本文選題：普適計(jì)算 + 服務(wù)發(fā)現(xiàn)　； 參考：《安徽工業(yè)大學(xué)》2012年碩士論文

【摘要】：有線網(wǎng)絡(luò)環(huán)境下的Web服務(wù)發(fā)現(xiàn)技術(shù)研究已經(jīng)比較成熟，如基于語(yǔ)義的服務(wù)匹配和分布式的服務(wù)發(fā)現(xiàn)架構(gòu)，但面臨人們是否可以快速獲取需要的信息和服務(wù)，這就需要進(jìn)一步的研究。在普適計(jì)算環(huán)境中，服務(wù)的使用者和提供者表現(xiàn)出移動(dòng)性、間歇連接性，其拓?fù)浣Y(jié)構(gòu)隨時(shí)間變化而改變，呈現(xiàn)出極強(qiáng)的動(dòng)態(tài)性。而且，移動(dòng)設(shè)備的存儲(chǔ)容量和通信帶寬都受限，這就要求服務(wù)發(fā)現(xiàn)所產(chǎn)生的消息負(fù)載不能過(guò)高。在動(dòng)態(tài)的網(wǎng)絡(luò)環(huán)境下，發(fā)現(xiàn)合適的服務(wù)是實(shí)現(xiàn)服務(wù)共享、復(fù)用的重要前提，服務(wù)發(fā)現(xiàn)的效果直接關(guān)系服務(wù)復(fù)用的質(zhì)量，影響到服務(wù)組合的相容性和替換性，關(guān)系到能否實(shí)現(xiàn)快速的使用個(gè)性化服務(wù)。在服務(wù)發(fā)現(xiàn)過(guò)程中，有效地服務(wù)發(fā)現(xiàn)依賴于服務(wù)的描述方法；而普適環(huán)境又涉及到服務(wù)社會(huì)關(guān)系，那么本文需要建立社會(huì)關(guān)系模型，在服務(wù)描述中增加服務(wù)社會(huì)屬性，并且要對(duì)服務(wù)的描述信息進(jìn)行壓縮優(yōu)化。 首先，本文在OWL-S本體描述語(yǔ)言基礎(chǔ)上，擴(kuò)展為一種輕量級(jí)的Web服務(wù)描述語(yǔ)言S-OWL-S。先分析了普適環(huán)境下存在的社會(huì)關(guān)系，建立支持社會(huì)上下文表達(dá)語(yǔ)義Web服務(wù)描述模型�？紤]OWL-S沒(méi)有服務(wù)社會(huì)上下文描述，但它具有可擴(kuò)展性，所以設(shè)計(jì)了S-OWL-S本體服務(wù)描述語(yǔ)言，，構(gòu)建的SCProfile本體囊括了社會(huì)上下文屬性以及屬性參數(shù)。 其次，本文提出了一種改進(jìn)的Counting Bloom Filter算法，即分域Counting BloomFilter算法，該算法按照服務(wù)的領(lǐng)域劃分，并用于服務(wù)信息的壓縮。壓縮的目的是簡(jiǎn)潔地表示出服務(wù)集合信息，在向用戶傳播服務(wù)廣告時(shí)減少帶寬消耗和緩存的占用，此外可以減小誤判率。文中分析了標(biāo)準(zhǔn)的Bloom Filter的基本原理、算法以及誤判率，同時(shí)分析計(jì)數(shù)式Bloom Filter算法，在此基礎(chǔ)上提出了分域計(jì)數(shù)式Bloom Filter算法，并比較了分域Counting Bloom Filter算法和Bloom Filter算法的誤判率以及設(shè)計(jì)相應(yīng)的哈希函數(shù)。 最后，本文對(duì)服務(wù)屬性進(jìn)行量化，并把發(fā)布的服務(wù)信息存儲(chǔ)到位串向量組以及進(jìn)行服務(wù)的查找。由于Bloom Filter算法只能用于表示數(shù)據(jù)集合，本文把服務(wù)信息分為領(lǐng)域和其他服務(wù)屬性兩部分，并按照量化的領(lǐng)域不同，將量化的服務(wù)信息存儲(chǔ)到不同位串向量。通過(guò)常用的查準(zhǔn)率和查全率性能指標(biāo)以及平均查找時(shí)間來(lái)檢測(cè)分域Counting BloomFilter算法的有效性。
[Abstract]:Web service discovery technology in wired network environment has been more mature, such as semantic service matching and distributed service discovery architecture, but whether people can quickly obtain the required information and services. This requires further study. In the pervasive computing environment, the service providers and consumers exhibit mobility and intermittent connectivity, and their topology changes with time, showing a strong dynamic nature. Moreover, the storage capacity and communication bandwidth of mobile devices are limited, which requires that the message load generated by service discovery should not be too high. In dynamic network environment, finding suitable services is an important prerequisite for service sharing and reuse. The effect of service discovery is directly related to the quality of service reuse, and affects the compatibility and substitution of service composition. It is related to whether to achieve the rapid use of personalized services. In the process of service discovery, the effective service discovery depends on the service description method, and the universal environment involves the service social relationship, so this paper needs to establish the social relationship model, and add the service social attribute to the service description. And the service description information should be compressed and optimized. Firstly, based on OWL-S ontology description language, this paper extends to a lightweight Web services description language S-OWL-S. In this paper, we first analyze the existing social relations in the universal environment, and establish a Web service description model that supports the semantic representation of social context. Considering that OWL-S has no service social context description, but it is extensible, S-OWL-S ontology service description language is designed, and the constructed SCProfile ontology includes social context attributes and attribute parameters. Secondly, this paper proposes an improved Counting Bloom filter algorithm, which is called Domain Counting Bloom filter algorithm, which is divided according to the domain of services and used to compress service information. The purpose of compression is to concisely represent the service set information, reduce the bandwidth consumption and cache usage when propagating the service advertisement to the user, and reduce the misjudgment rate. In this paper, the basic principle, algorithm and error rate of standard Bloom filter are analyzed. At the same time, the counting Bloom filter algorithm is analyzed. The error rate of domain Counting Bloom filter algorithm and Bloom filter algorithm are compared and the corresponding hash functions are designed. Finally, the service attributes are quantified, and the published service information is stored in the string vector group and the service search is carried out. Because Bloom filter algorithm can only be used to represent data sets, this paper divides the service information into two parts: domain and other service attributes, and stores the quantized service information to different bit string vectors according to the different quantized domain. The validity of the domain Counting Bloom filter algorithm is detected by the commonly used recall ratio, recall performance index and average lookup time.
【學(xué)位授予單位】：安徽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP393.09

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