【摘要】：面向服務(wù)的計(jì)算(Service Oriented Computing,簡(jiǎn)稱SOC)是當(dāng)前軟件領(lǐng)域備受關(guān)注的熱門(mén)主題之一。SOC倡導(dǎo)以標(biāo)準(zhǔn)的方式支持系統(tǒng)的開(kāi)放性,它所提供的服務(wù)協(xié)同和管理改善了軟件產(chǎn)品復(fù)雜的業(yè)務(wù)系統(tǒng),提高了軟件系統(tǒng)的生產(chǎn)效率。面向服務(wù)的架構(gòu)(Service Oriented Architecture, SOA)使得分布式應(yīng)用具有更好的靈活性和復(fù)用能力。SOA的主流實(shí)現(xiàn)方式是Web服務(wù)技術(shù),隨著面向服務(wù)架構(gòu)技術(shù)的大量應(yīng)用,當(dāng)前Web服務(wù)的數(shù)量正在以超線性的速度增長(zhǎng)。面對(duì)快速膨脹的Web服務(wù)資源,用戶如何才能方便、準(zhǔn)確的從大量的服務(wù)資源中找到自己需要的服務(wù)成為了當(dāng)前工業(yè)界和學(xué)術(shù)界的一大挑戰(zhàn)。 Web服務(wù)推薦和發(fā)現(xiàn)技術(shù)的出現(xiàn)為服務(wù)發(fā)現(xiàn)和查找難問(wèn)題的解決提供了一個(gè)方向。 現(xiàn)有的熱門(mén)推薦技術(shù)在電子商務(wù)行業(yè)的商品推薦中應(yīng)用較為廣泛,但是由于Web服務(wù)的異構(gòu)性和用戶需求的多樣性,傳統(tǒng)的推薦技術(shù)簡(jiǎn)單地應(yīng)用到Web服務(wù)推薦中往往推薦的準(zhǔn)確度較低。因此,如何把傳統(tǒng)推薦技術(shù)運(yùn)用在Web服務(wù)推薦中,并提高推薦的準(zhǔn)確度是當(dāng)前相關(guān)研究的難點(diǎn)之一�，F(xiàn)有技術(shù)大多從Web服務(wù)本身所蘊(yùn)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(wù)。這種方法大多忽略了服務(wù)之間的內(nèi)在關(guān)聯(lián)和兼容性,推薦過(guò)程沒(méi)有結(jié)合用戶自己的Web服務(wù)進(jìn)行考慮,使得推薦的Web服務(wù)不能保證與用戶自己的Web服務(wù)組合使用,造成服務(wù)資源的浪費(fèi),未能很好地滿足SOC軟件重用的初衷。 服務(wù)的推薦技術(shù)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(wù)發(fā)現(xiàn)方法則為用戶找到特定功能的服務(wù)提供了便捷。目前由于Web服務(wù)的描述文檔缺乏語(yǔ)義信息,服務(wù)的發(fā)現(xiàn)在準(zhǔn)確度和完備性上一直存在不足�；诒倔w論的Web服務(wù)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(wù)之間存在著相似度上的關(guān)聯(lián),本文從Web服務(wù)聚類問(wèn)題和文本向量空間模型的構(gòu)建入手,對(duì)目前Web服務(wù)聚類效果差的問(wèn)題進(jìn)行研究,指出現(xiàn)有聚類方法和Web服務(wù)相似度計(jì)算方面的不足,并針對(duì)性地提出新的解決方法。本文的主要研究?jī)?nèi)容和創(chuàng)新點(diǎn)如下： (1)針對(duì)服務(wù)推薦存在的問(wèn)題,提出了基于關(guān)聯(lián)規(guī)則挖掘的Web服務(wù)推薦方法(Services Recommending Method Based on Association Rule Mining,簡(jiǎn)稱RecARM)。RecARM利用Web服務(wù)組合的歷史記錄構(gòu)建Web服務(wù)間的關(guān)聯(lián)規(guī)則,挖掘服務(wù)之間潛在的關(guān)聯(lián)關(guān)系,利用用戶自有的Web服務(wù)為用戶進(jìn)行推薦,幫助完善和優(yōu)化用戶的服務(wù)組合。實(shí)驗(yàn)結(jié)果表明,改進(jìn)后的服務(wù)推薦結(jié)果相較于常規(guī)的推薦方法在穩(wěn)定性和準(zhǔn)確度上均有提高。該方法有效利用了Web服務(wù)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(duì)服務(wù)的發(fā)現(xiàn)引入新的聚類算法,根據(jù)Web服務(wù)間的相似性關(guān)聯(lián)提出了一種基于聚類的Web服務(wù)發(fā)現(xiàn)方法。本文引入改進(jìn)的ISODATA聚類算法,該方法有效解決了Web服務(wù)發(fā)現(xiàn)過(guò)程中聚類數(shù)量無(wú)法確定的問(wèn)題,并降低了異常數(shù)據(jù)對(duì)推薦結(jié)果產(chǎn)生的干擾和影響。 文本所提出的方法為服務(wù)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(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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