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Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our

In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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Description
A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words,

A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.

As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?

The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
ContributorsTang, Jiliang (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Aggarwal, Charu (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous

Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection). In this thesis, I carry out research along this direction with a particular focus on designing efficient optimization algorithms. I start with a unified bi-level learning model that contains several existing feature selection models as special cases. Then the proposed model is further extended to tackle the block-wise missing data, one of the major challenges in the diagnosis of Alzheimer's Disease (AD). Moreover, I propose a novel interpretable sparse group feature selection model that greatly facilitates the procedure of parameter tuning and model selection. Last but not least, I show that by solving the sparse group hard thresholding problem directly, the sparse group feature selection model can be further improved in terms of both algorithmic complexity and efficiency. Promising results are demonstrated in the extensive evaluation on multiple real-world data sets.
ContributorsXiang, Shuo (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans D (Committee member) / Davulcu, Hasan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and

The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing.

Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects.

Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
ContributorsGao, Huiji (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Caverlee, James (Committee member) / Arizona State University (Publisher)
Created2014
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Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate

This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection

Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The rapid growth in the high-throughput technologies last few decades makes the manual processing of the generated data to be impracticable. Even worse, the machine learning and data mining techniques seemed to be paralyzed against these massive datasets. High-dimensionality is one of the most common challenges for machine learning and

The rapid growth in the high-throughput technologies last few decades makes the manual processing of the generated data to be impracticable. Even worse, the machine learning and data mining techniques seemed to be paralyzed against these massive datasets. High-dimensionality is one of the most common challenges for machine learning and data mining tasks. Feature selection aims to reduce dimensionality by selecting a small subset of the features that perform at least as good as the full feature set. Generally, the learning performance, e.g. classification accuracy, and algorithm complexity are used to measure the quality of the algorithm. Recently, the stability of feature selection algorithms has gained an increasing attention as a new indicator due to the necessity to select similar subsets of features each time when the algorithm is run on the same dataset even in the presence of a small amount of perturbation. In order to cure the selection stability issue, we should understand the cause of instability first. In this dissertation, we will investigate the causes of instability in high-dimensional datasets using well-known feature selection algorithms. As a result, we found that the stability mostly data-dependent. According to these findings, we propose a framework to improve selection stability by solving these main causes. In particular, we found that data noise greatly impacts the stability and the learning performance as well. So, we proposed to reduce it in order to improve both selection stability and learning performance. However, current noise reduction approaches are not able to distinguish between data noise and variation in samples from different classes. For this reason, we overcome this limitation by using Supervised noise reduction via Low Rank Matrix Approximation, SLRMA for short. The proposed framework has proved to be successful on different types of datasets with high-dimensionality, such as microarrays and images datasets. However, this framework cannot handle unlabeled, hence, we propose Local SVD to overcome this limitation.
ContributorsAlelyani, Salem (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Zhao, Zheng (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual

Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual can leverage social network to search for information that is relevant to him or her. We propose to answer this question by developing computational algorithms that analyze a user's social network. The features of the social network we analyze include the network topology and member communications of a specific user's social network. Determining the "social value" of one's contacts is a valuable outcome of this research. The algorithms we developed were tested on Twitter, which is an extremely popular social network. Twitter was chosen due to its popularity and a majority of the communications artifacts on Twitter is publically available. In this work, the social network of a user refers to the "following relationship" social network. Our algorithm is not specific to Twitter, and is applicable to other social networks, where the network topology and communications are accessible. My approaches are as follows. For a user interested in using the system, I first determine the immediate social network of the user as well as the social contacts for each person in this network. Afterwards, I establish and extend the social network for each user. For each member of the social network, their tweet data are analyzed and represented by using a word distribution. To accomplish this, I use WordNet, a popular lexical database, to determine semantic similarity between two words. My mechanism of search combines both communication distance between two users and social relationships to determine the search results. Additionally, I developed a search interface, where a user can interactively query the system. I conducted preliminary user study to evaluate the quality and utility of my method and system against several baseline methods, including the default Twitter search. The experimental results from the user study indicate that my method is able to find relevant people and identify valuable contacts in one's social circle based on the query. The proposed system outperforms baseline methods in terms of standard information retrieval metrics.
ContributorsXu, Ke (Author) / Sundaram, Hari (Thesis advisor) / Ye, Jieping (Committee member) / Kelliher, Aisling (Committee member) / Arizona State University (Publisher)
Created2010
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Description
Cloud computing has received significant attention recently as it is a new computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and visualized manner. SaaS (Software-as-a-Service) provide a now paradigm in cloud computing, which goal is to provide an effective and intelligent way

Cloud computing has received significant attention recently as it is a new computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and visualized manner. SaaS (Software-as-a-Service) provide a now paradigm in cloud computing, which goal is to provide an effective and intelligent way to support end users' on-demand requirements to computing resources, including maturity levels of customizable, multi-tenancy and scalability. To meet requirements of on-demand, my thesis discusses several critical research problems and proposed solutions using real application scenarios. Service providers receive multiple requests from customers, how to prioritize those service requests to maximize the business values is one of the most important issues in cloud. An innovative prioritization model is proposed, which uses different types of information, including customer, service, environment and workflow information to optimize the performance of the system. To provide "on-demand" services, an accurate demand prediction and provision become critical for the successful of the cloud computing. An effective demand prediction model is proposed, and applied to a real mortgage application. To support SaaS customization and fulfill the various functional and quality requirements of individual tenants, a unified and innovative multi-layered customization framework is proposed to support and manage the variability of SaaS applications. To support scalable SaaS, a hybrid database design to support SaaS customization with two-layer database partitioning is proposed. To support secure SaaS, O-RBAC, an ontology based RBAC (Role based Access Control) model is used for Multi-Tenancy Architecture in clouds. To support a significant number of tenants, an easy to use SaaS construction framework is proposed. As a summary, this thesis discusses the most important research problems in cloud computing, towards effective and intelligent SaaS. The research in this thesis is critical to the development of cloud computing and provides fundamental solutions to those problems.
ContributorsShao, Qihong (Author) / Tsai, Wei-Tek (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Naphade, Milind (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable,

Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency.

This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.

The following chapters are provided in the architecture:

Multi-Tenancy Simulation Software-as-a-Service.

Policy Specification for MTA simulation environment.

Model Driven PaaS Based SaaS modeling.

Dynamic analysis and dynamic calibration for timing analysis.

Event-driven Service-Oriented Simulation Framework.

LTBD: A Triage Solution for SaaS.
ContributorsLi, Wu (Author) / Tsai, Wei-Tek (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015