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.

Displaying 1 - 2 of 2
Filtering by

Clear all filters

155671-Thumbnail Image.png
Description
Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and

the central idea is that multiple tenant applications can be developed using compo

nents stored in the SaaS infrastructure. Recently, MTA has been extended where

a tenant application can have its own sub-tenants as the tenant application acts

like a SaaS infrastructure. In other

Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and

the central idea is that multiple tenant applications can be developed using compo

nents stored in the SaaS infrastructure. Recently, MTA has been extended where

a tenant application can have its own sub-tenants as the tenant application acts

like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy

Architecture ). In STA, each tenant application not only need to develop its own

functionalities, but also need to prepare an infrastructure to allow its sub-tenants to

develop customized applications. This dissertation formulates eight models for STA,

and proposes a Variant Point based customization model to help tenants and sub

tenants customize tenant and sub-tenant applications. In addition, this dissertation

introduces Crowd- sourcing to become the core of STA component development life

cycle. To discover fit tenant developers or components to help building and com

posing new components, dynamic and static ranking models are proposed. Further,

rank computation architecture is presented to deal with the case when the number of

tenants and components becomes huge. At last, an experiment is performed to prove

rank models and the rank computation architecture work as design.
ContributorsZhong, Peide (Author) / Davulcu, Hasan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Huang, Dijiang (Committee member) / Tsai, Wei-Tek (Committee member) / Arizona State University (Publisher)
Created2017
157543-Thumbnail Image.png
Description
With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling

With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling intelligent decision making. In this thesis, I first introduce the Low-rank, Win-dowed, Incremental Singular Value Decomposition (SVD) framework to inclemently maintain SVD factors over streaming data. Then, I present the Group Incremental Non-Negative Matrix Factorization framework to leverage redundancies in the data to speed up incremental processing. They primarily tackle the challenges of using factorization models in the scenarios with streaming textual data. In order to tackle the challenges in improving the effectiveness and efficiency of generative models in this streaming environment, I introduce the Incremental Dynamic Multiscale Topic Model framework, which identifies multi-scale patterns and their evolutions within streaming datasets. While the latent factor models assume the linear independence in the latent factors, the generative models assume the observation is generated from a set of latent variables with various distributions. Furthermore, some models may not be accessible or their underlying structures are too complex to understand, such as simulation ensembles, where there may be thousands of parameters with a huge parameter space, the only way to learn information from it is to execute real simulations. When performing knowledge discovery and decision making through data- and model-driven simulation ensembles, it is expensive to operate these ensembles continuously at large scale, due to the high computational. Consequently, given a relatively small simulation budget, it is desirable to identify a sparse ensemble that includes the most informative simulations, while still permitting effective exploration of the input parameter space. Therefore, I present Complexity-Guided Parameter Space Sampling framework, which is an intelligent, top-down sampling scheme to select the most salient simulation parameters to execute, given a limited computational budget. Moreover, I also present a Pivot-Guided Parameter Space Sampling framework, which incrementally maintains a diverse ensemble of models of the simulation ensemble space and uses a pivot guided mechanism for future sample selection.
ContributorsChen, Xilun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Pedrielli, Giulia (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019