CN111400611B - Service discovery method based on Web complex relation network - Google Patents
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Abstract
The invention discloses a service discovery method based on a Web complex relation network, which comprises the following steps: step one: calculating parameter correlation between service sets; step two: calculating historical correlations between service sets; step three: calculating connection preference among service sets; step four: constructing a complex relation network; step five: service discovery is based on a complex relational network. By the service discovery method based on the Web complex relation network, the isolated Web services are connected to form the relation network of one Web service, so that a user can discover a plurality of services along a link from one service when the service discovery is performed, and the service discovery quality, efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of Web network services, in particular to a service discovery method based on a Web complex relation network.
Background
The complex relationship network takes Web service as a node in the social network, and the relationship between users is not discussed separately, but the relationship between nodes is also considered. For the Web service node, besides the calling and called relations between users, the relation between Web services is divided into three types of substitution, cooperation and competition through the functional parameters of the Web services: the substitution relation refers to that the functional attributes of two Web services are the same, and the function of the relation is that when a compound service consisting of a plurality of Web services is called, a certain service is problematic and can not be called, and the Web service in substitution relation with the service can take over the service and continue the execution of the service; the competition relationship is the same as the substitution relationship, and the competition relationship is also aimed at Web services with the same functional parameters, and only one Web service can be called in the plurality of Web services in the competition relationship; a collaboration relationship is a relationship in which Web services cooperate with each other in a composite service, in which functional parameters and quality of service parameters of the Web services are often affected by the Web service with which they are in a collaboration relationship.
Because of the isolation of services and the lack of relationships between services, it is desirable to have a service discovery method based on a Web complex relationship network that can solve the problems existing in the prior art.
Disclosure of Invention
The relational network of Web services is a network capable of reflecting the interaction between services, and its structure is a directed graph g= < V, E >, in which: v represents a set of points, each point representing a social service;
e represents a set of directed edges, one for each link.
The relationship between services is described by the following three attributes: parameter relevance, history relevance, connection preference.
The following symbols are defined:
s represents a known service;
C n is a set to be connected on Web, n is the number of services;
e and e' are data elements, each element being part of a data entity;
en and En' are data entities representing inputs and outputs of a service, each data entity is a set of data elements, e.g., en= { e1, e2, e3, … }.
The invention discloses a service discovery method based on a Web complex relation network, which comprises the following steps:
step one: calculating parameter correlation between service sets;
step two: calculating historical correlations between service sets;
step three: calculating connection preference among service sets;
step four: constructing a complex relation network;
step five: service discovery is based on a complex relational network.
Preferably, the step one parameter correlation Q cbt (S，C n ) To give a correlation of the functional relationship between the known service S and the target service C, the parameter correlation Q cbt (S，C n ) Is defined as formula (1):
the above calculates the known service S and target service C inputsOutputting the proportion of the overlapping attributes to ensure the correct link between the known service S and the target service C, wherein the parameter correlation Q cbt (S，C n ) The higher the value, the higher the quality of the link. The direction of the link is directed to the target service by the known service because the next service is invoked by the output attribute of the service matching the input attribute of the service.
Preferably, the historical dependencies are in most cases not invoked alone, but with other services to accomplish some complex tasks. By exploiting the previous interactions of the services, it is possible to know which services were invoked with the services in the past, and thus infer which services are likely to be invoked with the services in the future. Defining association rules between services as:
wherein W is 1 ，/>And->
The Web service is treated as a term, and the composite service is treated as a transaction. Define i= { I1, I2, …, in } as the item set of the service, t= { T1, T2, …, tn } as the item set of the transaction.
History correlation Q in the second step hd Indicating the possibility that when a known service S appears in a transaction, the service C to be connected is also in the transaction, i.e. equation (2):
Q hd (S, C) denotes the possibility that if the composite service contains S, then the composite service contains C at the same time, Q hd Higher (S, C) values indicate a higher likelihood.
Preferably, connection preference is an important attribute in a relational network,is the distance between two services, which is defined as the minimum number of edges that must pass from one service to the other. In order to reduce the average path length of the relational network, the constructed relational network is made to conform to the characteristics of the small world network. Services should be more likely to connect to those already having many links, i.e., to higher degrees. Many networks in reality exhibit connection preference: for a new node in the network, the probability of connecting to those nodes with higher degrees is greater. Connection preference Q in the third step cp Representing the likelihood of a service S being connected to a service C, the connection preference being dependent on K i ，K i For the number of links serving i, defined as equation (3):
the connection preference Q cp (S, C) means that the possibility of connecting to a certain service depends on the degree of the service node, Q cp The higher the value of (S, C), the higher the likelihood that a new node will be connected to that node.
Preferably, by going to the three relationships given above, they are combined as a criterion for judging the link quality. Link quality is defined as Q (S, C) as shown in equation (6):
Q(S,C n )＝Q cbt (S,C n )+Q hd (S,C n )+Q cp (S,C n ) (6)
the above equation shows that the quality of the link between S and Cn depends on three properties, the higher the value the higher the quality of the link. First, based on Q cbt (S,C n ) Selecting a link whose function matches the best service, the factor being dominant; then select Q hd (S,C n ) The linking of higher value services ensures the quality of the link by learning which services were invoked in the past and which services were determined to be likely invoked in the future. Finally, select Q cp (S,C n ) Links to higher value services, services with more links to each otherThere is a higher likelihood of attracting other services to connect to it.
The services are connected through the links to form a service relationship network, and then whether the service relationship network accords with the characteristics of the complex network is verified.
In order to build a relational network of Web services to improve the efficiency of service discovery, four characteristics of the relational network need to be considered. First of all, the relationship network is open, consisting of an increasing number of new services, and therefore the number of points N should remain incremental. Next is connection preference, in a relational network of services, the likelihood that two points are connected together is not random, but instead follows connection preference: one point is more likely to attract other points to connect to it in the case of many links per se. Then competitive, each point has the ability to compete with the other points for new points and their connections. Finally, variability, links in the relationship network reflect the actual social situation of the service based on previous interactions of the service. Some old links may be replaced by new links because the latter have a higher value of link quality.
The fourth step is to construct a complex relation network, aiming at the growth of the network, the number of the set points starts from a number m0, and each step is added with one point and m edges, wherein m is less than m0, so that a new point is connected to an original node; for the connection preference of the relational network, assume that the probability that a new point is connected to a point i is shown in formula (3), and depends on the number of links to which the point is connected, namely, the degree of the point; setting an fitness parameter for the competitiveness of the network, each time a new service is added to the relational network, the service having a fitness parameter whose value depends on the parameter correlation and the history correlation; for variability, updating links with low link quality and adding new links with high link quality, the value of link quality is shown in formula (4):
η i the calculation formula of (2) is formula(5) The following is shown:
w in the formula cbt +W hd ＝1，η i Higher values indicate more competitive points i.
Preferably, the fifth step includes the following operations:
step 5.1, inputting a target service W and a connection service set thereof;
step 5.2, traversing the connection service set, outputting a calling service set of W if the traversing is finished, and then finishing; if the traversal is not finished, executing the step 5.3;
step 5.3, judging whether the input parameter set of the W contains the output parameter of the current service, removing the contained parameter from the input parameter set of the W when the input parameter set of the W contains the output parameter of the current service, and executing the step 5.4 when the input parameter set of the W does not contain the output parameter of the current service;
step 5.4, judging whether the element is removed from the input parameter set of the W, and if the element is not removed from the input parameter set of the W, returning to the step 5.2; if the elements are removed from the input parameter set of W, adding the current service into a calling service set, and executing the step 5.5;
step 5.5, judging whether the input parameter set of W is empty, and if the input parameter set of W is not empty, executing step 5.2; if the input parameter set of W is empty, saving the call service set and restoring the input parameter set of W, and then executing step 5.2.
The invention provides a service discovery method based on a Web complex relation network, which connects isolated Web services to form a relation network of Web services, thereby allowing a user to discover a plurality of services along a link from one service when the user discovers the services. The service discovery method based on the Web complex relation network can improve the service discovery quality, efficiency and accuracy.
Drawings
FIG. 1 is a flow chart for relational network model analysis and construction.
FIG. 2 is a flow chart for service invocation based on a complex relationship network.
Fig. 3 is a schematic diagram of service discovery.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention become more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: as shown in fig. 1, a service discovery method based on a Web complex relationship network includes the following steps:
step one: calculating parameter correlation between service sets;
step two: calculating historical correlations between service sets;
step three: calculating connection preference among service sets;
step four: constructing a complex relation network;
step five: service discovery is based on a complex relational network.
The parameter correlation Q in the step one cbt (S，C n ) To give a correlation of the functional relationship between the known service S and the target service C, the parameter correlation Q cbt (S，C n ) Is defined as formula (1):
the above-mentioned method calculates the ratio of the input-output attribute coincidence of the known service S and the target service C, ensures the correct link between the known service S and the target service C, and the parameter correlation Q cbt (S，C n ) The higher the value, the higher the quality of the link. The direction of the link is directed to the target service by the known service because the next service is invoked by the output attribute of the service matching the input attribute of the service.
Historical dependencies are in most cases not invoked alone, but with other services to accomplish some complex tasks. By exploiting the previous interactions of the services, it is possible to know which services were invoked with the services in the past, and thus infer which services are likely to be invoked with the services in the future. Defining association rules between services as:
wherein W is 1 ，/>And->
The Web service is treated as a term, and the composite service is treated as a transaction. Define i= { I1, I2, …, in } as the item set of the service, t= { T1, T2, …, tn } as the item set of the transaction.
History correlation Q in the second step hd Indicating the possibility that when a known service S appears in a transaction, the service C to be connected is also in the transaction, i.e. equation (2):
Q hd (S, C) denotes the possibility that if the composite service contains S, then the composite service contains C at the same time, Q hd (S, C) valueHigher means higher likelihood.
Connection preference is an important attribute in a relational network, which is the distance between two services, defined as the minimum number of edges that must pass from one service to another. In order to reduce the average path length of the relational network, the constructed relational network is made to conform to the characteristics of the small world network. Services should be more likely to connect to those already having many links, i.e., to higher degrees. Many networks in reality exhibit connection preference: for a new node in the network, the probability of connecting to those nodes with higher degrees is greater. Connection preference Q in the third step cp Representing the likelihood of a service S being connected to a service C, the connection preference being dependent on K i ，K i For the number of links serving i, defined as equation (3):
the connection preference Qcp (S, C) indicates that the likelihood of connecting to a service depends on the degree of the service node, the higher the value of Qcp (S, C), the higher the likelihood that a new node will be connected to the node.
By going to the three relationships given above, they are combined as criteria for judging the link quality. Link quality is defined as Q (S, C) as shown in equation (6):
Q(S,C n )＝Q cbt (S,C n )+Q hd (S,C n )+Q cp (S,C n ) (6)
the above equation shows that the quality of the link between S and Cn depends on three properties, the higher the value the higher the quality of the link. First, based on Q cbt (S,C n ) Selecting a link whose function matches the best service, the factor being dominant; then select Q hd The linking of higher (S, cn) value services ensures the quality of the link by learning which services were invoked in the past along with which services to determine which services might be invoked in the future. Finally, select Q cp (S,C n ) Links to higher valued services, services with more links to each other will have a higher likelihood of attracting other services to it.
The services are connected through the links to form a service relationship network, and then whether the service relationship network accords with the characteristics of the complex network is verified.
In order to build a relational network of Web services to improve the efficiency of service discovery, four characteristics of the relational network need to be considered. First of all, the relationship network is open, consisting of an increasing number of new services, and therefore the number of points N should remain incremental. Next is connection preference, in a relational network of services, the likelihood that two points are connected together is not random, but instead follows connection preference: one point is more likely to attract other points to connect to it in the case of many links per se. Then competitive, each point has the ability to compete with the other points for new points and their connections. Finally, variability, links in the relationship network reflect the actual social situation of the service based on previous interactions of the service. Some old links may be replaced by new links because the latter have a higher value of link quality.
The fourth step is to construct a complex relation network, aiming at the growth of the network, the number of the set points starts from a number m0, and each step is added with one point and m edges, wherein m is less than m0, so that a new point is connected to an original node; for the connection preference of the relational network, assume that the probability that a new point is connected to a point i is shown in formula (3), and depends on the number of links to which the point is connected, namely, the degree of the point; setting an fitness parameter for the competitiveness of the network, each time a new service is added to the relational network, the service having a fitness parameter whose value depends on the parameter correlation and the history correlation; for variability, updating links with low link quality and adding new links with high link quality, the value of link quality is shown in formula (4):
η i the formula (5) is as follows:
w in the formula cbt +W hd ＝1，η i Higher values indicate more competitive points i.
Four parameters are used for constructing the network, and m0 represents the number of initial nodes; m is the number of links added in each step; and p: newly increasing the probability of the link; q: the likelihood of the link being reconnected. The algorithm starts from m0 points in the network, and each step performs one of the following three steps:
new links are added, knowing the probability p, m < = m0. The link start node is uniquely selected and the end of the link is selected by algorithm 1 through the link quality in equation 4. This process is repeated m times.
The m edges are reconnected, the probability q is known. In this case, the point i is randomly selected, one of its links, lij, of which link quality is the lowest is selected, and then this link is deleted. Another point z is selected by algorithm 1, adding a new edge Liz.
A new node is added with m links, known as probabilities 1-p-q, connecting this node to the other m nodes selected by algorithm 1.
When the addition of the required N nodes is completed, the algorithm is stopped.
As shown in fig. 2, the fifth step includes the following operations:
step 5.1, inputting a target service W and a connection service set thereof;
step 5.2, traversing the connection service set, outputting a calling service set of W if the traversing is finished, and then finishing; if the traversal is not finished, executing the step 5.3;
step 5.3, judging whether the input parameter set of the W contains the output parameter of the current service, removing the contained parameter from the input parameter set of the W when the input parameter set of the W contains the output parameter of the current service, and executing the step 5.4 when the input parameter set of the W does not contain the output parameter of the current service;
step 5.4, judging whether the element is removed from the input parameter set of the W, and if the element is not removed from the input parameter set of the W, returning to the step 5.2; if the elements are removed from the input parameter set of W, adding the current service into a calling service set, and executing the step 5.5;
step 5.5, judging whether the input parameter set of W is empty, and if the input parameter set of W is not empty, executing step 5.2; if the input parameter set of W is empty, saving the call service set and restoring the input parameter set of W, and then executing step 5.2.
Example 2: a service discovery method based on a Web complex relationship network, the service discovery method comprising the steps of:
the first step: computing parameter correlations between service sets
Parameter correlation: let c.en= { e1, e2, e3, … } and
s.en '= { e1', e2', e3', … }, then Q cbt Specifically, it is:
taking query for disease names as a known service as an example, the following service is first assumed:
service w1: input (disease name) output (Address type, hospital information)
Service w2: input (disease information, hospital information) output (postal address, hospital address)
Service w3: input (address type, hospital information) output (mailbox address, hospital address)
Where w1 is a known service and w2, w3 is a target service, then the parameter correlation between them is calculated as follows:
and a second step of: computing historical correlations between service sets
The Web service is treated as a term, and the service set is treated as a transaction. Assume that the transaction distribution to which the three services described above belong is as follows:
TABLE 1 transaction set for Web services
The historical correlation between them is calculated as follows:
the above historical correlation indicates that there is a 40% probability of containing service w2 and a 60% probability of containing service w3 in the set containing service w 1.
And a third step of: computing connection preference between service sets
According to a calculation formula of connection preference, after calculating the sum of degrees of all points of a complex network formed by service sets, taking the proportion of the degrees of each service as the connection preference, taking a complex network constructed by a service set C represented by services w1, w2 and w3 as an example, in the experiment, the connection preference of w1, w2 and w3 is calculated as follows:
fourth step: construction of complex relationship networks
And calculating the link quality of the relation network to be constructed according to the parameter correlation, the historical correlation and the connection preference obtained by calculation of each service. Taking the link quality calculations for services w1, w2, w3 as an example:
Q(w1,w2)＝<Q cbt (w1,w2),Q hd (w1,w2),Q cp (w1,w2)>
Q(w1,w3)＝<Q cbt (w1,w3),Q hd (w1,w3),Q cp (w1,w3)>
Q(w2,w3)＝<Q cbt (w2,w3),Q hd (w2,w3),Q cp (w2,w3)>
first, based on Q cbt (S,C n ) The selection function matches the link of the best service. Then, select Q hd (S,C n ) The link of the higher value service ensures the quality of the link by retrieving information of which services the service was invoked with in the past to determine which services may be invoked with in the future. Finally, select Q cp (S,C n ) Links to higher valued services, services with more links to each other will have a higher likelihood of attracting other services to it.
Fifth step: service discovery based on complex relational network
In order for the services in the target service set to be invoked, a calling service set needs to be regenerated on the basis of the linked service set. The call service set is generated to classify the services in the link service set, and the services meeting the user requirements are stored in different sets. This ensures that Web services in the generated set of target services can be invoked. If no new service set is generated when the calling service set is generated, the calling service set is completely generated.
A patient who is ill wants to go to a hospital to see his doctor, who needs a "diagnostic service" to give a diagnosis based on his symptoms. Then, it is necessary to "acquire a hospital information service" to obtain information of a hospital capable of treating a disease of him, and it is necessary to "acquire a location information service" to obtain more address information. Thereafter, a "acquire restaurant information service" is required to acquire information of the restaurant. Finally, based on the obtained address, a "navigation service" is required to obtain the roadmap. As shown in fig. 3, these services can be found quickly in a relational network following links, only taking 174ms.
TABLE 2 input and output of Web services
Finally, it should be pointed out that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting. Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (1)
1. A service discovery method based on a Web complex relationship network, the service discovery method comprising the steps of:
step one: calculating parameter correlation between service sets;
the parameter correlation Q in the step one cbt (C, S) is a correlation giving a functional relationship between the known service S and the target service C, the parameter correlation Q cbt (C, S) is defined as formula (1):
the above-mentioned method calculates the ratio of the input-output attribute coincidence of the known service S and the target service C, ensures the correct link between the known service S and the target service C, and the parameter correlation Q cbt The higher the (C, S) value, the higher the quality of the link; en and En' are data entities representing inputs and outputs of a service, each data entity being a collection of data elements;
step two: calculating historical correlations between service sets;
history correlation Q in the second step hd Indicating the possibility that when a known service S appears in a transaction, the service C to be connected is also in the transaction, i.e. equation (2):
Q hd (S, C) denotes the possibility that if the composite service contains S, then the composite service contains C at the same time, Q hd Higher (S, C) values indicate a higher likelihood;
step three: calculating connection preference among service sets;
connection preference Q in the third step cp Representing the likelihood of a service S being connected to a service C, the connection preference being dependent on K i ，K i For the number of links serving i, defined as equation (3):
the connection preference Q cp (S, C) means that the possibility of connecting to a certain service depends on the degree of the service, Q cp The higher the value of (S, C), the higher the likelihood that a new node will be connected to that node;
step four: constructing a complex relation network;
the fourth step is to construct a complex relation network, aiming at the growth of the network, the number of the set points starts from a number m0, and each step is added with one point and m edges, wherein m is less than m0, so that a new point is connected to an original node; for the connection preference of the relational network, assume that the probability that a new point is connected to a point i is shown in formula (3), and depends on the number of links to which the point is connected, namely, the degree of the point; setting an fitness parameter for the competitiveness of the network, each time a new service is added to the relational network, the service having a fitness parameter whose value depends on the parameter correlation and the history correlation; for variability, updating links with low link quality and adding new links with high link quality, the value of link quality is shown in formula (4):
C n is a set to be connected on Web, n is the number of services;
η i the formula (5) is as follows:
w in the formula cbt +W hd ＝1，η i Higher values indicate more competitive points i;
step five: service discovery is carried out based on a complex relation network;
the fifth step comprises the following operations:
step 5.1, inputting a target service W and a connection service set thereof;
step 5.2, traversing the connection service set, outputting a calling service set of W if the traversing is finished, and then finishing; if the traversal is not finished, executing the step 5.3;
step 5.3, judging whether the input parameter set of the W contains the output parameter of the current service, removing the contained parameter from the input parameter set of the W when the input parameter set of the W contains the output parameter of the current service, and executing the step 5.4 when the input parameter set of the W does not contain the output parameter of the current service;
step 5.4, judging whether the element is removed from the input parameter set of the W, and if the element is not removed from the input parameter set of the W, returning to the step 5.2; if the elements are removed from the input parameter set of W, adding the current service into a calling service set, and executing the step 5.5;
step 5.5, judging whether the input parameter set of W is empty, and if the input parameter set of W is not empty, executing step 5.2; if the input parameter set of W is empty, saving the call service set and restoring the input parameter set of W, and then executing step 5.2.
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