本文選題：語義物聯(lián)網(wǎng) + RDF流�。� 參考：《大連海事大學(xué)》2017年碩士論文

【摘要】：語義物聯(lián)網(wǎng)作為較新的研究領(lǐng)域是對(duì)物聯(lián)網(wǎng)的一個(gè)擴(kuò)展,其特點(diǎn)是在使用語義技術(shù)消除數(shù)據(jù)異構(gòu)的基礎(chǔ)上,能結(jié)合豐富的知識(shí)進(jìn)行語義查詢、推理。傳感器網(wǎng)絡(luò)作為語義物聯(lián)網(wǎng)的基礎(chǔ)設(shè)施,實(shí)時(shí)、持續(xù)地產(chǎn)生高度動(dòng)態(tài)的數(shù)據(jù),為了能良好地表達(dá)和處理這種動(dòng)態(tài)數(shù)據(jù),RDF流數(shù)據(jù)以及相應(yīng)的RDF流處理技術(shù)被提出。目前的RDF流處理大多是在擴(kuò)展了 SPARQL查詢的基礎(chǔ)上對(duì)RDF流數(shù)據(jù)進(jìn)行持續(xù)地查詢處理,但如何在RDF流數(shù)據(jù)查詢的基礎(chǔ)上分析、識(shí)別其中蘊(yùn)含的時(shí)間、因果關(guān)系,即以事件驅(qū)動(dòng)的角度進(jìn)行更高層次地處理還較少涉及,本文主要針對(duì)這一問題進(jìn)行研究。本文闡述了已有的RDF流處理技術(shù)和基于RDF流處理技術(shù)的語義復(fù)雜事件處理方法,在此基礎(chǔ)上,首先提出了基于RDF流的復(fù)雜事件處理框架RCEP。然后對(duì)RCEP中使用到的事件本體進(jìn)行建模,在建模過程中考慮了事件模式的特點(diǎn),并結(jié)合使用了現(xiàn)有的SSN本體,使得構(gòu)建的事件本體具有良好地可表達(dá)性;在用戶自定義事件過程中,根據(jù)用戶的設(shè)置和傳感器本體中的元數(shù)據(jù)選擇合適的傳感器;在RDF流處理上,選擇Sparkwave作為RDF流處理工具,針對(duì)其RETE網(wǎng)絡(luò)結(jié)構(gòu)不夠優(yōu)化這一問題進(jìn)行改進(jìn),即在RETE網(wǎng)絡(luò)構(gòu)建過程中根據(jù)事件模式的子圖的過濾能力這一因素決定其在RETE網(wǎng)絡(luò)中的連接順序,以此達(dá)到減少連接比較次數(shù)和內(nèi)存占用的目的。在Sparkwave的RDF流推理過程中,為減少推理所用時(shí)間,根據(jù)事件模式選擇性地從本體中加載背景知識(shí)。最后對(duì)RCEP的各個(gè)模塊依據(jù)文中提出的方法進(jìn)行設(shè)計(jì)與實(shí)現(xiàn)。為了驗(yàn)證本文中提出的方法對(duì)Sparkwave的改進(jìn)效果,對(duì)比分析了改進(jìn)后的Sparkwave和原Sparkwave在吞吐量和內(nèi)存占用上的情況。實(shí)驗(yàn)證明,使用本文改進(jìn)的Sparkwave能夠有效地適用于復(fù)雜事件的檢測(cè)。
[Abstract]:The semantic Internet of things, as a new research field, is an extension of the Internet of things. It is characterized by semantic query and reasoning based on the use of semantic technology to eliminate the heterogeneity of data. Sensor networks, as the infrastructure of semantic Internet of things, generate highly dynamic data in real time and continuously. In order to express and process the dynamic data well, RDF-stream data and the corresponding RDF stream processing technology are proposed. Most of the current RDF stream processing is based on extending the SPARQL query, but how to analyze the RDF stream data query and identify the time and causality. That is to say, a higher level of event-driven processing is less involved, this paper mainly focuses on this issue. In this paper, the existing RDF flow processing technology and the semantic complex event processing method based on RDF flow processing technology are described. Based on this, a complex event processing framework based on RDF flow is proposed. Then, the event ontology used in RCEP is modeled, the characteristics of event pattern are considered in the modeling process, and the existing SSN ontology is combined to make the event ontology well expressible. In the process of user-defined event, the appropriate sensor is selected according to the user's setting and the metadata in the sensor body, and the Sparkwave is chosen as the RDF flow processing tool in the RDF stream processing. In order to solve the problem that the RETE network structure is not optimized enough, the connection order in the RETE network is determined by the filtering ability of the sub-graph of the event pattern in the process of constructing the RETE network. In order to reduce the number of connections and memory consumption. In the process of RDF flow reasoning in Sparkwave, background knowledge is selectively loaded from ontology according to event pattern to reduce the time required for reasoning. Finally, each module of RCEP is designed and implemented according to the method proposed in this paper. In order to verify the improved effect of the proposed method on Sparkwave, the throughput and memory usage of the improved Sparkwave and the original Sparkwave are compared and analyzed. Experiments show that the improved Sparkwave can be used to detect complex events effectively.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.44;TN929.5

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本文編號(hào)：1830587