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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Data Preparation For Social Network Mining And Analysis, Yazhe Wang
Data Preparation For Social Network Mining And Analysis, Yazhe Wang
Dissertations and Theses Collection (Open Access)
This dissertation studies the problem of preparing good-quality social network data for data analysis and mining. Modern online social networks such as Twitter, Facebook, and LinkedIn have rapidly grown in popularity. The consequent availability of a wealth of social network data provides an unprecedented opportunity for data analysis and mining researchers to determine useful and actionable information in a wide variety of fields such as social sciences, marketing, management, and security. However, raw social network data are vast, noisy, distributed, and sensitive in nature, which challenge data mining and analysis tasks in storage, efficiency, accuracy, etc. Many mining algorithms cannot …
Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati
Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati
Dissertations and Theses Collection (Open Access)
Opinions are central to almost all human activities by influencing greatly the decision making process. In this thesis, we present the problems of mining issues, extracting entities and suggestive opinions towards the entities, detecting thoughtful comments, and extracting stances and ideological expressions from online comments in the sociopolitical domain. This study is essential for opinion mining applications that are beneficial for policy makers, government sectors and social organizations. Much work has been done to try to uncover consumer sentiments from online comments to help businesses improve their products and services. However, sociopolitical opinion mining poses new challenges due to complex …
Multi-Cost And Upgradable Spatial Network Databases, Yimin Lin
Multi-Cost And Upgradable Spatial Network Databases, Yimin Lin
Dissertations and Theses Collection (Open Access)
In this dissertation, we first consider data processing problems in multi-cost networks and in upgradable networks. These network types are motivated by real-life situations, which do not fall under the standard spatial network formulation and have not received much attention from database researchers. In a multi-cost network (MCN), each edge is associated with more than one weight type that may affect the user-specific perception of distance. We study two query types on MCNs, namely, the MCN skyline and the MCN top-k query. In an upgradable network, a subset of the edges are amenable to weight reduction, at a cost (e.g., …
Recommendation Support For Multi-Attribute Databases, Jilian Zhang
Recommendation Support For Multi-Attribute Databases, Jilian Zhang
Dissertations and Theses Collection (Open Access)
This dissertation studies the subject of providing recommendation support for multi-attribute databases. Recommendation is an important and very useful information evaluation mechanism that explores a database of huge volume, and retrieves from it the interesting data items (tuples) for users based on their preferences.
Ranking-Based Approaches For Localizing Faults, Lucia Lucia
Ranking-Based Approaches For Localizing Faults, Lucia Lucia
Dissertations and Theses Collection (Open Access)
A fault is the root cause of program failures where a program behaves differently from the intended behavior. Finding or localizing faults is often laborious (especially so for complex programs), yet it is an important task in the software lifecycle. An automated technique that can accurately and quickly identify the faulty code is greatly needed to alleviate the costs of software debugging. Many fault localization techniques assume that faults are localizable, i.e., each fault manifests only in a single or a few lines of code that are close to one another. To verify this assumption, we study how faults spread …
On Predicting User Affiliations Using Social Features In Online Social Networks, Minh Thap Nguyen
On Predicting User Affiliations Using Social Features In Online Social Networks, Minh Thap Nguyen
Dissertations and Theses Collection (Open Access)
User profiling such as user affiliation prediction in online social network is a challenging task, with many important applications in targeted marketing and personalized recommendation. The research task here is to predict some user affiliation attributes that suggest user participation in different social groups.
Pricing Strategy For Cloud Computing Services, Jianhui Huang
Pricing Strategy For Cloud Computing Services, Jianhui Huang
Dissertations and Theses Collection (Open Access)
The cloud computing services market exhibits unique characteristics such as instant accessibility, fluctuating demand and supply, and interruptible service provision. Various pricing mechanisms exist in current industry practice. None of these pricing mechanisms, however, is comprehensive enough to capture all these features in a way that allows the vendors to optimize resource allocation. This dissertation identifies key factors related to cloud computing pricing, and examines their interplays. This research uses multiple approaches, including a market survey, game theory modeling, simulation, lab experiments and econometric modeling, to analyze the pricing strategy of cloud services vendors. A field study of a representative …
Social Correlation In Latent Spaces For Complex Networks, Freddy Chong Tat Chua
Social Correlation In Latent Spaces For Complex Networks, Freddy Chong Tat Chua
Dissertations and Theses Collection (Open Access)
This dissertation addresses the subject of measuring social correlation among users within a complex social network. Social correlation is closely related to the measurement of social influence in social sciences. While social influence focuses on the existence of causal influence among users, we take a computational approach to measure correlation strength among users based on their shared interactions. We call this social correlation. To formally model social correlation, we propose a framework which contains two major parts. The first part is that of representing users behavior in a computationally efficient and accurate manner. For example, social media users perform many …
Virtualization-Based System Hardening Against Untrusted Kernels, Yueqiang Cheng
Virtualization-Based System Hardening Against Untrusted Kernels, Yueqiang Cheng
Dissertations and Theses Collection (Open Access)
Applications are integral to our daily lives to help us processing sensitive I/O data, such as individual passwords and camera streams, and private application data, such as financial information and medical reports. However, applications and sensitive data all surfer from the attacks from kernel rootkits in the traditional architecture, where the commodity OS that is supposed to be the secure foothold of the system is routinely compromised due to the large code base and the broad attack surface. Fortunately, the virtualization technology has significantly reshaped the landscape of the modern computer system, and provides a variety of new opportunities for …
Using User Saliency For Effective Oled Display Power Management, Kiat Wee Tan
Using User Saliency For Effective Oled Display Power Management, Kiat Wee Tan
Dissertations and Theses Collection (Open Access)
The display component on mobile device has always been a main power drain compared to networking and processing components. Even with advancement in display technology such as Organic Light Emitting Diodes (OLED) displays to help reduce this cost, the increasing display size and new mobile usage paradigm (App stores and Social networking), the display still remains as the largest power drain component. In this thesis, we describe an approach to reduce power consumption on OLED display by dimming areas of the screen that the user might not be interested in. We determine these uninteresting areas by reviewing large number applications …
Generic Instance-Specific Automated Parameter Tuning Framework, Linda Lindawati
Generic Instance-Specific Automated Parameter Tuning Framework, Linda Lindawati
Dissertations and Theses Collection (Open Access)
Meta-heuristic algorithms play an important role in solving combinatorial optimization problems (COP) in many practical applications. The caveat is that the performance of these meta-heuristic algorithms is highly dependent on their parameter configuration which controls the algorithm behaviour. Selecting the best parameter configuration is often a difficult, tedious and unsatisfying task. This thesis studies the problem of automating the selection of good parameter configurations. Existing approaches to address the challenges of parameter configuration can be classified into one-size-fits-all and instance-specific approaches. One-size-fits-all approaches focus on finding a single best parameter configuration for a set of problem instances, while instance-specific approaches …