• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Data allocation and query optimization in large scale distributed databases

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_td_9713439_sip1_c.pdf
    Size:
    9.388Mb
    Format:
    PDF
    Download
    Author
    Zhou, Zehai, 1962-
    Issue Date
    1996
    Keywords
    Business Administration, Management.
    Computer Science.
    Advisor
    Sheng, Olivia R. Liu
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Distributed database technology is expected to have a significant impact on data processing in the upcoming years because distributed database systems have many potential advantages over centralized systems for geographically distributed organizations. Data allocation and query optimization are two of the most important aspects of distributed database design. Data allocation involves placing a database and the applications that run against it in the multiple sites of a network. It is a very complex problem consisting of two processes: data fragmentation and fragment allocation. Data fragmentation involves the partitioning of each relation into a group of fragment relations while fragment allocation deals with the distribution of these fragmented relations across the sites of the distributed system. Query optimization includes designing algorithms that analyze and convert queries into a set of data manipulation operations. Both the data allocation and query optimization problems are NP-hard in nature and notoriously difficult to solve. We have attempted to combine the two highly interrelated and interactive decision processes in data allocation by formulating them as integer programs taking into consideration different constraints and under various assumptions. Various solution methods are discussed and a new linearization method is investigated. We next analyze the query optimization problem and reduce it to a join ordering problem. Several heuristics and a genetic algorithm have been developed for solving the join ordering problem. Some computational experiments on these algorithms were conducted and solution qualities compared. The computation experiments show that the suggested linearization method performs clearly and consistently better than a currently widely used method and that heuristics and genetic algorithms are viable methods for solving query optimization problem. It is anticipated that the models and solution methods developed in this study for data allocation and query optimization in distributed database systems may be of practical as well as theoretical use. Nevertheless, much more needs to be done to solve the distributed database design problems in order to achieve its potential benefits. Our models and solution methods can be the starting point for eventual resolution of these complex problems in large scale distributed database systems.
    Type
    text
    Dissertation-Reproduction (electronic)
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Industrial Management
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.