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Improving Query Performance through Application-Driven Processing and Retrieval

Gibas, Michael A.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1218470693

Abstract Details

2008, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
The proliferation of massive data sets across many domains and the need to gain meaningful insights from these data sets highlight the need for advanced data retrieval techniques. Because I/O cost dominates the time required to answer a query, sequentially scanning the data and evaluating each data object against query criteria is not an effective option for large data sets. An effective solution should require reading as small a subset of the data as possible and should be able to address general query types. Access structures built over single attributes also may not be effective because 1) they may not yield performance that is comparable to that achievable by an access structure that prunes results over multiple attributes simultaneously and 2) they may not be appropriate for queries with results dependent on functions involving multiple attributes. Indexing a large number of dimensions is also not effective, because either too many subspaces must be explored or the index structure becomes too sparse at high dimensionalities. The key is to find solutions that allow for much of the search space to be pruned while avoiding this ‘curse of dimensionality’. This thesis pursues query performance enhancement using two primary means 1) processing the query effectively based on the characteristics of the query itself and 2) physically organizing access to data based on query patterns and data characteristics. Query performance enhancements are described in the context of several novel applications including 1) Optimization Queries, which presents an I/O-optimal technique to answer queries when the objective is to maximize or minimize some function over the data attributes, 2) High-Dimensional Index Selection, which offers a cost-based approach to recommend a set of low dimensional indexes to effectively address a set of queries, and 3) Multi-Attribute Bitmap Indexes, which describes extensions to a traditionally single-attribute query processing and access structure framework that enables improved query performance.
Hakan Ferhatosmanoglu (Advisor)
Atanas Rountev (Committee Member)
Hui Fang (Committee Member)
153 p.
Computer Science
Databases; Query Processing; Indexing; High-Dimensional

Recommended Citations

Citations

  • Gibas, M. A. (2008). Improving Query Performance through Application-Driven Processing and Retrieval [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218470693

    APA Style (7th edition)

  • Gibas, Michael. Improving Query Performance through Application-Driven Processing and Retrieval. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1218470693.

    MLA Style (8th edition)

  • Gibas, Michael. "Improving Query Performance through Application-Driven Processing and Retrieval." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218470693

    Chicago Manual of Style (17th edition)

osu1218470693
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© 2008, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.