Full metadata
Title
Query Workload-Aware Index Structures for Range Searches in 1D, 2D, and High-Dimensional Spaces
Description
Most current database management systems are optimized for single query execution.
Yet, often, queries come as part of a query workload. Therefore, there is a need
for index structures that can take into consideration existence of multiple queries in a
query workload and efficiently produce accurate results for the entire query workload.
These index structures should be scalable to handle large amounts of data as well as
large query workloads.
The main objective of this dissertation is to create and design scalable index structures
that are optimized for range query workloads. Range queries are an important
type of queries with wide-ranging applications. There are no existing index structures
that are optimized for efficient execution of range query workloads. There are
also unique challenges that need to be addressed for range queries in 1D, 2D, and
high-dimensional spaces. In this work, I introduce novel cost models, index selection
algorithms, and storage mechanisms that can tackle these challenges and efficiently
process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular,
I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces),
and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently
handle range query workload and the unique challenges arising from their respective
spaces. I experimentally show the effectiveness of the above proposed index structures
by comparing with state-of-the-art techniques.
Yet, often, queries come as part of a query workload. Therefore, there is a need
for index structures that can take into consideration existence of multiple queries in a
query workload and efficiently produce accurate results for the entire query workload.
These index structures should be scalable to handle large amounts of data as well as
large query workloads.
The main objective of this dissertation is to create and design scalable index structures
that are optimized for range query workloads. Range queries are an important
type of queries with wide-ranging applications. There are no existing index structures
that are optimized for efficient execution of range query workloads. There are
also unique challenges that need to be addressed for range queries in 1D, 2D, and
high-dimensional spaces. In this work, I introduce novel cost models, index selection
algorithms, and storage mechanisms that can tackle these challenges and efficiently
process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular,
I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces),
and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently
handle range query workload and the unique challenges arising from their respective
spaces. I experimentally show the effectiveness of the above proposed index structures
by comparing with state-of-the-art techniques.
Date Created
2017
Contributors
- Nagarkar, Parth (Author)
- Candan, Kasim S (Thesis advisor)
- Davulcu, Hasan (Committee member)
- Sapino, Maria Luisa (Committee member)
- Sarwat, Mohamed (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
188 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.45562
Level of coding
minimal
Note
Doctoral Dissertation Computer Science 2017
System Created
- 2017-10-02 07:20:59
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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