Author: Li, Yu
Title: User-centric query optimization over web data services
Advisors: Yiu, Man Lung (COMP)
Degree: Ph.D.
Year: 2015
Subject: Querying (Computer science)
Web services
Hong Kong Polytechnic University -- Dissertations
Department: Department of Computing
Pages: xvi, 166 pages : illustrations
Language: English
Abstract: Web-based data services have become more and more popular. Users from different fields are interested in different web-based data services. In this thesis, we consider three application scenarios with different queries and objectives. We propose effective methods to process and optimize users' queries over web data services. In the first application, users are interested in datasets provided in Cloud Data Market (e.g., Windows Azure Data Market), which is an emerging cloud service that enables data owners to sell their datasets in a public cloud. Users (i.e., buyers) can access their interested data in data market via a RESTful API. Accessing data in the data market may not be free. We present PayLess, a system that helps users to process and optimize their SQL queries such that they pay less. In the second application, mobile users of location-based services (LBS) issue range/K-NN queries over points-of-interest (e.g., restaurants, cafes), and they require accurate query results with up-to-date travel times. Lacking the monitoring infrastructure for road traffic, the LBS may obtain live travel times of routes from online route APIs (e.g., Google Directions API and Bing Maps API) in order to offer accurate results. Our goal is to reduce the number of external requests issued by the LBS significantly while preserving accurate query results. In the third application, emerging spatial crowdsourcing web services enable the users (i.e., crowdsourcing workers) to complete spatial crowdsourcing tasks (like taking photos, conducting citizen journalism) that are tagged with both time and location features. We study the problem of online recommending an optimal route for a crowdsourcing worker, such that he can (i) reach his destination on time and (ii) receive the maximum reward for tasks along the route. We show that no algorithms can achieve a non-zero competitive ratio in this problem. Therefore, we propose several heuristics, and powerful pruning rules to speed up the methods. For each application scenario above, we evaluate the performance of our solutions on both synthetic data and real data. Our experimental results show that our solutions are effective and scalable.
Rights: All rights reserved
Access: open access

Files in This Item:
File Description SizeFormat 
b28259452.pdfFor All Users2.99 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show full item record

Please use this identifier to cite or link to this item: