ASU Electronic Theses and Dissertations
- 3 English
- 3 Public
Similarity search in high-dimensional spaces is popular for applications like image processing, time series, and genome data. In higher dimensions, the phenomenon of curse of dimensionality kills the effectiveness of most of the index structures, giving way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity searches. In addition to range searches and k-nearest neighbor searches, there is a need to answer negative queries formed by excluded regions, in high-dimensional data. Though there have been a slew of variants of LSH to improve efficiency, reduce storage, and provide better accuracies, none of the techniques are capable of answering ...
- Bhat, Aneesha, Candan, Kasim Selcuk, Davulcu, Hasan, et al.
- Created Date
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 ...
- Nagarkar, Parth, Candan, Kasim S, Davulcu, Hasan, et al.
- Created Date
Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting the skyline that takes into account the preferences of the user is still open. Each user has varying interests towards each attribute and hence "one size fits all" methodology might not satisfy all the users. True user satisfaction can be obtained only when the skyline is tailored specifically for each user based on his preferences. This research investigates the problem of preference aware skyline processing which consists of inferring the ...
- Rathinavelu, Sriram, Candan, Kasim Selcuk, Davulcu, Hasan, et al.
- Created Date
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries.
For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.