Dissertation/Thesis Abstract

Efficient updates for continuous queries over moving objects
by Hsueh, Yu-Ling, Ph.D., University of Southern California, 2009, 160; 3368702
Abstract (Summary)

As a result of recent technological advances, mobile devices with significant computational abilities, gigabytes of storage, and wireless communication capabilities have become widely available. In addition, positioning chips are embedded in more and more of these mobile devices. These services correspond to continuous spatial queries that are posted within an environment of moving objects and produce as their results a time-varying set of objects. In the most ambitious case both queries and data objects are dynamic, making it very challenging to find an efficient query evaluation strategy. Furthermore, monitoring moving objects to maintain the correctness of the query answers often incurs frequent location updates from these moving objects. To address these two challenges we group our work into three main topics, namely (i) efficient location updates, (ii) efficient query result updates, and (iii) query approximation. We now give an overview of each group in turn.

Efficient Location Updates. Two novel efficient location update strategies are proposed in this dissertation. The first strategy for a trajectory movement environment is the Adaptive Safe Region (ASR) technique that retrieves an adjustable safe region which is continuously reconciled with the surrounding dynamic queries. The communication overhead is reduced in a highly dynamic environment where both queries and data objects change their positions frequently. In addition, we design a framework that supports multiple query types (e.g., range and c- kNN queries). In this framework, our query re-evaluation algorithms take advantage of ASRs and issue location probes only to the affected data objects, without flooding the system with many unnecessary location update requests.

The second strategy for an arbitrary movement environment is the Partition-based Lazy Update (PLU, for short) algorithm that elevates this idea further by adopting Location Information Tables ( LIT) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. We first define the data structure of a LIT which is essentially packed with a set of surrounding query locations across the terrain and discuss the mobile-side and server-side processes in correspondence to the utilization of LITs. In addition, we further apply three lossless compression methods that condense a LIT to reduce the data stream size.

Efficient Query Result Updates. In the second part of the dissertation, we focus on the problem of maintaining skyline queries efficiently over dynamic objects with d dimensions for totally-ordered and partially-ordered domains. Skyline queries are an important new search capability for multi-dimensional databases. For the totally-ordered domain skyline queries, we propose the ESC algorithm, an Efficient update approach for Skyline Computations, which creates a pre-computed second skyline set that facilitates an efficient and incremental skyline update strategy and results in a quicker response time. With the knowledge of the second skyline set, ESC enables (a) to efficiently find the substitute skyline points from the second skyline set only when removing or updating a skyline point (which is called a first skyline point) and (b) to delegate the most time-consuming skyline update computation to another independent procedure, which is executed after the complete updated query result is reported.

For the skyline queries with partially-ordered domains, we introduce a novel approach, termed C-SKY, to reduce the latency by caching query results with their unique user preferences. The results of skyline queries performed on data sets with partially-ordered domains vary depending on users' preference profiles specified for the partially-ordered domains. Existing work has addressed the issue of handling each individual query with some efficiency. However, processing large volumes of such queries for online applications with low response time is still very challenging. Of paramount importance in this case is that cached queries with compatible preference profiles need to be utilized. For this purpose, we introduce a similarity measure that establishes how related a new query is to each of the previously cached queries and profiles.

Query Approximation. In existing methods, the cost of retrieving the exact c-kNN data set is expensive, particularly in highly dynamic spatio-temporal applications. The cost includes the location updates of the moving objects when the velocities change over time and the number of continuous kNN queries posed by the moving object to the server. In some applications (e.g., finding my nearest taxies while I am moving), obtaining the perfect result set is not necessary. For such applications, we introduce an AC-kNN technique that approximates the results of the classic c-kNN algorithm, but with efficient updates and while still retaining a competitive accuracy. (Abstract shortened by UMI.)

Indexing (document details)
Advisor: Zimmermann, Roger
Commitee: Kuo, C.-C. Jay, Shahabi, Cyrus
School: University of Southern California
Department: Computer Science
School Location: United States -- California
Source: DAI-B 70/08, Dissertation Abstracts International
Subjects: Computer science
Keywords: Continuous queries, Moving objects
Publication Number: 3368702
ISBN: 9781109294965