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Efficient information access for location-based services in mobile environments.

機(jī)譯：移動(dòng)環(huán)境中基于位置的服務(wù)的有效信息訪問(wèn)。

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The demand for pervasive access of location-related information (e.g., local traffic, restaurant locations, navigation maps, weather conditions, pollution index, etc.) fosters a tremendous application base of Location Based Services (LBSs). Without loss of generality, we model location-related information as spatial objects and the accesses of such information as Location-Dependent Spatial Queries (LDSQs) in LBS systems. In this thesis, we address the efficiency issue of LDSQ processing through several innovative techniques.First, we develop a new client-side data caching model, called Complementary Space Caching (CSC), to effectively shorten data access latency over wireless channels. This is motivated by an observation that mobile clients, with only a subset of objects cached, do not have sufficient knowledge to assert whether or not certain object access from the remote server are necessary for given LDSQs. To address this problem, our CSC requests each client to cache a global data view, that is composed of (i) cached spatial objects and (ii) complementary regions that cover the locations of all the non-cached objects. With a global data view cached, CSC enables clients to assert the completeness of LDSQ results locally. Second, we investigate two new types of complex LDSQs, namely, Nearest Surrounder (NS) Queries and Skyline Queries. Both of them have a wide application base. An NS query returns spatial objects along with disjointed angular ranges within which they are the nearest to a given query point. A skyline query retrieves non-dominated spatial objects. An object o is said to be dominated if there is another object o' that is strictly better than o for at least one attribute and is not worse than o for all the other attributes. We conduct in-depth analysis and propose novel techniques to efficiently answer these new queries. Third, we propose an LDSQ processing framework, namely ROAD, to support efficient access of spatial objects on a road network. ROAD adopts a search space pruning technique that has not been explored before in this context. In ROAD, a large road network is organized as a hierarchy of interconnected regional sub-networks called Rnets, i.e., search subspaces. Further, with two novel concepts, namely, (i) shortcuts, that allow jumps across Rnets to accelerate the search traversal, and (ii) object abstracts, that provide search guidance during traversals, searches supported by ROAD can bypass those Rnets that contain no object of interest. Also, ROAD is flexible to support various LDSQs and objects.We conduct extensive empirical studies to evaluate the performance of our proposed approaches. The experiment results demonstrate the efficiency of our approaches and their superiority over state-of-the-art approaches in corresponding domains.
機(jī)譯：對(duì)位置相關(guān)信息（例如，本地交通，飯店位置，導(dǎo)航地圖，天氣狀況，污染指數(shù)等）的普遍訪問(wèn)的需求促進(jìn)了基于位置的服務(wù)（LBS）的巨大應(yīng)用基礎(chǔ)。在不失一般性的前提下，我們將與位置相關(guān)的信息建模為空間對(duì)象，并在LBS系統(tǒng)中將諸如位置依賴(lài)的空間查詢(xún)（LDSQ）之類(lèi)的信息的訪問(wèn)建模。本文通過(guò)幾種創(chuàng)新技術(shù)解決了LDSQ處理的效率問(wèn)題。首先，我們開(kāi)發(fā)了一種新的客戶(hù)端數(shù)據(jù)緩存模型，稱(chēng)為補(bǔ)充空間緩存（CSC），以有效地縮短無(wú)線信道上的數(shù)據(jù)訪問(wèn)延遲。這是由于觀察到的，即僅緩存了??一部分對(duì)象的移動(dòng)客戶(hù)端沒(méi)有足夠的知識(shí)來(lái)斷言對(duì)于給定的LDSQ是否需要從遠(yuǎn)程服務(wù)器進(jìn)行某些對(duì)象訪問(wèn)。為了解決這個(gè)問(wèn)題，我們的CSC請(qǐng)求每個(gè)客戶(hù)端緩存一個(gè)全局?jǐn)?shù)據(jù)視圖，該視圖由（i）緩存的空間對(duì)象和（ii）覆蓋所有非緩存對(duì)象位置的互補(bǔ)區(qū)域組成。通過(guò)緩存全局?jǐn)?shù)據(jù)視圖，CSC使客戶(hù)端能夠在本地?cái)嘌訪DSQ結(jié)果的完整性。其次，我們研究了兩種新型的復(fù)雜LDSQ，即最近環(huán)繞聲（NS）查詢(xún)和天際線查詢(xún)。兩者都有廣泛的應(yīng)用基礎(chǔ)。 NS查詢(xún)返回空間對(duì)象以及不相交的角度范圍，在該角度范圍內(nèi)它們最接近給定查詢(xún)點(diǎn)。天際線查詢(xún)檢索非主導(dǎo)的空間對(duì)象。如果存在至少在一個(gè)屬性上嚴(yán)格優(yōu)于o且在所有其他屬性上均不劣于o的另一個(gè)對(duì)象o'，則稱(chēng)對(duì)象o為主導(dǎo)。我們進(jìn)行深入的分析，并提出新穎的技術(shù)來(lái)有效回答這些新查詢(xún)。第三，我們提出了一個(gè)LDSQ處理框架，即ROAD，以支持對(duì)道路網(wǎng)絡(luò)中空間對(duì)象的有效訪問(wèn)。 ROAD采用了一種搜索空間修剪技術(shù)，在這種情況下以前沒(méi)有進(jìn)行過(guò)探索。在ROAD中，大型道路網(wǎng)絡(luò)被組織為稱(chēng)為Rnets的互連區(qū)域子網(wǎng)絡(luò)（即搜索子空間）的層次結(jié)構(gòu)。此外，有了兩個(gè)新穎的概念，即（i）允許在Rnet上跳轉(zhuǎn)以加快搜索遍歷的快捷方式，以及（ii）在遍歷期間提供搜索指導(dǎo)的對(duì)象摘要，ROAD支持的搜索可以繞過(guò)那些不包含Rnet的Rnet。感興趣的對(duì)象。此外，ROAD可以靈活地支持各種LDSQ和對(duì)象。我們進(jìn)行了廣泛的經(jīng)驗(yàn)研究，以評(píng)估我們提出的方法的性能。實(shí)驗(yàn)結(jié)果證明了我們的方法的效率及其在相應(yīng)領(lǐng)域中相對(duì)于最新方法的優(yōu)越性。

著錄項(xiàng)

  • 作者

    Lee, Chi Keung.;

  • 作者單位

    The Pennsylvania State University.;

  • 授予單位 The Pennsylvania State University.;
  • 學(xué)科 Information Science.Computer Science.
  • 學(xué)位 Ph.D.
  • 年度 2009
  • 頁(yè)碼 193 p.
  • 總頁(yè)數(shù) 193
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類(lèi)
  • 關(guān)鍵詞

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