ASU Electronic Theses and Dissertations

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The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis. Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic ...

Contributors
Garg, Yash, Candan, Kasim Selcuk, Chowell-Punete, Gerardo, et al.
Created Date
2015

In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analysis tasks, such as visualization, segmentation, and searching / retrieving based on multi-variate time series similarities. ...

Contributors
Wang, Xiaolan, Candan, Kasim Selcuk, Sapino, Maria Luisa, et al.
Created Date
2013

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 ...

Contributors
Bhat, Aneesha, Candan, Kasim Selcuk, Davulcu, Hasan, et al.
Created Date
2016

Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. ...

Contributors
Kim, Jung Hyun, Candan, K. Selcuk, Davulcu, Hasan, et al.
Created Date
2017

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 ...

Contributors
Nagarkar, Parth, Candan, Kasim S, Davulcu, Hasan, et al.
Created Date
2017

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 gradformat@asu.edu.