Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Liu, Huan
Description
Recent efforts in data cleaning have focused mostly on problems like data deduplication, record matching, and data standardization; few of these focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like CFDs (which have to be provided by domain experts, or learned from a clean sample of the database). In this thesis, I provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. I thus avoid the necessity for a domain expert or master data. I also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. A Map-Reduce architecture to perform this computation in a distributed manner is also shown. I evaluate these methods over both synthetic and real data.
ContributorsDe, Sushovan (Author) / Kambhampati, Subbarao (Thesis advisor) / Chen, Yi (Committee member) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation considers the question of how convenient access to copious networked observational data impacts our ability to learn causal knowledge. It investigates in what ways learning causality from such data is different from -- or the same as -- the traditional causal inference which often deals with small scale i.i.d. data collected from randomized controlled trials? For example, how can we exploit network information for a series of tasks in the area of learning causality? To answer this question, the dissertation is written toward developing a suite of novel causal learning algorithms that offer actionable insights for a series of causal inference tasks with networked observational data. The work aims to benefit real-world decision-making across a variety of highly influential applications. In the first part of this dissertation, it investigates the task of inferring individual-level causal effects from networked observational data. First, it presents a representation balancing-based framework for handling the influence of hidden confounders to achieve accurate estimates of causal effects. Second, it extends the framework with an adversarial learning approach to properly combine two types of existing heuristics: representation balancing and treatment prediction. The second part of the dissertation describes a framework for counterfactual evaluation of treatment assignment policies with networked observational data. A novel framework that captures patterns of hidden confounders is developed to provide more informative input for downstream counterfactual evaluation methods. The third part presents a framework for debiasing two-dimensional grid-based e-commerce search with observational search log data where there is an implicit network connecting neighboring products in a search result page. A novel inverse propensity scoring framework that models user behavior patterns for two-dimensional display in e-commerce websites is developed, which aims to optimize online performance of ranking algorithms with offline log data.
ContributorsGuo, Ruocheng (Author) / Liu, Huan (Thesis advisor) / Candan, K. Selcuk (Committee member) / Xue, Guoliang (Committee member) / Kiciman, Emre (Committee member) / Arizona State University (Publisher)
Created2021