本文選題：關(guān)系數(shù)據(jù)庫(kù)　切入點(diǎn)：關(guān)鍵詞查詢　出處：《黑龍江大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著數(shù)據(jù)庫(kù)技術(shù)的高速發(fā)展，普通用戶可以像網(wǎng)頁(yè)搜索引擎一樣通過(guò)輸入關(guān)鍵詞直接在關(guān)系數(shù)據(jù)上查詢結(jié)果，而不需要了解底層的數(shù)據(jù)庫(kù)模式以及復(fù)雜的SQL查詢語(yǔ)句。當(dāng)前的關(guān)系數(shù)據(jù)庫(kù)關(guān)鍵詞查詢系統(tǒng)還有一些不足的地方，例如，查詢系統(tǒng)的效率不高，查詢準(zhǔn)確率低，不支持短語(yǔ)查詢，查詢的結(jié)果展示比較單一等。本文針對(duì)上述的不足做了一些改進(jìn)工作。 第一，提出了CNI方法來(lái)提高候選網(wǎng)的生成速度。原有的基于模式圖查詢系統(tǒng)是在產(chǎn)生元組集圖后再進(jìn)行廣度優(yōu)先搜索，比較費(fèi)時(shí)，研究發(fā)現(xiàn)針對(duì)OR語(yǔ)義的查詢可以通過(guò)建立候選網(wǎng)索引的方法，，直接通過(guò)候選網(wǎng)索引得到所需要的候選網(wǎng)絡(luò)。 第二，提出了P-B方法來(lái)有效地識(shí)別關(guān)系數(shù)據(jù)庫(kù)中存在的短語(yǔ)，并將識(shí)別出的短語(yǔ)反饋給用戶，用戶自主選擇符合查詢條件的短語(yǔ)，從而提高了查詢性能和查詢準(zhǔn)確率。 第三，提出了TCD方法來(lái)有效地對(duì)查詢的結(jié)果連接樹聚類，將相同語(yǔ)義的連接樹分成一組，并給出每個(gè)聚類的描述，方便用戶理解查詢結(jié)果的語(yǔ)義，并快速找到準(zhǔn)確的查詢結(jié)果。 理論分析和實(shí)驗(yàn)表明上述提出的三種方法是有效的。
[Abstract]:With the rapid development of database technology, ordinary users can like web search engines like the input keywords directly results in relational data, without the need to understand the underlying database schema and complex SQL queries. Keywords current relational database query system has some deficiencies, for example, the efficiency of query system is not high. The low accuracy of the query, does not support the phrase query, the query results show is single. Aiming at the above problems do some improvement work.
First, we propose the CNI method to improve the production rate of candidate networks. The original query system based on schema graph is generated in the tuple set graph and breadth first search, time-consuming, the study found that in view of the OR semantic query through the method to establish the candidate net index, directly from the candidate network required by the candidate network index.
Second, P-B method is proposed to identify the phrases existing in relational database effectively, and feedback the identified phrases to users. Users can choose the phrases that meet the query conditions independently, thus improving query performance and query accuracy.
Third, the TCD method is proposed to effectively cluster the query results, and divide the connection tree of the same semantic into a set, and give the description of each cluster, so that users can understand the semantics of query results and find the exact query results quickly.
Theoretical analysis and experiments show that the three methods proposed above are effective.

【學(xué)位授予單位】：黑龍江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP311.13

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 朱彬;基于查詢模板的關(guān)鍵詞聚集查詢研究[D];河北大學(xué);2013年

本文編號(hào)：1604901