Matching Items (45)
Description
The Global Change Assessment Model (GCAM) is an integrated assessment tool for exploring consequences and responses to global change. However, the current iteration of GCAM relies on NetCDF file outputs which need to be exported for visualization and analysis purposes. Such a requirement limits the uptake of this modeling platform for analysts that may wish to explore future scenarios. This work has focused on a web-based geovisual analytics interface for GCAM. Challenges of this work include enabling both domain expert and model experts to be able to functionally explore the model. Furthermore, scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment. The inter-comparison of scenario analysis remains a big challenge in both the climate science and visualization communities. In a close collaboration with the Global Change Assessment Model team, I developed the first visual analytics interface for GCAM with a series of interactive functions to help users understand the simulated impact of climate change on sectors of the global economy, and at the same time allow them to explore inter comparison of scenario analysis with GCAM models. This tool implements a hierarchical clustering approach to allow inter-comparison and similarity analysis among multiple scenarios over space, time, and multiple attributes through a set of coordinated multiple views. After working with this tool, the scientists from the GCAM team agree that the geovisual analytics tool can facilitate scenario exploration and enable scientific insight gaining process into scenario comparison. To demonstrate my work, I present two case studies, one of them explores the potential impact that the China south-north water transportation project in the Yangtze River basin will have on projected water demands. The other case study using GCAM models demonstrates how the impact of spatial variations and scales on similarity analysis of climate scenarios varies at world, continental, and country scales.
ContributorsChang, Zheng (Author) / Maciejewski, Ross (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / White, Dave (Committee member) / Luo, Wei (Committee member) / Arizona State University (Publisher)
Created2015
Description
A Cyber Physical System consists of a computer monitoring and controlling physical processes usually in a feedback loop. These systems are increasingly becoming part of our daily life ranging from smart buildings to medical devices to automobiles. The controller comprises discrete software which may be operating in one of the many possible operating modes and interacting with a changing physical environment in a feedback loop. The systems with such a mix of discrete and continuous dynamics are usually termed as hybrid systems. In general, these systems are safety critical, hence their correct operation must be verified. Model Based Design (MBD) languages like Simulink are being used extensively for the design and analysis of hybrid systems due to the ease in system design and automatic code generation. It also allows testing and verification of these systems before deployment. One of the main challenges in the verification of these systems is to test all the operating modes of the control software and reduce the amount of user intervention.
This research aims to provide an automated framework for the structural analysis and instrumentation of hybrid system models developed in Simulink. The behavior of the components introducing discontinuities in the model are automatically extracted in the form of state transition graphs. The framework is integrated in the S-TaLiRo toolbox to demonstrate the improvement in mode coverage.
This research aims to provide an automated framework for the structural analysis and instrumentation of hybrid system models developed in Simulink. The behavior of the components introducing discontinuities in the model are automatically extracted in the form of state transition graphs. The framework is integrated in the S-TaLiRo toolbox to demonstrate the improvement in mode coverage.
ContributorsThekkalore Srinivasa, Rahul (Author) / Fainekos, Georgios (Thesis advisor) / Mayyas, Abdel Ra’ouf (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.
ContributorsSun, Xin (Author) / Tsai, Wei-Tek (Thesis advisor) / Xue, Guoliang (Committee member) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2016
Description
The proper quantification and visualization of uncertainty requires a high level of domain knowledge. Despite this, few studies have collected and compared the roles, experiences and opinions of scientists in different types of uncertainty analysis. I address this gap by conducting two types of studies: 1) a domain characterization study with general questions for experts from various fields based on a recent literature review in ensemble analysis and visualization, and; 2) a long-term interview with domain experts focusing on specific problems and challenges in uncertainty analysis. From the domain characterization, I identified the most common metrics applied for uncertainty quantification and discussed the current visualization applications of these methods. Based on the interviews with domain experts, I characterized the background and intents of the experts when performing uncertainty analysis. This enables me to characterize domain needs that are currently underrepresented or unsupported in the literature. Finally, I developed a new framework for visualizing uncertainty in climate ensembles.
ContributorsLiang, Xing (Author) / Maciejewski, Ross (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Testing and Verification of Cyber-Physical Systems (CPS) is a challenging problem. The challenge arises as a result of the complex interactions between the components of these systems: the digital control, and the physical environment. Furthermore, the software complexity that governs the high-level control logic in these systems is increasing day by day. As a result, in recent years, both the academic community and the industry have been heavily invested in developing tools and methodologies for the development of safety-critical systems. One scalable approach in testing and verification of these systems is through guided system simulation using stochastic optimization techniques. The goal of the stochastic optimizer is to find system behavior that does not meet the intended specifications.
In this dissertation, three methods that facilitate the testing and verification process for CPS are presented:
1. A graphical formalism and tool which enables the elicitation of formal requirements. To evaluate the performance of the tool, a usability study is conducted.
2. A parameter mining method to infer, analyze, and visually represent falsifying ranges for parametrized system specifications.
3. A notion of conformance between a CPS model and implementation along with a testing framework.
The methods are evaluated over high-fidelity case studies from the industry.
In this dissertation, three methods that facilitate the testing and verification process for CPS are presented:
1. A graphical formalism and tool which enables the elicitation of formal requirements. To evaluate the performance of the tool, a usability study is conducted.
2. A parameter mining method to infer, analyze, and visually represent falsifying ranges for parametrized system specifications.
3. A notion of conformance between a CPS model and implementation along with a testing framework.
The methods are evaluated over high-fidelity case studies from the industry.
ContributorsHoxha, Bardh (Author) / Fainekos, Georgios (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Maciejewski, Ross (Committee member) / Ben Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2017
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 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.
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
Description
Affect signals what humans care about and is involved in rational decision-making and action selection. Many technologies may be improved by the capability to recognize human affect and to respond adaptively by appropriately modifying their operation. This capability, named affect-driven self-adaptation, benefits systems as diverse as learning environments, healthcare applications, and video games, and indeed has the potential to improve systems that interact intimately with users across all sectors of society. The main challenge is that existing approaches to advancing affect-driven self-adaptive systems typically limit their applicability by supporting the creation of one-of-a-kind systems with hard-wired affect recognition and self-adaptation capabilities, which are brittle, costly to change, and difficult to reuse. A solution to this limitation is to leverage the development of affect-driven self-adaptive systems with a manufacturing vision.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
ContributorsGonzalez-Sanchez, Javier (Author) / Burleson, Winslow (Thesis advisor) / Collofello, James (Thesis advisor) / Garlan, David (Committee member) / Sarjoughian, Hessam S. (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2016
Description
The complexity of the systems that software engineers build has continuously grown since the inception of the field. What has not changed is the engineers' mental capacity to operate on about seven distinct pieces of information at a time. The widespread use of UML has led to more abstract software design activities, however the same cannot be said for reverse engineering activities. The introduction of abstraction to reverse engineering will allow the engineer to move farther away from the details of the system, increasing his ability to see the role that domain level concepts play in the system. In this thesis, we present a technique that facilitates filtering of classes from existing systems at the source level based on their relationship to concepts in the domain via a classification method using machine learning. We showed that concepts can be identified using a machine learning classifier based on source level metrics. We developed an Eclipse plugin to assist with the process of manually classifying Java source code, and collecting metrics and classifications into a standard file format. We developed an Eclipse plugin to act as a concept identifier that visually indicates a class as a domain concept or not. We minimized the size of training sets to ensure a useful approach in practice. This allowed us to determine that a training set of 7:5 to 10% is nearly as effective as a training set representing 50% of the system. We showed that random selection is the most consistent and effective means of selecting a training set. We found that KNN is the most consistent performer among the learning algorithms tested. We determined the optimal feature set for this classification problem. We discussed two possible structures besides a one to one mapping of domain knowledge to implementation. We showed that classes representing more than one concept are simply concepts at differing levels of abstraction. We also discussed composite concepts representing a domain concept implemented by more than one class. We showed that these composite concepts are difficult to detect because the problem is NP-complete.
ContributorsCarey, Maurice (Author) / Colbourn, Charles (Thesis advisor) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsAllen, Eowyn K. (Author) / Falconer, Steven E. (Author) / Sarjoughian, Hessam S. (Author) / Barton, C. Michael (Author) / Fall, Patricia L. (Author)
Created2006
ContributorsSarjoughian, Hessam S. (Author) / Mayer, Gary R. (Author) / Allen, Eowyn K. (Author)
Created2006