unit's "contribution" for the system structure command and control risk is

                 CC i CC j j i CO f CO i CO IS f IS i IS CC f CC i CC CC f CC i CC q p N N N N p node Risk          1 ) , ( ) 1 )( 1 ( ) ( (3.6) If the rank of i node in cooperation information flow is ) , ( j i CO  , CO i j    1
, the system unit's "contribution" for the system structure cooperation risk is

                 CO i CO j j i CO f CO i CO IS f IS i IS CC f CC i CC CO f CO i CO q p N N N N p node Risk          1 ) , ( ) 1 )( 1 ( ) ( (3.7)
Therefore, the system unit's total risk for system structure is

                                                      CO i CO CC i CC IS i IS j j i CO f CO i CO IS f IS i IS CC f CC i CC CO f CO j j i CO f CO i CO IS f IS i IS CC f CC i CC CC f CC j j i CO f CO i CO IS f IS i IS CC f CC i CC IS f IS i CO i CC i IS i q p N N N N p q p N N N N p q p N N N N p node Risk node Risk node Risk node Risk                            1 ) , ( 1 ) , ( 1 ) , ( ) 1 )
( 1 ( ) 1 )( 1 ( ) 1 )( 1 ( ) ( ) ( ) ( ) ( (3.8)

System Relationship Risk Model

For each system relationship .According to (3.3) , the "contribution" the system relationship for the risk of the information flow is

1 ) 1 ( ) 1 (              i i CO f CO i CO IS f IS i IS CC f CC i CC q p N N N q       (3.9)
Similar to system unit, the more forward position



, the more contribution to flow risk, conversely smaller.

Denoting the set of system relationship in system structure is

} , , 2 , 1 , , | ) , ( { r node e i s i e i s i i r N i S node node node node r S      。 For each system relationship i r ) 1 ( r N i   , if the rank of i r in intelligence information flow is ) , ( j i IS  , IS i j    1
, the system relationship's "contribution" for the system structure intelligence risk is

                 IS i IS j j i CO f CO i CO IS f IS i IS CC f CC i CC IS f IS i IS q p N N N N q r Risk          1 ) , ( ) 1 )( 1 ( ) ( (3.10) If the rank of i r in command and control information flow is ) , ( j i CC  , CC i j    1
, the system relationship's "contribution" for the system structure command and control risk is

                 CC i CC j j i CO f CO i CO IS f IS i IS CC f CC i CC CC f CC i CC q p N N N N q r Risk          1 ) , ( ) 1 )( 1 ( ) ( (3.11) If the rank of i r in cooperation information flow is ) , ( j i CO  , CO i j    1
, the system relationship's "contribution" for the system structure cooperation risk is

                 CO i CO j j i CO f CO i CO IS f IS i IS CC f CC i CC CO f CO i CO q p N N N N q r Risk          1 ) , ( ) 1 )( 1 ( ) ( (3
.12) Therefore, the system relationship's total risk ( i r ) for system structure is

                                                      CO i CO CC i CC IS i IS j j i CO f CO i CO IS f IS i IS CC f CC i CC CO f CO j j i CO f CO i CO IS f IS i IS CC f CC i CC CC f CC j j i CO f CO i CO IS f IS i IS CC f CC i CC IS f IS i CO i CC i IS i q p N N N N q q p N N N N q q p N N N N q r Risk r Risk r Risk r Risk                            1 ) , ( 1 ) , ( 1 ) , ( ) 1 )( 1 ( ) 1 )( 1 (
) 1 )( 1 ( ) ( ) ( ) ( ) ( (3.13)
Therefore, the calculating process of system structure risk consists four steps: first, traversing all information flows in system structure; second, determining every system unit's rank in each information flow and computing the total contribution; third, determining every system relationship's rank in each information flow and computing the total contribution; forth, summing all system units' risk and system relationships' risk to obtain the system structure's risk.

Case Study

This paper compares traditional tree network structure with network-centric structure, which is good for comparative analyzing risk trend in network-centric structure and providing guidance for network development.

Network-centric structure dynamic organize and optimize the distribution of the loosely coupled system component in network, which can maximize system's function and capability and then realize the goal of dynamically adaptation to environmental changes. Reflected in the physical structure is that none of the system component is a must exist one, named "equality." Its manifestation includes circle, center or connection of tertiary-level in structure and so on.

The selected structure is Fig. 1 and2. Respectively, circle, triangle, square and diamond represent observer unit, process unit, decision unit and actor unit. Fig. 1 shows a traditional three-tree network which according to the triangular organization. Each decision unit has three child decision units and bottom decision unit controls two actor units. Each processor unit guarantees six decision units and has three father observer units. Fig. 2 adds some "horizontal" factor. (1) forms a p-circle by adding some cooperation relations among processor units. (2) forms a p-center by adding some intelligence relations among processor units and decision units. (3) joins some command and control relationships of tertiary-level. (4) joins intelligence relationships between processor units and actor units which forming safeguarding of tertiary-level.

Observer

Processor Decission Actor 4 shows the risk value in system structure in about 0.01, to the more realistic.

After analyzing the result, when adopting (1), the risk of cooperation flow appears. Because the number of cooperation flows is few, the risk value is relatively large. If we want to decrease the influence of cooperation flows, we can add the number of cooperation flows or decrease the ratio that flow through cooperation units. When adopting ( 2) and ( 4), the number of intelligence flows is increased, therefore risk of intelligence flow increases. However, the ratio that through decision units decreases, the risk of command and control information flows decreases. When adopting (3), the number of command and control flows is increased, therefore risk of command and control flow increases.

Overall, the total risk of system structure is increased relatively. But to some extent, we can adopt some measures to decrease risk. However, as the numbers of units or relationships getting larger, the cost increases relatively, which is an important factor in structure development we have to think.

Conclusion

This paper utilizes information flow in the risk metric of system structure, proposes risk metric model and algorithm based on information flow. In the basis of comparative analyzing different structures' risk trend, we conclude that:  There are three reasons affecting the change of structure risk: the number of information flows; the proportion of the number of flows through units or relationships and system structure flows; the rank that the unit or relationship in each information flow;  The general trend of structure risk is increased. Risk can used as a main consideration indicator of structural optimization, considering how to improve the system structure utility under the premise of meeting the constraint of the risk;  The adding of "horizontal" factor in structure makes the risk value reduced possibly;  When considering structural risk optimization, it may be appropriate to add some units or relationships that make certain types of information flow sudden increase except for the zero case, which reduces the risk. The physical meaning of such units' adding improves the probability of mission completing, which reduces risk of failure, but when the unit and the relationship continues to grow, the new unit and the relationship's risk becomes the dominant factor in the information flow risk, the risk becomes large.

  ), the more contribution to flow risk; the more rearward position in the flow( i bigger), the little contribution to flow risk.If the rank of

,

  denoting the number of information flow which contain this relationship is CC i  , IS i  and CO i  , denoting the system relationship weight of each kind of information flow is CC 

Fig. 1 structure 1 Fig. 3 Fig. 4

 134 Fig. 1 structure 1

Observer

Processor

Decission Actor

(1)

(3) (4)

Fig. 2 structure 2

Assuming the probability of system unit's risk event occurrence is 0.1, the probability of system relationship's risk event occurrence is 0.1.Because the command and control relationship is more important in information transmission in information system, flow weight of command and control information flow is 0.5, the other two is 0.25. The same is with unit weight and relationship weight . To analysis the change of risk in-depth, extract the common law, we analysis the risk change process with the combination. The risk value of different combinations((1)(2)(3)(4)) is in Fig. 3 and Fig. 4. The intelflow represents intelligence information flow, c2flow represents command and control information flow, coflow represents cooperation information flow. Fig. 4 shows the average risk value of system structure.


H E Qiushuo
email: heqs@sziit.com.cn

An Empirical Study about the Marketization Degree of Labor Market from the Perspective of Wage Determination Mechanism

Keywords: Labor market, Wage determination mechanism, Marketization degree

Ten years ago, economists had raised the issue of whether economic freedom can be measured, and there are a large number of domestic and foreign institutions and individuals had researched the measure of marketization degree, but it is few to measure the market degree of the labor market from the perspective of wage determination mechanism. This article attempts to investigate by using the stochastic frontier method of wage determination mechanisms from the micro-perspective, and measure the marketization degree of labor market, but also examine China's labor market and wage reform process, understand and grasp the labor market. At the same time, the research result will feedback the effect of labor market reform and promote the reform of labor market.

1

Background and Objective

Ten years ago, economists had raised the issue of whether economic freedom can be measured, and there are a large number of domestic and foreign institutions and individuals who had researched the measure of marketization degree, but it is few to measure the market degree of the labor market from the perspective of wage determination mechanism ( [2] degree of market about wage). Most researches focus on the part of measure of labor market, which is the part of the measure of overall market. This article attempts to use stochastic frontier method to measure the trend about the degree of marketization of wage determination of Chinese labor market from the perspective of individual human capital promoting the potential income (i.e. market value), thereby measuring the marketization degree of the labor market.

The Research Methods

This article attempts to use stochastic frontier method from the perspective of wage determination mechanism to measure marketization degree of labor market, it will refer to Contreras' method of robustness test and add to more related omitted variables such as work experience squared, marital status, sex, occupation nature except level of education, work experience.

3

The Empirical Analysis

Stochastic Frontier Approach

The basic idea of the stochastic frontier method is as follows. Considering a group of individuals, each individual has the same level of human capital. It is obvious that each individual won't own the same income level, because even assuming the same level of human capital, different individual is in different environments such as the department environment, family environment, working industry etc. and this will effect the income level of individual. And so different individual will get different income level, and degree of marketization of the wage determination mechanism will be different. Therefore, if the individuals can get their potential income, then it indicates that the labor market in their wage determination mechanism is completely market-oriented.

Stochastic frontier model is as follows [START_REF] Sdpc Market | Price Research Group: Judgment of China's economic marketization degree[END_REF] :

it it it v X E it        ln
Potential wage (wage border)

Random wages border

Observations wage (real wage)

That it it it v     , ) , ( ~2 it it it m N   , ) , 0 ( ~2 v it N v



Where i represents the number of samples,i=1,…n;t is the time series,t =1,…T,Since this article attempts to get the marketization degree of wage determination mechanism on the labor market each year, so this article will adopt sectional data, and T=1。Thus, the equation becomes:

i i i i v X E i        0 ln i=1,2,…N (1)
Here i E is the observable income of individual i, Xi is a vector of explanatory variables, i v is the white noise, representative of the random error term, subject to the normal distribution of the expected value of 0, and variance s v 2 , and is independent to i  . i  is non-negative random variables and can measure the degree of non-marketization, subject to the normal distribution of the expected value reflects the degree of [START_REF] Sdpc Market | Price Research Group: Judgment of China's economic marketization degree[END_REF] The prototype of the model equation derived from the income equation belonging to Jacob, but here we use stochastic frontier approach to estimate. non-marketization of individual i obtaining the potential wage, random items i  is limited to non-negative, because some individuals may get random wages border which is

i i v X    
. The parameters in the Equation 1 are estimated to use the least squares method and the method of maximum likelihood estimation method.

2 2 2 v         ( 1 0    can make 2 2 2       v ) (2)
Which  represents the proportion of non-marketization in the random disturbance term, 2   is the variance of the difference of marketization degree, [START_REF] Zongsheng | Marketization process research of China's economic system[END_REF]  is the summation between 2   and the variance of the random noise ( s 2 = s v 2 +s m 2 ). We won't use OLS estimation unless 0 ...

1 0      i    
. When  is close to 1, it indicates that the deviation of the stochastic frontier income function comes mainly from random variables i  , and also indicates that the gap between the real income and the boundary income (ie, potential revenue) primarily dues to the non-market of wage determination mechanism. When  is close to 0, it indicates that the gap between the real income and the maximum possible income mainly dues to the statistical error.

Data Description and Variable Description

In this paper, we use the Chinese Nutrition and Health Survey (CHNS) data. CHNS is operated by international research team, and funded by the American University of North Carolina Carolina Population Research Center (since 1989). The survey included 3800 towns and villages; 14,000 people in total. This paper uses 1989, 1991, 1993, 1997, 2000 and 2004. Since this paper attempts to find the trend of the different marketization degree of wage determination mechanism, so each year is as a cross-section data to analyze in this paper. It will remove the rural areas sample and only leave the working-age population in urban areas sample (survey for the resident

Empirical Results and Discussion

This paper uses stochastic frontier method to estimate the existence of non-market in the model. The results are shown in Table 2. Here we use stata statistical software.

Analysis

First we examine the result in Table 3 (a). The empirical result of using the maximum likelihood method shows that the degree of wage determination mechanism of the market is not simply random error distribution. Empirical results suggest that it can reject the null hypothesis at the 5% significance level. The difference of marketization degree is existed in the individual of the sample. T-statistics of  shows that  is statistically non-zero from the Table 3 (a). From Fig. 1, we are sure that marketization degree of wage determination mechanism first decreased and then increased gradually in the entire time series, in 1993 it is obvious turning point. Marketization degree of wage determination has been low before this point. The average degree of the whole marketization degree is approximately 20%, the average marketization degree gradually increases to above 50% after 1997, which partly shows that Chinese labor market reform is successful. But in absolute terms, compared to developed countries it is substantially lower, because in United States it is 86% in the same period [7].

From the perspective of human capital investment, Table 3 (a) shows that the impact of education on wages border is very significant, and its coefficient indicates that wage boundary will rise as the year of education is added one (it was 0.7% in 1989, and has been increased to 3% in 2004). And this suggests that the impact year of education on wage border is increased year by year, and the impact of experience on wage border is very significant, but little change.

Overall, the marketization degree of wage determination mechanisms is low, and its evolution path has been relatively consistent with China's development. For example in Fig. 1, the marketization is lower in the early 1990s, which may be mainly two reasons. On the one hand, although the government had began to reform wage before 1993, such as proposing the wage relevant to economic efficiency of enterprises in 1984 and also practiced various forms of wage system. But the reforms didn't change the wage determination of enterprise. The difference of wage determination is huge between the two types of market. In result, the income gap between the two markets is not only reduced, but expanded.

On the other hand, the affection of wage system reform is not obvious. There are two reasons: One is that the concept of return on human capital in the early 1990s might not be very clear. At that time, China adopted a unified wage policy and the gap is not obvious between the wages of various positions, so the status of competitive price system about the marginal productivity compensation paid to workers is not yet stable, especially in the Chinese state-owned sector, competitive wage payment mechanism is far from established. Second is the price information has been vacant in the state-owned economy, because the information is inefficient and the fact was that workers were short of the information about the work accumulation with workers' human capital. More seriously, the country's income level is very consistent, especially in state-owned enterprises. Although the wage was higher in non-state-owned enterprises but the staff mobility was not high due to social security and labor ideology. Because labors were lazy to learn about wage information of other companies, so the wage of workers cannot reach its borders largely.

Also from Fig. 1, marketization degree of wage determination mechanism began to shoot up after 1993, which indicates that the labor market situation had improved significantly. In 1993, the government officially announced the goal as the socialist market economic system reform, and selects the 100 state-owned enterprises to implement the corporate system. State-owned enterprises speed up to change from the planning system to market economy system. At the same time, the government began efforts to build a comprehensive social security system. Labor law formally was enacted in 1995. The government began to build macro-guidance system of enterprise wage income and establish wage guidelines system. Thus series of market-oriented reform made the individual human capital returns to rise naturally. The marketization degree of wage determination mechanism had been continuously improved.

Stability Test

This paper discusses whether the result is significantly changed after adding more relevant omitted variables using Contreras method. This paper used the same method to estimate [Equation 1]. In addition to the above estimates used by the relevant variables (level of education, work experience and work experience squared), we now add new variables which include the level of education (education), work experience (exp) and work experience squared (exp 2 ), marital status (mar), gender, occupation nature, where corporate ownership dummy variable (SOE) -to estimate the equation [Equation 1].

Estimation results are in Table 3 (b). it is worth noting that the change of marktization degree is small in all time, and basically all the newly added variables were significant. Thus confirms our results stability.

We see that adding gender variable is significant, suggesting that the impact of gender on wages boundary is significant, and wages border of men is average higher around 7% than women. But we examine that men coefficient is 0.0693 in 1989 and rises to 0.0743 in 1993, but began to decline after 1993. This suggests that the impact of gender on wages border is waning after 1993. Marital status variables are most significant, and results also indicate that unmarried can raise wages border around 6% relative to married persons, the most important fact is the coefficient of sector is significant at the 1% level of significance. Before 2000 coefficient is negative which showed that wage boundary in state-owned enterprises was lower than in non-state-owned enterprise. It is consistent with our expectation.

Due to lack of competitive in SOE labor market early, wages and employment determination were more planned (which has been demonstrated in previous chapters), so the wages of workers were lower. Rather than non-state-owned enterprises had been in a competitive labor market, so its wages were relatively higher. However this situation has changed from 2004 (2004's coefficient symbol is changed and a very significant), which also indirectly shows that a series policies about wage and employment of state-owned enterprises play a role and reforms began to show results, particularly human capital returns gradually increased significantly which suggests that the marketization degree of wage determination mechanism gradually increased and also shows that the marketization degree of the labor market is also improving.

Conclusions

This paper estimates the marketization degree of the wage determination mechanism. We found that the marketization degree of wage determination mechanism is very low before the 1990s.1993 was a turning point. The marketization degree of the wage determination mechanism had been low before this point. The average degree of the whole marketization degree is approximately 20%, the average marketization degree gradually increases to above 50% after 1997, which partly shows that Chinese labor market reform is successful. But in absolute terms, compared to developed countries it is substantially lower, because in United States it is 86% in the same period [7].

Overall, the result is consistent with the path of China's labor market reform. These empirical studies show that there are some factors that affect non-market-oriented. There is not a bargaining system between labor and State-owned enterprise, and the government still plays a key role in wages of workers' determination. Enterprises have not the autonomy of wage determination. The marketization degree of wage determination in the market is low. While workers' bargaining power has increased in the non-state-owned enterprises, but it is thin in an oversupply of labor market, which also shows that China's labor market need to deepen reform, improve the marketization of wage determination mechanism.

Table 3 (

 3 a) indicates that the parameter at the 5% significance level is significant.  is close to 1. It indicates that there are factors from the non-market in the random disturbance term, other factors come from other exogenous variables such as the statistical error. In addition, based on the results in 2004, we can estimate degree of marketization of each individual and calculate the average marketization degree of all individuals in the sample is 63%, which indicates that the degree of non-marketization is about 37 percent comparing to the individual of the highest marketization level of wage determination on the random border (the highest marketization level of wage determination is 1) 2 .

Fig. 1.Degree of

  Fig. 1.Degree of wage determination market trend.

Table 1 .

 1 Definition of the variables

	Variable name
	lnWage	logarithm of hourly wage
	Education	years of education (years)
	Age	Age
	exp	Age -years of education -6
	Exp 2	work experience squared
	Gender	1: Men 0: Female
	Marriage	1: Unmarried 0: Married
	The nature of a	1:SOE 0: Non-state-owned enterprises
	work unit
	Regional dummies	1: Elsewhere 0: Guizhou

Table 2 .

 2 Sample statistics description.

	State sector

  **, and *** represent 10%, 5% and 1% significance level,  is the variance estimation parameters

	The	average	0.37158	0.37158	0.27245	0.27245	0.111	0.111	0.339	0.339	0.5123	0.5123	0.63	0.63
	degree	of
	market
			Note: *,

Mentioned before, the results of marketization degree of wage determination in[START_REF] Zongsheng | Marketization process research of China's economic system[END_REF] were 5.44%, 28.98%,

32.63% and 78.80% in 1979, 1985, 1990 and 1995, although this measure is about different year , but from the overlap of the Year 1990 and the 1995, the result is less than Chen's results, which may be due to the different methods and the different measurement range, so the contrast is not very strong.


Huiying Gao
email: huiying@bit.edu.cn

Susu Wang

, Lily Sun

Fuxing Nian

Hierarchical Clustering based Web Service Discovery

Keywords: Web service discovery, semantic analysis, hierarchical clustering, service matching, vector space model, Web service description 1

This paper presents a hierarchical clustering method for semantic Web service discovery. This method aims to improve the accuracy and efficiency of the traditional service discovery using vector space model. The Web service is converted into a standard vector format through the Web service description document. With the help of WordNet, a semantic analysis is conducted to reduce the dimension of the term vector and to make semantic expansion to meet the user's service request. The process and algorithm of hierarchical clustering based semantic Web service discovery is discussed. Validation is carried out on the dataset.

Introduction

Web services describe a standardized way of integrating Web-based applications using open standards, such as Extensible Markup Language (XML), Simple Object Access Protocol (SOAP), Web service description language (WSDL) and the Universal Description, Discovery, and Integration (UDDI) over an Internet protocol backbone [START_REF] Thomas | Service-oriented Architecture Concepts, Technology, and Design[END_REF]. Popova et al. [START_REF] Popova | Web Services-an Instrument to Resolve the Problems of Information Systems Integration[END_REF] believe that Web service can enable the applications built on different servers to be accessed more easily, independent of platforms and programming languages. In recent years, especially with the development of cloud computing, different types of Web services are emerging [START_REF] Yue | The Core Support Technology of Web Service: Review[END_REF]. As the rapid increase in the number of Web services, however, looking for the Web service in the online registry is like looking for a needle in the haystack. A better way for Web services discovery is to classify the Web services into different categories based on semantic analysis when they are published so that the matching algorithm can be done only in the related category with high efficiency and accuracy.

There are many approaches to build automatic taxonomies, such as rule based approach, semantic analysis, cluster analysis and learning algorithms. The trend by more and more taxonomy systems is to combine multiple methods based on various algorithms using statistical method, semantic analysis and clustering technique. This paper aims at a hybrid solution for Web service discovery by combining semantic analysis with the techniques of hierarchical clustering.

The remainder of the paper is organised as follows: Section 2 discusses the state of art in relation to the methods of Web service matching. Clustering analysis is also argued. In Section 3 the semantic expansion of Web service is described and a hierarchical clustering based Web service discovery algorithm is put forwarded to classify and discover the Web services automatically. By using a Web service testing set, Section 4 illustrates the discovery process and validates the feasibility of the proposed method. Finally Section 5 draws the conclusion and flowed by the outlook.

2.

Literature Review

Web Service Matching

A lot of research work has been conducted to solve the problem of Web service discovery. Two important ones are 1) Web service matching algorithm with high recall, precision and efficiency, and 2) the effective services selection and ranking methods in the found preliminary services set. The Web service matching is mainly through three stages [START_REF] Liao | Review of Web Service Discovery Technology[END_REF]: a grammatical matching method based on keyword or vector space model such as UDDI systems of IBM, Microsoft, and SUN; a semantic matching method based on ontology such as OWL-S, METEOR-S and WSMO; a service matching method based on information search technology, for example, Niu et al. [START_REF] Niu | Semantic Web Service Discovery Based on Context and Action Reasoning[END_REF] have devised a semantic Web service discovery method based on context and action inference.

Clustering Algorithm

Clustering is a process of dividing data objects into several classes or clusters to make the similarity between different clusters maximal and the similarity between objects within one cluster minimal [START_REF] Ertel | Introduction to artificial intelligence[END_REF]. Clustering algorithm can generally be divided into five categories [START_REF] He | Summary of Clustering Algorithm in Data Mining[END_REF][8]: hierarchy-based approach, division-based approach, densitybased approach, grid-based approach and model-based approach. Each method has its own advantages and disadvantages. As the hierarchy clustering method can show all the whole process of clustering, so we can confirm the number of categories by analyzing the process. It may reduce the time spending on determining the number of categories.

The basic hierarchical clustering algorithm can be divided into agglomerate hierarchy clustering algorithm (AGNES) and divisive hierarchy clustering algorithm (DIANA). The idea of agglomerate algorithm is that treat every object as a separate class, then merge the two closest clusters and update the original distance between clusters and the new cluster, repeat this step until all objects are in one cluster; while the idea of divisive algorithm is contrary [START_REF] Liang | Detection of Top-n Global Outliers in Datasets Based on Hierarchical Clustering[END_REF].

As a technique widely used in the field of information search, clustering method improve the efficiency of information discovery to some extent. There are also some studies on the utilization of service clustering to improve efficiency of Web service discovery. For example, Rajagopal et al. [START_REF] Rajagopal | Semantic grad service discovery approach using clustering of service ontologies[END_REF] propose a service discovery method which is based on ontology clustering for grid service. Sudha et al. [START_REF] Sudha | A Clustering Based Approach for Facilitating Semantic Web Service Discovery[END_REF] complete the service clustering based on the WSDL to improve the efficiency of service discovery. Sun et al. [START_REF] Sun | Process Model-oriented Semantic Web Service Discovery Using Service Clustering Optimization[END_REF] cluster the services with high function and process similarities based on Petri Net. Wang et al. [START_REF] Wang | Web Clustering and Composition based on P2P[END_REF] propose a service discovery method by combining the ideas of P2P and clustering. Xu et al. [START_REF] Xu | Web Service Discovery Method based on Clustering Optimization[END_REF] propose a discovery method based on graph theory. Yahyaoui et al. [START_REF] Yahyaoui | A Novel Non-functional Matchmaking Approach between Fuzzy User Queries and Real World Web Services Based on Rough Sets[END_REF] introduce a novel matching approach which allows reducing the matching space through fuzzy classification rules. Facing the explosive increase of Web service, combine semantic analysis together with clustering technique to improve the accuracy and efficiency of Web service discovery is a trend.

The Method of Hierarchical Clustering based Web Service Discovery

In our approach, we aim to realize the semantic Web service discovery process based on a hierarchical clustering. The advantage of this approach is that it can generate a hierarchically nested clustering and has high accuracy.

Web Service Description

In order to conduct similarity calculation on Web services, Web service description documents should be abstracted mathematically based on WSDL [START_REF] Gao | Web Services Classification Based on Intelligent Clustering Techniques[END_REF]. According to the structure of the WSDL document, Web service can be defined as follows.

Definition 1 [START_REF] Liao | Review of Web Service Discovery Technology[END_REF] Service description is an abstract of functional attributes and process model of Web service which can be expressed as a four-tuples:

  sOutput sInput on sDescripti sName WS , , ,
among which, sName is the service name on sDescripti is the service description } ,..., , { 2

1 m I I I sInput  shows the input set of service } ,... , { 2 1 m O O O sOutput 
shows the output set of service

Corresponding to the description of Web service, we can define the functional similarity of two services as follows.

Definition 2 [START_REF] Ma | Semantic Web service Dynamic Discovery based on Evolution Distributed Ontology[END_REF] Define the similarity of service 1 ws and 2 ws in Equation ( 1).

(1)

Among which, 1  , 2  , 3  , 4
 are respectively the weight of name similarity, description similarity, input similarity, output similarity of Web service in the whole similarity, and 1

4 3 2 1         , 1 0 1    , 1 0 2    , 1 0 3    , 1 0 4    .
Service name similarity can be calculated by using:

) ( ) , ( ) , ( ) , ( ) , ( 2 1 4 2 1 3 2 1 2 2 1 1 2 1 So So Sim Si Si Sim Sd Sd Sim Sn Sn Sim ws ws Sim               q j p i j i concept n n Sim q p Sn Sn Sim 1 1 ) 2 ( ) 1 ( 2 1
) , ( 1) , ( Service description similarity can be calculated by using:

Service input similarity can be calculated by using:

Service output similarity can be calculated by using: among which, p , q are respectively the number of concepts in

       .
Similar to the description of Web service, a service request can be defined as follows.

Definition 3

The description information of service request of a Web service requestor can be defined as the four-tuples: is the output set that service requestor provides Semantics is introduced through the calculation of concept similarity and in generally, the similarity between two concepts can be defined as follows. Definition 4 [START_REF] Wu | Verb semantics and lexical selection[END_REF] In a ontology category, the similarity of two concepts can be defined by Equation [START_REF] Popova | Web Services-an Instrument to Resolve the Problems of Information Systems Integration[END_REF]. The value of Sim is between [0,1].


(

) 2
in which, LCS refers to the smallest common ancestor node of two concepts, depth represents the hierarchy depth of the repository, dis represents the shortest path length between two concepts.



    q j p i j i concept d d Sim q p Sd Sd Sim 1 1 ) 2 ( ) 1 ( 2 1
) , ( 1) , (

     q j p i j i concept i i Sim q p Si Si Sim 1 1 ) 2 ( ) 1 ( 2 1
) , ( 1) , (

     q j p i j i concept o o Sim q p So So Sim 1 1 ) 2 ( ) 1 ( 2 1
) , ( 1) , (

)) , ( ( * 2 )) , ( , ( )) , ( , (
)) , ( ( * 2 ) , ( 2 1 2 1 2 2 1 1 2 1 2 1 c c LCS depth c c LCS c dis c c LCS c dis c c LCS depth c c sim   

Web Service Discovery processes Based on Clustering

Web service discovery consists mainly of two basic processes: the clustering and matching of Web services. The introduction of semantic technology and clustering method may affect both the two processes and ultimately impact the efficiency and accuracy of Web service discovery.

Process of Web Service Clustering

Web service can be seen as a kind of a short text. It can be described by using the service name, service description, service input and service output at the same time. So the process of text clustering can be equally applicable to Web service clustering as shown in Fig. 1[19].

Fig.1. The clustering process of Web service based on service description documents

The main process of hierarchical clustering are described as follows.

Step 1. Preprocessing of Web services: To preprocess the Web service description text to complete the Web service description segmentation and stemming process.

Step 2. The establishment of Web service feature space: There is a variety of feature representations of Web service information. We adopt the vector space model [START_REF] Hamadi | A Petri Net based Model for Web Service Composition[END_REF] (VSM) which is one of the methods that have been widely used and got better results in recent years and Term Frequency-Inverse Document Frequency (TF-IDF) which is a weighting technique commonly used for information retrieval and text mining.

Step 3. Reduction of Web service information feature set: Filter the words extracted from the Web service description text, remove prepositions, pronouns etc that have no actual meaning, and then stemming. For the feature vector space of Web service got from Step 2, calculate the similarity of feature words and reduce the dimension of the semantic vector space. According to definition 4, similarities can be calculated. (3) in which each row of the matrix represents a service, element j i tfidf , represents the weight of service i on feature word j , s is the dimension of feature word vector.

Step 5. Web services clustering: The prerequisite of clustering is determining the similarity between Web services. As mentioned before, TF-IDF is normally applying together with cosine similarity. Therefore, the cosine coefficients can be calculated by Equation ( 4) [START_REF] Jing | A Text Clustering Algorithm Based on Semantic Inner Product Space Model[END_REF]. [START_REF] Liao | Review of Web Service Discovery Technology[END_REF] in which tfidf represents the TF-IDF weight of two Web service in the feature vector space, d represents the documents and T represents the feature word set.

Web Service Matching Process

Three main steps are involved in the Web service matching process.

Step Step 2. The semantic matching of service requests: according to definition 1 and 2, supposing that the feature vector of Web service set is } ,..., , { 2

1 s t t t T 
, then the matching process of traditional method will be: for all feature word in T , judge whether there is ws R . If there is, note the frequency of the word as 1; otherwise as 0; then treat it as a Web service to calculate the TF-IDF value in the space of T , represented as

) ,..., , ( , 2 , 1 , m tfidf tfidf tfidf tfidf R R R R 
. Therefore, the matching process is namely the process of calculating the similarity between tfidf R and all services in WS . Among them, the conversion from service request vector space to Web service feature word vector space can be achieved by Hungarian algorithm and the concept similarity is calculated based on WordNet.

          | | 1 | | 1 2 , 2 , | | 1 , , ) , ( ) , ( T k T
k k j k i T k k j k i j i j i tfidf tfidf tfidf tfidf d d Cos d d Sim                s n n n j i s s tfidf tfidf tfidf tfidf tfidf tfidf tfidf tfidf tfidf tfidf WS , 2 , 1 , , , 2 2 , 2 1 , 2 , 1 2 , 1 1 , 1 ...
Step 3. Service request matching based on clustering: After clustering based on Equation (4) the Web services storing in the database, the storage of Web service become different. Each cluster has a representative. If the new service has a higher similarity with a representative, it might mean that the similarity between the new service and all services in the cluster which the representative is in is higher than others. Before calculating the similarity between service request and all services, the similarity between Web service request and each representative should be compared. Select the clusters whose representatives have the top n similarity with the service request, calculate the similarity between the service request and all services in these clusters and finally return them to users by the similarity.

4.

Results and Discussion

In The distribution with 24 clusters is relatively the most average and thereafter every time a cluster is added, the distribution only adds a cluster with the frequency less than 5 while the impaction on the clusters with higher frequency is weak. So considering both average and simplicity of computation, we choose 24 as the number of clusters of experimental data.

According to the Web service matching process of Section 3.2.2, we take "movie price" as a service request and complement the experiment while "movie" does not appear in the feature vector and "price" exists in the feature vector. The gravity of every cluster is taken as representative and cosine similarity between all representatives and service request is calculated by software Matlab to compare the results of matching. Table 1 shows the results of similarity between representatives and service request. After clustering, the top ten clusters are chosen which have the greatest similarity between representatives and service request and are returned to users in the descending order.

Taking the reality into consideration, users' browse focuses on the results in the top pages, we select the top 100 Web services to calculate the evaluation indicators after the matching results are returned. Let average of ten times calculating results be the indicator value of two methods on time efficiency and let Recall rate = (number of related service discovered/total number of related)*100% Precision rate = (number of related service discovered/total number of service discovered)*100%. The results are shown in Table 2, from which we can see that the recall rate and precision rate in the experiment of the method proposed in this paper are 0,4167 and 0,2 respectively, both are higher than that of the traditional method without hierarchical clustering and semantic analysis. Yet the time consuming of our method is 0.1086 seconds which is less than that of the traditional method of 0.4441seconds. The verification results show the advantages of hierarchical clustering based Web discovery on recall rate, precision rate and time efficiency which indicates the effectiveness of the method.

Conclusions

The method of hierarchical clustering based Web services discovery is devised to improve the efficiency and accuracy of Web service discovery. The hybrid solution for building and populating a taxonomy structure of web services are studied by combining semantic analysis with clustering techniques. The verification is carried out by using the dataset of OWL-TC4.0 to demonstrate the acceptance of the hierarchical clustering method in Web service discovery. The work to convert the Web services into term vectors as well as the semantic expansion of the query of the user is done on WordNet, based on which a service provider and consumer would derive a meaning of the text or key word of the query or the Web service description document in a semantic level. However, a full understanding of web service potentiality can only be reached when the effects of the sign, made by contextual interpretation, is made by the service provider and consumer through consensus (pragmatics) [22]. In order to overcome limitations of this work, the future work will consider introducing domain ontologies in a semiotics way to make the Web service discovery context aware so as to improve the accuracy of service discovery in further step. Additionally, more properties like the preconditions and results of Web services in the Web service description document should be taken into account when the semantic vector space are set up.

  two services. If we ignore the difference between the name, description, input and output of service, then 4 3 2 1

Step 4 .

 4 The description of Web services collection: After reducing the feature set, the establishment of feature vector space of Web services is completed. Assuming that services set containing n Web services, in which i ws represents the number i service, then the service set can be represented abstractly by Equation (3).

  our research work we choose OWL-TC 4.0 as the data set. It is the fourth version of OWL-S service test set which provides 1083 semantic Web services from nine different areas. The method mentioned in the paper and the traditional method of Web service matching is realized on the same data set. Comparative analysis is conducted on the recall, precision and time efficiency. According to the advice of Lehmann that the number of categories should be between (in which n represents the number of samples), we can implement clustering using SPSS19.0 based on the above results. The cosine similarity can be the measurement of similarity between Web services according to the process of Web service clustering mentioned in Section 3.2.1. Comparing the clustering distribution with 19-36 clusters, some of the results are shown in Fig.2.

Fig. 2 .

 2 Fig.2. The clustering distribution histogram with 22-25 clusters

Table 1 .

 1 The calculating results of similarity between representatives and service request

	Cluster NO	Cluster size	Similarity (high to low)
	13	88	0.483482
	1	126	0.101886
	10	129	0.081205
	3	177	0.058091
	11	5	0.053409
	16	3	0.035541
	20	1	0.020473
	4	7	0.014712
	9	3	0.014255
	17	13	0.01176
	18	3	0.009699
	12	48	0.009568
	5	103	0.00072
	6	176	0.000445
	2	92	0
	7	74	0
	8	24	0
	14	4	0
	15	1	0
	19	1	0
	21	1	0
	22	2	0
	23	1	0
	24	1	0

Table 2 .

 2 The comparison between Web service discovery method based on traditional keywords and hierarchical clustering (based on the top 100 return results)

		Recall	Precision	Time-consuming(s)
	Traditional method	0	0	0.4441
	This method	0.4167	0.2000	0.1086

Acknowledgments. This work was supported in part by the Beijing Natural Science Foundation (Grant No. 9133020) and by the National Natural Science Foundation of China under Grant 71102111.


Yanru Qi

Yun Liu
email: liuyun@bit.edu.cn

Jing An
email: anjing@tobacco.gov.cn

A Comprehensive Evaluation Approach that Highlights the 'Equilibrium' and its Application in Evaluation on Regional Innovation Capability

Keywords: Comprehensive Evaluation, Multiple Attribute Decision Making, Relatively Balanced Coefficient

In this paper we proposed a novel method that aggregates the "Functionality" and the "Equilibrium" to calculate the weight of comprehensive evaluation problem, The method calculate the weight coefficient according to the principle of "variance drive" firstly, then considering the development of the system balance with relative equilibrium coefficient to arrive at the comprehensive evaluation results of various systems. The method reflects the scientificity, impartiality and rationality. So it can avoid the phenomenon "one swallow make a summer", which can promote the healthy development of the whole system. Finally, a numerical example was given to illustrate the effectiveness of the proposed method.

Introduction

Many comprehensive evaluation problems can be met frequently in realistic society, such as evaluation of the region innovation capability or urban competitiveness of a country or area. The method use now for this problems are classified into 9 classes, such as Comprehensive Evaluation methods [START_REF] Zongjun | On the methods, problems and research trends of comprehensive evaluation[END_REF][START_REF] Hwang | Multiple Objective Decision Making Methods and Applications[END_REF][START_REF] Fank | Issues in measuring industrial R&D[END_REF][START_REF] Charnes | Measuring the efficiency of decision making units[END_REF], Operational Research methods [START_REF] Coooper | Measures of inefficiency in data envelopment analysis and stochastic frontier estimation[END_REF][START_REF] Junxia | The application of composition DEA method in measuring the performance of knowledge management of enterprise[END_REF][START_REF] Xiaoqun | The Methods of Modern Statistics Analysis[END_REF], Statistical methods [START_REF] Savoy | Statistical inference in retrieval effectiveness evaluation[END_REF][START_REF] Wallmark | Quality of research measured by citation method and by peer review-a comparison[END_REF], Systematic Engineering methods [START_REF] Saaty | Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process[END_REF][START_REF] Schen | Avoiding rank reversal in AHP decision-support models[END_REF][START_REF] Carrettoni | RETISS: A real time security system for threat detection using fuzzy logic[END_REF], Fuzzy Mathematics methods [START_REF] Chen | Fuzzy Multiple Attribute Decision Making[END_REF][START_REF] Guohong | Fuzzy mode identification in R&D project termination[END_REF][START_REF] Dimitras | Business failure prediction using rough sets[END_REF][START_REF] Grabowski | An expert system for maritime pilots: Its design and assessment using gaming[END_REF].

Multi-index comprehensive evaluation is a kind of method to get a comprehensive index to make an overall evaluation and a vertical or horizontal comparison on evaluation object, by integrating the multiple index information which describes the different aspects of the evaluation object. This method is widely used in many areas since it could make accurate description for evaluation objects and process the dynamic objects which have many decision makers and indexes.

In the evaluation process, the comprehensive evaluation result is directly influenced by the determination of weight coefficient and evaluation index. There are several methods of determining the weight, which can be grossly divided into subjective and objective weighting method [START_REF] Fan | An integration weight determine method of multiple attribute decision making[END_REF]. Subjective weighting method such as Delphi and AHP often depends on subjective experience, the result of which instability with the influence of the expert experience or preference. Objective methods such as Maximizing Deviation Method [START_REF] Wang | Multiple attribute decision making and sorting with maximizing deviations method[END_REF] and Mean Square Difference Method [START_REF] Wang | Weight coefficient to determine the deviation and mean square deviation decision method in multi-index evaluation[END_REF] depends on practical data of every index, the result of these methods based on principle that "the difference is driven" may led to lopsided development of the evaluation system. To avoid the defects of subjective and objective weighting methods, a new weighting method is proposed [START_REF] Fan | The subjective and objective method in multiple attribute decision making[END_REF], and the new mathematical programming model synthesized both subjective and objective characters is established. This research will set up a comprehensive evaluation model that introduce "relative equilibrium coefficient" into the objective weighting method to encouraging the advanced and urging on the backward in evaluation. With all these efforts, we can expect to realize the health development of system.

In the real evaluation process, fewer scholars consider whether the indexes of evaluation objects are in balanced development. In this paper we proposed a novel method that aggregates the "Functionality" and the "Equilibrium" to calculate the weight of comprehensive evaluation problem, the method utilize the deviations between each indicator and the average value to calculate the in the multi-index comprehensive evaluation, then considering the development of the system balance with relative equilibrium coefficient to arrive at the comprehensive evaluation results of various systems. The case analysis result is given to prove the effectiveness of this method in the end of this paper. This method gives consideration to both functionality and equilibrium of evaluation objects and realize the function of encourage advanced spur lagging behind.

2

Determination of index weight coefficients

    , 1, 2, , ; 1, 2, , min x ij x i n j m ij x ij i     (4)
We mark

x ij  as x ij below for convenience
Gather the multi-target information with linear weighted model; assume that y i is the evaluating value of scheme o i , then,

1 1 2 2 1 ( 1, 2, 3, , ) y x x x m i im ii m T xx j ij i j in             (5)
In the formula ( , , , ) 12

T x x x x i im ii  , ( , , , ) 12
T m      is m -dimensional vector, name x i as the index vector of o i ,  as index weight vector. If 12 T y y y y n
   , then the formula (5) can be written as:

yA   (6)
We can regard n evaluation objects as n dots or vectors of m-dimensional space that consist of m-evaluation indexes. Then what we should do is select index weight coefficient to enlarge the difference of each evaluation objects and highlight the overall differences of each evaluation objects, based on that improved the method of maximizing deviations which was mentioned in literature [18] , and build the function

() 11 mn H MAX x x j ij j ji       (7) If define 2 1 1 m j j     (8)
in advance,then we can transform the issue into the maximum problem of type [START_REF] Xiaoqun | The Methods of Modern Statistics Analysis[END_REF] under this Limited-Term, that is select  to make . .

1 0 m j j mn H MAX x x j ij j ji st             (9)
Apply Lagrange conditional extreme to type (9), we can get

2 1 2 1 1 n xx ij j j i m j j         (10)
And we can obtain the weight coefficient after normalization processing as follows,

1 11 n xx ij j i j mn xx ij j ji          (21)
Utilize the weight coefficient obtained from formula [START_REF] Schen | Avoiding rank reversal in AHP decision-support models[END_REF] with formula (5), we can get the comprehensive index y based on 'differences drive'.

The analysis result of the system which is obtained from the evaluating value is independent on people's subjective judgment and strong objectivity. However, this evaluating value has the character of highlight indicators intrinsic difference, which can cause the system appear the "deformed" development phenomenon that focus on strengthening an index because of optimizing. In order to avoid the occurrence of this phenomenon, we introduce the system relative equilibrium coefficient concept. Meanwhile, we encourage these balanced development systems and punish these deformed development systems through the above coefficient to get the more close to the actual situation and acceptable evaluation result.

The introduction of relative equilibrium coefficient

In this paper, we will utilize the reciprocal of the coefficient of variation of decentralized characterization data to describe the balance of the system [START_REF] Mei | Data Analysis Method[END_REF]. We define system o i (while the index value of o i is data after dimensionless process)

    2 1 1 1 ( 0; 1, 2, , ) 1 max , ( 0; 0) m m x ij xx ij i m j j s i n JH i i s i m JH c s s i dd                     (32)
It is observed that the larger JH i , the smaller volatility of numerical value between the indicators in the system is. Otherwise, the bigger volatility of the system indicators, the less balanced development of the system is. We use formula [START_REF] Guohong | Fuzzy mode identification in R&D project termination[END_REF] to define the relative equilibrium coefficient of system.

1 1 JH i JH n i n JH i i n JH n i i         (43)
There from we can deduce that, for these n systems, if JH i  is less than 1, then there is a gap between the indicators of this system; if JH i  is bigger than 1, then indicators in the system is relatively balanced; if JH i  is equal to 1, then the development of indicators in the system is in a middle position among all the systems. For these systems that JH i  is bigger than 1, we should increase its value to some extent as motivation. Meanwhile, for these "deformed" developed systems, we should decrease its value as punishment so as to realize the disciplinary role of evaluation model.

A build-up model that introduce the relative equilibrium coefficient

On the base of relative equilibrium coefficient, we concentrate the system over again and obtain that

( 1) 1
1 1 n JH y ii i yy ii n n JH i i           (54)
Via the objective evaluation of the difference process of formula ( 14), we can realize the function of promoting the smooth development and punish imbalanced development.

Example

Utilize the functionality and balanced aggregate model to calculate the region innovation capacity data of 31 provinces as tab 1 in literature [START_REF] Guo | The report of Chinese regional innovation capacity of 2011-Study of regional innovation system in the Pearl River Delta[END_REF], x ij Was separately substituted into formula (5), we can get the comprehensive evaluation value of innovation capacity of provinces, autonomous regions and municipalities. We sort the value and compared with literature [START_REF] Guo | The report of Chinese regional innovation capacity of 2011-Study of regional innovation system in the Pearl River Delta[END_REF] and the results as shown table 3. The weight of each index provided by the literature [22] was   0.15,0.15,0.25,0.25,0.20

 

, compared with the weight indexes provided by the literature [22] , this paper pays more attention on the role of enterprise innovation while studying the regional innovation capability. And accordingly, the weights of innovation environment and innovation performance are relatively reduced. After calculating the comprehensive evaluation value of each region's innovation capability by using the aggregation model which focuses more on equilibrium, we get an evaluation result which is close to the one from literature [START_REF] Guo | The report of Chinese regional innovation capacity of 2011-Study of regional innovation system in the Pearl River Delta[END_REF].

During the calculation process, we find that, the innovation capabilities of Jiangsu province, Beijing city, Guangdong province, Shanghai city and Zhejiang province are ranked from 1 to 5 based on the 2 calculation results. That is, in the development process, all of these 5 regions have got leading advantage in innovation even they have different equilibrium in knowledge creation, knowledge acquisition, enterprise innovation, innovation environment and innovation performance.

At the same time it shows that, all the index developments from Guangdong province are in a more balanced pace than the other 4 regions. There is still room for Zhejiang province to improve its capabilities on knowledge creation and knowledge acquisition. And we also find Shanghai city has obvious shortcomings on innovation environment.

The regions ranked from 6-9 have no obvious differences from these 2 calculations. Most of these 14 provinces, cities and autonomous regions are located in eastern China. Considering the development equilibrium of each index, the innovation capability of Hunan province climbs up 3 places to the 8th comparing with the first time. While Hubei and Chongqing slips down 2 places.

The result shows that the utility value rankings of each index for Hunan province are the 8th, the 10th, the 13th, the 15th and the 9th, which means it has better equilibrium. By contrast of that, Hubei province should improve its capability of knowledge acquisition. And Chongqing should pay more attention on the development of knowledge creation capability.

The countries rank low in west of china, the innovation capabilities of most regions in western China are ranked backward. In the backward ranked regions, the regional innovation capability of Gansu province moves up 6 positions to the 22nd. Hainan and Yunnan move up 3 and 2 positions respectively to the 20th and 23th. While both of Jilin and Jiangxi declines 3 positions to the 24th and 25th respectively.

The analysis shows that the enterprise innovation capability of Gansu province is ranked at the 2nd in the last 12 regions. In addition, the developments of its knowledge creation, knowledge acquisition, innovation environment and innovation performance are in balance and without obvious differences. Among the last 12 regions, Jilin and Jiangxi have more advantages in the innovation performance than the other regions. But the former should improve its capabilities of knowledge acquisition and enterprise innovation. And the latter should pay more attention on its capabilities of knowledge creation and enterprise innovation.

Conclusions

For the issue of less consideration on whether the system is in balanced development when evaluating the regional economic development and industry innovation capability, this paper proposes a comprehensive evaluation model which could highlight the 'Equilibrium' of each evaluation index development from the evaluation object. And also, this model is applied to the comprehensive evaluation on our country's regional innovation capabilities. This model not only considers the influence on the evaluation result from each index value, and also, it considers the equilibrium of system development. During the evaluation process, it 'promotes' the system which has balanced development in each index. While it 'punishes' the system which only focuses on the development of some index and ignores the others' development. Finally that will aggregate the 'difference' and 'equilibrium' to get a more reasonable and reliable evaluation result. And it will really work on 'encourage the developed ones, and motivate the developing ones'.

2.1 Based on the weight determination method of "variance drive"

	which is short-cut process of scheme o i about the indicator	p	j .Then the index
	matrix about scheme set	O and index set	P can expressed as
	Ax ij  nm   	11 x  12 x x 1 21 22 2 m x x x m   	(3)
									12 x nn  x x nm
	Without loss of generality, assume the index of index set	P are all extra-large, carry
	out the dimensionless treatment with the data in	A as below:
	Setting the evaluation object set
				O		{ , , , } 12 o o o n	(1)
	Index set
				P		{ , , , p p	p	}
									12	m
	x		( )( 1, 2, , ; x o i n j 		1, 2,	m	)	(2)
	ij			j i

Table 1 . The region innovation capacity index of each province of 2011

 1

		The Utility	The Utility	The Utility	The Utility	The Utility
	Regions	Value Knowledge of	Value Knowledge of	Value Enterprise	of	Value Innovation	of	Value Innovation	of
		Creation	Acquisition	Innovation		Environment	Performance
	Jiangsu	40.94	50.59	62.85		50.85		50.58
	Guangdong	48.92	44.54	50.81		55.10		56.95
	Beijing	79.96	40.65	46.26		36.70		45.44
	Shanghai	47.60	63.82	44.93		37.37		44.68
	Zhejiang	31.89	30.91	56.53		41.22		36.87
	Shandong	31.42	25.12	44.09		39.72		39.54
	Tianjin	28.36	37.49	41.04		32.85		37.69
	Hubei	24.89	21.33	36.79		30.27		34.57
	Sichuang	26.00	26.18	29.17		34.41		31.12
	Chongqing	20.6	25.43	35.66		32.01		30.12
	Hunan	29.27	25.51	28.27		29.30		35.91
	Liaoning	23.59	38.66	26.20		30.36		27.26
	Anhui	19.65	15.88	33.29		30.54		36.35
	Shaanxi	30.25	18.62	26.74		33.41		27.10
	Henan	22.39	20.83	23.69		29.58		30.81
	Fujian	17.38	21.50	21.88		26.71		30.91
	Shanxi	20.61	15.69	30.85		21.80		26.12

Table 2 . The weight of each index

 2

		The Utility	The Utility	The Utility	The Utility	The Utility
		Value	of	Value	of	Value	of	Value	of	Value	of
		Knowledge	Knowledge	Enterprise		Innovation		Innovation
		Creation		Acquisition	Innovation		Environment	Performance
	weight	0.196032		0.181183		0.211934		0.130354		0.125122

Table 3 . The relative equilibrium coefficient,evaluating value of assembled and sorting result of provinces, autonomous regions and municipalities

 3

			Calculations
	Regions	Relative Equilibrium Coefficient	Evaluation Value of Literature [22]	Evaluation Value of This Paper	Sorting Result Literature [22] of	Sorting Result of This Paper
	Jiangsu	1.336999	52.27	51.76501	1	1
	Guangdong	2.105035	51.89	51.58099	2	2
	Beijing	0.585137	47.92	50.9122	3	3
	Shanghai	0.989686	46.23	48.38886	4	4
	Zhejiang	0.772414	41.23	39.83768	5	5
	Shandong	0.961705	37.34	35.98531	6	7
	Tianjin	1.460951	35.89	35.69753	7	6
	Hubei	0.928789	30.61	29.71281	8

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