基于服務關聯(lián)的服務推薦和發(fā)現(xiàn)方法研究
發(fā)布時間：2018-09-19 14:50
【摘要】：面向服務的計算(Service Oriented Computing,簡稱SOC)是當前軟件領域備受關注的熱門主題之一。SOC倡導以標準的方式支持系統(tǒng)的開放性,它所提供的服務協(xié)同和管理改善了軟件產(chǎn)品復雜的業(yè)務系統(tǒng),提高了軟件系統(tǒng)的生產(chǎn)效率。面向服務的架構(Service Oriented Architecture, SOA)使得分布式應用具有更好的靈活性和復用能力。SOA的主流實現(xiàn)方式是Web服務技術,隨著面向服務架構技術的大量應用,當前Web服務的數(shù)量正在以超線性的速度增長。面對快速膨脹的Web服務資源,用戶如何才能方便、準確的從大量的服務資源中找到自己需要的服務成為了當前工業(yè)界和學術界的一大挑戰(zhàn)。 Web服務推薦和發(fā)現(xiàn)技術的出現(xiàn)為服務發(fā)現(xiàn)和查找難問題的解決提供了一個方向。 現(xiàn)有的熱門推薦技術在電子商務行業(yè)的商品推薦中應用較為廣泛,但是由于Web服務的異構性和用戶需求的多樣性,傳統(tǒng)的推薦技術簡單地應用到Web服務推薦中往往推薦的準確度較低。因此,如何把傳統(tǒng)推薦技術運用在Web服務推薦中,并提高推薦的準確度是當前相關研究的難點之一�，F(xiàn)有技術大多從Web服務本身所蘊含的描述信息及其用戶之間的相似度出發(fā)為用戶推薦服務。這種方法大多忽略了服務之間的內在關聯(lián)和兼容性,推薦過程沒有結合用戶自己的Web服務進行考慮,使得推薦的Web服務不能保證與用戶自己的Web服務組合使用,造成服務資源的浪費,未能很好地滿足SOC軟件重用的初衷。 服務的推薦技術可以在用戶的功能需求還不明確的情況下完成推薦,而服務發(fā)現(xiàn)方法則為用戶找到特定功能的服務提供了便捷。目前由于Web服務的描述文檔缺乏語義信息,服務的發(fā)現(xiàn)在準確度和完備性上一直存在不足�；诒倔w論的Web服務發(fā)現(xiàn)研究還不成熟,而現(xiàn)有基于規(guī)則、聚類方法和文本向量空間模型的研究取得了較好的效果�；赪eb服務之間存在著相似度上的關聯(lián),本文從Web服務聚類問題和文本向量空間模型的構建入手,對目前Web服務聚類效果差的問題進行研究,指出現(xiàn)有聚類方法和Web服務相似度計算方面的不足,并針對性地提出新的解決方法。本文的主要研究內容和創(chuàng)新點如下： (1)針對服務推薦存在的問題,提出了基于關聯(lián)規(guī)則挖掘的Web服務推薦方法(Services Recommending Method Based on Association Rule Mining,簡稱RecARM)。RecARM利用Web服務組合的歷史記錄構建Web服務間的關聯(lián)規(guī)則,挖掘服務之間潛在的關聯(lián)關系,利用用戶自有的Web服務為用戶進行推薦,幫助完善和優(yōu)化用戶的服務組合。實驗結果表明,改進后的服務推薦結果相較于常規(guī)的推薦方法在穩(wěn)定性和準確度上均有提高。該方法有效利用了Web服務所特有的歷史組合記錄的數(shù)據(jù),為推薦提供了依據(jù)。 (2)針對服務的發(fā)現(xiàn)引入新的聚類算法,根據(jù)Web服務間的相似性關聯(lián)提出了一種基于聚類的Web服務發(fā)現(xiàn)方法。本文引入改進的ISODATA聚類算法,該方法有效解決了Web服務發(fā)現(xiàn)過程中聚類數(shù)量無法確定的問題,并降低了異常數(shù)據(jù)對推薦結果產(chǎn)生的干擾和影響。 文本所提出的方法為服務的推薦和發(fā)現(xiàn)提供了新的方法和思路。
[Abstract]:Service Oriented Computing (SOC) is one of the hot topics in the software field. SOC advocates the openness of support systems in a standard way. The service coordination and management provided by SOC improves the complex business systems of software products and the productivity of software systems. Service Oriented Architecture (SOA) makes distributed applications more flexible and reusable. The mainstream implementation of SOA is Web services technology. With the extensive application of Service Oriented Architecture (SOA), the number of Web services is increasing at a superlinear rate. In order to conveniently and accurately find their own needs from a large number of service resources has become a major challenge in the current industry and academia.
The emergence of Web service recommendation and discovery technology provides a direction for solving the problem of service discovery and discovery.
Current popular recommendation technologies are widely used in the commodity recommendation of e-commerce industry. However, due to the heterogeneity of Web services and the diversity of users'needs, traditional recommendation technologies are often used to recommend Web services with low accuracy. Improving the accuracy of recommendation is one of the difficulties in current research. Most of the existing technologies are based on the description information contained in Web services and the similarity between users. Row considerations make the recommended Web services not guaranteed to be used in combination with the user's own Web services, resulting in a waste of service resources and failing to meet the original intention of SOC software reuse.
Service recommendation technology can accomplish recommendation when the user's functional requirements are not clear, while service discovery method provides convenience for users to find services with specific functions. The research of EB service discovery is not mature, but the existing research based on rules, clustering methods and text vector space model has achieved good results. Based on the similarity between web services, this paper starts with the clustering problem of Web services and the construction of text vector space model, and advances the problem of poor clustering effect of web services. The main contents and innovations of this paper are as follows:1.
(1) Aiming at the problems of service recommendation, a Web service recommendation method based on association rule mining (RecARM) is proposed. RecARM uses the history of Web service composition to construct association rules between Web services, mining the potential association between services, and utilizing users. The experimental results show that the improved service recommendation results are more stable and accurate than the conventional recommendation methods. This method effectively utilizes the data recorded by the unique historical composition of Web services and provides a basis for recommendation.
(2) Introduce a new clustering algorithm for service discovery, and propose a clustering-based Web service discovery method according to the similarity relationship between Web services. This paper introduces an improved ISODATA clustering algorithm, which effectively solves the problem that the number of clusters can not be determined in the process of Web service discovery, and reduces the recommendation result of abnormal data. Interference and impact.
The method proposed in this paper provides new methods and ideas for service recommendation and discovery.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
本文編號：2250448
[Abstract]:Service Oriented Computing (SOC) is one of the hot topics in the software field. SOC advocates the openness of support systems in a standard way. The service coordination and management provided by SOC improves the complex business systems of software products and the productivity of software systems. Service Oriented Architecture (SOA) makes distributed applications more flexible and reusable. The mainstream implementation of SOA is Web services technology. With the extensive application of Service Oriented Architecture (SOA), the number of Web services is increasing at a superlinear rate. In order to conveniently and accurately find their own needs from a large number of service resources has become a major challenge in the current industry and academia.
The emergence of Web service recommendation and discovery technology provides a direction for solving the problem of service discovery and discovery.
Current popular recommendation technologies are widely used in the commodity recommendation of e-commerce industry. However, due to the heterogeneity of Web services and the diversity of users'needs, traditional recommendation technologies are often used to recommend Web services with low accuracy. Improving the accuracy of recommendation is one of the difficulties in current research. Most of the existing technologies are based on the description information contained in Web services and the similarity between users. Row considerations make the recommended Web services not guaranteed to be used in combination with the user's own Web services, resulting in a waste of service resources and failing to meet the original intention of SOC software reuse.
Service recommendation technology can accomplish recommendation when the user's functional requirements are not clear, while service discovery method provides convenience for users to find services with specific functions. The research of EB service discovery is not mature, but the existing research based on rules, clustering methods and text vector space model has achieved good results. Based on the similarity between web services, this paper starts with the clustering problem of Web services and the construction of text vector space model, and advances the problem of poor clustering effect of web services. The main contents and innovations of this paper are as follows:1.
(1) Aiming at the problems of service recommendation, a Web service recommendation method based on association rule mining (RecARM) is proposed. RecARM uses the history of Web service composition to construct association rules between Web services, mining the potential association between services, and utilizing users. The experimental results show that the improved service recommendation results are more stable and accurate than the conventional recommendation methods. This method effectively utilizes the data recorded by the unique historical composition of Web services and provides a basis for recommendation.
(2) Introduce a new clustering algorithm for service discovery, and propose a clustering-based Web service discovery method according to the similarity relationship between Web services. This paper introduces an improved ISODATA clustering algorithm, which effectively solves the problem that the number of clusters can not be determined in the process of Web service discovery, and reduces the recommendation result of abnormal data. Interference and impact.
The method proposed in this paper provides new methods and ideas for service recommendation and discovery.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
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