近來有越來越多的研究傾向於延伸資料庫系統的能力提供使用者偏好這一類的查詢。在我們的研究中，我們分別針對在集中式或分散式環境下提出使用者偏好查詢時所需要的數個有效率的演算法。在討論這些使用者偏好查詢前，由於我們研究中的數個使用者偏好皆是在分散式的感測環境中執行，而感測環境本身是一個電力受限且處理器能力有限的環境。因此，在本研究的一開始我們在不改變感測範圍的情況下透過動態的調整感應半徑，減少感測器電量的消耗以延長感測環境監測的壽命，如此一來才可提供使用者在感測環境中執行所提出的偏好查詢。明確的說，我們將在本論文中討論三個不同類型的使用者偏好查詢。第一類的使用者偏好查詢為相似搜尋，使用者可以透過這樣的搜尋找到最接近的答案，特別是當感測環境中找不到任何一個完全符合查詢的資料值。第二類則是天際線查詢，天際線查詢可以提供使用者針對不同的屬性給定其所偏好的資料值，例如屬性值較高，較低等不同的資料值。第三個則是前k筆組合式天際線查詢，這一類的查詢可以提供使用者從多筆資料中所產生的組合中找出前k個令使用者最感興趣的組合式天際線。我們分別提出了幾個有效率的方法來解決這三類的問題。所有的方法皆是漸進式並能夠在很短的時間內回答使用者的查詢。我們透過許多的實驗分析證明我們的方法不論是在集中式或分散式的環境中都能有效率的解決使用者偏好的查詢。

Supporting user preferences has recently been a growing interest in extending the capability of database systems. In our work, we propose several efficient structures and algorithms for evaluating such user preference queries in centralized and distributed environments. Since some of the user preference queries are processed in distributed sensor networks which are usually with limited power sources and low computation capability, we first dynamically adjust its sensing radius in our work so that the global coverage of the whole detecting area remains unchanged, energy consumption is minimized, and the lifetime of the sensor network is extended. Specifically, we study the evaluation of three specific types of user preference queries. First, a similarity query allows the users to retrieve the similar data, even if there is no value which exactly matches the given value of the query in sensor networks. Second, a skyline query allows the users to specify whether they favor low, high or different values of the attributes in sensor networks. Third, a top-k combinatorial skyline query allows the users to evaluate the top-k interesting skyline combinations from various kind of combinations of the give tuples. We propose several approaches to efficiently evaluate these queries. All approaches are progressive and provide a fast initial response time. All the schemes are further analyzed empirically through extensive experimental studies and the results indicate that they are effective in supporting user preferences in both centralized and distributed database environments.

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