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|標題:||分散式環境下基於序列型樣之 KNN 查詢|
Distributed KNN Query Processing for Sequences with Similar Patterns
Sequence Pattern Mining
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|摘要:||序列資料在我們的日常生活中隨處可見，像是物件移動軌跡、生物基因序列、金融或購買商品交易記錄等等，涵蓋的範圍極廣，具有極高的研究價值。序列型樣分析 (Sequence Pattern Analysis) 是資料探勘最重要的研究領域之一，主要是要找出隱藏在序列資料中的特徵 (型樣)，例如 DNA 或蛋白質序列的 Motif、物件移動軌跡的規律性或瀏覽網頁的習慣性等等。序列資料的特徵比原始序列更容易理解且更具代表性，經常用於資料的檢索和查詢。近來，隨著生物晶片、感測和無線網路等相關技術的進步，大量序列資料快速累積。序列資料有著大量且分散的特性，傳統以單一伺服器儲存和處理的方式已無法負荷，因此以分散式資料庫來儲存序列資料是必然的趨勢。
面對龐大且分散的資料，如何進行有效率的查詢是一個重要且困難的課題。本論文的研究主題主要是在分散式環境中基於序列型樣進行比對的 KNN 查詢問題。為解決此問題，我們提出分散式漸進二元探測 (Distributed and Incremental KNN query with Binary Probing, DIKNN-BP) 演算法，其以 Probabilistic Suffix Tree (PST) 建構演算法探勘序列資料的型樣，並以漸進式的方式傳送查詢目標的型樣給分散的資料庫，各資料庫根據已接收的型樣進行相似度的上下限的估計，藉以排除不合格候選物件，最後回傳與目標物件最相似的 K 個物件。此外，我們根據所的提出的七個定理設計二元探測法，以加速上下限的收斂和KNN的查詢。根據實驗結果顯示 DIKNN-BP 演算法可以有效節省傳送型樣數及資料傳輸量，比起傳統的查詢方式更能節省查詢時間。|
Sequence data are ubiquitous in our daily life, such as i.e., moving object trajectories、biological gene sequences、records of the commodity purchasing, which are of high research value. Sequence Pattern Analysis is one of the most important research fields in data mining. Its goal is to discover important features (patterns) hidden in sequence data, such as movement regularity in trajectories, motif in DNA or protein sequences, or purchasing behavior in transaction sequences. Recently, with the advance of DNA microarray chips, sensing, and wireless techniques, the huge amounts of sequence data are rapidly accumulated. Since a centralized server cannot afford the huge and ubiquitous sequence data, a distributed approach is the natural trend. Facing the huge and distributed sequence data, how to query data efficiently is an important and difficulty problem. In the thesis, we focus on the problem of distributed KNN query of sequences with similar patterns. To solve this problem, we propose a distributed and incremental KNN query with binary probing (DIKNN-BP) algorithm. It mines sequence patterns by using Probabilistic Suffix Tree（PST）and transmits pattern to remote servers in a progressive manner. The remote servers estimate the upper and lower bound of similarity value between target object and remote candidate objects and based on which to prune unqualified candidates. In addition, we derive five theorems and propose a binary probing approach to speed up the converge of upper and lower bound, also the KNN query. According to the experimental results, our DIKNN-BP algorithm can obtain the KNN solution with fewer patterns transmitted and with less data transmitted. Compared with a naive approach, it achieves a shorter query execution time.
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