本文選題：關(guān)系數(shù)據(jù)庫　切入點：圖數(shù)據(jù)庫　出處：《大連海事大學》2013年碩士論文

【摘要】：關(guān)系數(shù)據(jù)庫技術(shù)與信息檢索技術(shù)的融合,在應(yīng)用需求的推動下迅速發(fā)展。使用戶既不需要懂得復雜的結(jié)構(gòu)化查詢語言,又不需要懂得底層的數(shù)據(jù)庫模式,便可以像使用Web搜索引擎一樣對數(shù)據(jù)庫中的數(shù)據(jù)進行查詢。對于關(guān)系數(shù)據(jù)庫信息檢索的策略,國內(nèi)外專家學者提出了許多不同的觀點。其中,既有元組級別的又有對象級別的。關(guān)系數(shù)據(jù)庫中數(shù)據(jù)量的與日俱增,使得數(shù)據(jù)圖的規(guī)模越來越大,信息檢索的效率也越來越低。關(guān)系數(shù)據(jù)庫信息檢索領(lǐng)域面臨大數(shù)據(jù)的挑戰(zhàn),己成為一個不可回避的事實。 隨著圖數(shù)據(jù)庫技術(shù)的不斷成熟,其應(yīng)用領(lǐng)域正在不斷地擴大。與生俱來的靈活的圖模型不但滿足了社交類網(wǎng)站的應(yīng)用需求,而且對圖算法的適應(yīng)能力也非常強。本文研究了圖數(shù)據(jù)庫技術(shù)和全文索引技術(shù),分析了對象級別信息檢索及其圖數(shù)據(jù)庫檢索的研究現(xiàn)狀,提出了一種由關(guān)系數(shù)據(jù)向圖數(shù)據(jù)轉(zhuǎn)換的數(shù)據(jù)抽取方式,并對現(xiàn)有的對象級別建模方式進行了改進,設(shè)計了一個嵌入圖數(shù)據(jù)庫的對象級別信息檢索算法,相比元組級別的信息檢索方式,對象級別的檢索方式具有數(shù)據(jù)圖規(guī)模小、結(jié)果完整性高和無重復結(jié)果等優(yōu)點。該算法在考慮檢索關(guān)鍵詞的重要性的基礎(chǔ)上,采用啟發(fā)式的方式進行了規(guī)則查詢,結(jié)合了圖數(shù)據(jù)庫與關(guān)系數(shù)據(jù)庫,為海量數(shù)據(jù)條件下進行關(guān)系數(shù)據(jù)庫信息檢索提供了一種有效的解決方案,并拓展了圖數(shù)據(jù)庫的應(yīng)用領(lǐng)域。 為驗證算法的有效性和原型系統(tǒng)的可用性,本文利用DBLP數(shù)據(jù)集對該算法的查詢效果和查詢效率進行了實驗驗證。論文采用P@k對檢索效果進行了驗證,并對檢索效率進行了對比和分析。最終的實驗結(jié)果表明,圖數(shù)據(jù)庫對象級別關(guān)鍵詞檢索算法具有良好的檢索效果和較高的應(yīng)用前景。
[Abstract]:With the combination of relational database technology and information retrieval technology, it develops rapidly under the impetus of application requirements, so that users do not need to understand the complex structured query language and the underlying database schema. We can query the data in the database just like using the Web search engine. For the strategy of information retrieval in relational database, experts and scholars at home and abroad have put forward many different viewpoints. With the increasing of data volume in relational database, the scale of data graph is becoming larger and larger, and the efficiency of information retrieval is becoming lower and lower. The field of relational database information retrieval is facing the challenge of big data. Has become an unavoidable fact. With the development of graph database technology, its application field is expanding continuously. The inherent flexible graph model not only meets the application needs of social networking sites, Moreover, the adaptability of graph algorithm is very strong. This paper studies graph database technology and full-text index technology, and analyzes the research status of object level information retrieval and graph database retrieval. In this paper, a data extraction method from relational data to graph data is proposed, and the existing object level modeling method is improved, and an object level information retrieval algorithm based on embedded graph database is designed. Compared with tuple level information retrieval, object level retrieval has the advantages of small scale of data graph, high result integrity and no repetition. This paper adopts heuristic method to query rules, combines graph database with relational database, provides an effective solution for relational database information retrieval under the condition of massive data, and extends the application field of graph database. In order to verify the validity of the algorithm and the usability of the prototype system, the query effect and efficiency of the algorithm are verified by using DBLP dataset. Finally, the experimental results show that the object level keyword retrieval algorithm of graph database has good retrieval effect and high application prospect.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.3;TP311.13

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