Doctor of Philosophy

dissertationWith the steady increase in online shopping, more and more consumers are resorting to Product Search Engines and shopping sites such as Yahoo! Shopping, Google Product Search, and Bing Shopping as their first stop for purchasing goods online. These sites act as intermediaries between shoppers and merchants to drive user experience by enabling faceted search, comparison of products based on their specifications, and ranking of products based on their attributes. The success of these systems heavily relies on the variety and quality of the products that they present to users. In that sense, product catalogs are to online shopping what the Web index is to Web search. Therefore, comprehensive product catalogs are fundamental to the success of Product Search Engines. Given the large number of products and categories, and the speed at which they are released to the market, constructing and keeping catalogs up-to-date becomes a challenging task, calling for the need of automated techniques that do not rely on human intervention. The main goal of this dissertation is to automatically construct catalogs for product search engines. To achieve this goal, the following problems must be addressed by these search engines: (i) product synthesis-creation of product instances that conform with the catalog schema; (ii) product discovery- derivation of product instances for products whose schemata are not present in the catalog; (iii) schema synthesis- construction of schemata for new product categories. We propose an end-to-end framework that automates, to a great extent, these tasks. We present a detailed experimental evaluation using real data sets which shows that our framework is effective, scaling to a large number of products and categories, and resilient to noise that is inherent in Web data

Doctor of Philosophy

dissertationCorrelation is a powerful relationship measure used in many fields to estimate trends and make forecasts. When the data are complex, large, and high dimensional, correlation identification is challenging. Several visualization methods have been proposed to solve these problems, but they all have limitations in accuracy, speed, or scalability. In this dissertation, we propose a methodology that provides new visual designs that show details when possible and aggregates when necessary, along with robust interactive mechanisms that together enable quick identification and investigation of meaningful relationships in large and high-dimensional data. We propose four techniques using this methodology. Depending on data size and dimensionality, the most appropriate visualization technique can be provided to optimize the analysis performance. First, to improve correlation identification tasks between two dimensions, we propose a new correlation task-specific visualization method called correlation coordinate plot (CCP). CCP transforms data into a powerful coordinate system for estimating the direction and strength of correlations among dimensions. Next, we propose three visualization designs to optimize correlation identification tasks in large and multidimensional data. The first is snowflake visualization (Snowflake), a focus+context layout for exploring all pairwise correlations. The next proposed design is a new interactive design for representing and exploring data relationships in parallel coordinate plots (PCPs) for large data, called data scalable parallel coordinate plots (DSPCP). Finally, we propose a novel technique for storing and accessing the multiway dependencies through visualization (MultiDepViz). We evaluate these approaches by using various use cases, compare them to prior work, and generate user studies to demonstrate how our proposed approaches help users explore correlation in large data efficiently. Our results confirmed that CCP/Snowflake, DSPCP, and MultiDepViz methods outperform some current visualization techniques such as scatterplots (SCPs), PCPs, SCP matrix, Corrgram, Angular Histogram, and UntangleMap in both accuracy and timing. Finally, these approaches are applied in real-world applications such as a debugging tool, large-scale code performance data, and large-scale climate data

Multilingual Schema Matching for Wikipedia Infoboxes

Recent research has taken advantage of Wikipedia's multilingualism as a resource for cross-language information retrieval and machine translation, as well as proposed techniques for enriching its cross-language structure. The availability of documents in multiple languages also opens up new opportunities for querying structured Wikipedia content, and in particular, to enable answers that straddle different languages. As a step towards supporting such queries, in this paper, we propose a method for identifying mappings between attributes from infoboxes that come from pages in different languages. Our approach finds mappings in a completely automated fashion. Because it does not require training data, it is scalable: not only can it be used to find mappings between many language pairs, but it is also effective for languages that are under-represented and lack sufficient training samples. Another important benefit of our approach is that it does not depend on syntactic similarity between attribute names, and thus, it can be applied to language pairs that have distinct morphologies. We have performed an extensive experimental evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and English. The results show that not only does our approach obtain high precision and recall, but it also outperforms state-of-the-art techniques. We also present a case study which demonstrates that the multilingual mappings we derive lead to substantial improvements in answer quality and coverage for structured queries over Wikipedia content.Comment: VLDB201

Supply chain information visibility and its impact on decision-making : an integrated model in the pharmaceutical industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

Supply chain information visibility (SCIV) has been largely recognized as a key issue in pharmaceutical supply chain management. In recent years, there has been growing concern regarding the exponential growth and ubiquity of supply chain information as the result of the application of advanced technologies. Thus, the topic of visibility of information flow across a supply chain has attracted interest in both practice and academia. Despite the existence of considerable literature on SCIV, the concept is still under-theorized. The lack of a clear understanding of the characteristics of SCIV has made it difficult to evaluate the effectiveness of SCIV and, consequently, hinders the improvement of SCIV (McIntire, 2014). Second, recent research identifies the potential of SCIV for operational performance through supporting managerial decision-making but also points out challenges and risks. In addition, there is a dearth of behavioral empirical research on supply chain management topics with which to achieve an increase in theory-building research in the field. This research addresses these gaps in the literature and investigates how SCIV across the pharmaceutical supply chain is perceived by pharmaceutical supply chain practitioners who are involved in supply chain decision-making, and how the decision-makers make use of SCIV in their supply chain decision-making process. This study adopted an exploratory, and qualitative approach to address two research questions: “How do supply chain professionals perceive SCIV in the pharmaceutical supply chain?” and “How do supply chain professionals make informed supply chain decisions?” The constructivist grounded theory methodology was used to guide the data gathering and analysis. The data were mainly drawn from semi-structured interviews with supply chain practitioners in New Zealand-based pharmaceutical firms, working at different levels of the supply chain, including manufacturers and distributors. Based on the findings a theoretical model was developed, the Pharmaceutical Supply Chain Information-based Decision-Making Model. The model explains the behavioral supply chain decision-making process in the pharmaceutical supply chain, based on the existence of a given level of SCIV. The empirical findings suggest that SCIV is achieved both within and outside of the pharmaceutical firms and that human relational factors tend to be more beneficial than technological factors in developing SCIV. The importance of this finding is that it addresses a frequently asked question in recent literature about what constitutes SCIV and how to successfully build information visibility in a supply chain. Moreover, this research contributes to the behavioural supply chain management research literature by introducing a theoretical model of pharmaceutical supply chain information-based decision-making, which is grounded in the field data. The model offers significant theoretical insight into information-based decision-making in the pharmaceutical supply chain context based on empirical data, which has been largely overlooked in the supply chain management discipline. The empirical findings suggest that supply chain practitioners make information-based decisions in which they conduct an informative engaging mechanism with technological tools, with relevant stakeholders, and with themselves. Thus, the decision-making process involves extensive data analysis along with the crucial support of experience-based intuition and relevant stakeholders’ engagement. Another key contribution of this study is the identification of the constructive aspect of political behaviour in the supply chain decision-making process in which relevant stakeholders when invited to engage in the process tend to positively contribute and buy into the decision. Finally, this thesis provides significant practical implications and suggest directions for future research. Supply chain practitioners may benefit from the study by utilizing the study’s results to develop supply chain information visibility in their firms. In addition, the theoretical model of the information-based decision-making process explicates a useful step-by-step approach for supply chain practitioners to follow in making effective supply chain operational decisions. Recommendations for further research are provided, especially the recommendations for further studies that are crucially needed to assist firms to counter the pharmaceutical supply chain disruption risks caused by the Covid-19 pandemic

The institutional context influencing rural-urban migration choices and strategies for young married women and men in Vietnam

This report draws together secondary data and informed opinion relating to the wider context in which young married rural-urban migrants must craft strategies for managing their reproductive and family lives. In contrast to long standing patterns of male migration, the increasing numbers of migrants and the emergence of new forms of migration mean that young married women are increasingly moving for work too. The report outlines the wider situation in which these dynamics are occurring: the growing inequalities in the context of doi moi, the declining barrier that household registration poses to mobility, and the changing opportunities for work in the city. It also reviews changing gender relations in Vietnam with particular attention to changes in marriage and marital relations, in sexuality and fertility and in parenting. Finally it explores how changes in social entitlements in Vietnam may affect these migrants with special attention to maternal health, child health and children’s education. The report concludes that migrants with young families and new marriages face a plethora of barriers and opportunities that they must negotiate and that the strategies they formulate are dynamic and involve complex trade-offs

DeepPeep: A Form Search Engine

posterWe present DeepPeep (http://www.deeppeep.org), a new search engine specialized in Web forms. DeepPeep uses a scalable infrastructure for discovering, organizing and analyzing Web forms which serve as entry points to hidden-Web sites. DeepPeep provides an intuitive interface that allows users to explore and visualize large form collections. We presented the overall architecture of DeepPeep which can support both general and specific deep Web search; benefits not only casual users but also application builders. The system provides a scalable and automatic solution to deep Web search and can adapt to the dynamic evolution of deep Web which is growing fast and will play an important role in the future of search

Factors affecting corruption in the public sector: evidence from Vietnam

This research utilizes a structural equation modeling (SEM) technique to comprehensively examine the intricate interactions among various factors influencing corruption in Vietnam's public sector. The findings reveal that certain factors, including inadequate anti-corruption policies and enforcement, a lack of accountability and transparency in anti-corruption endeavors, and significant income disparities between public officials and anti-corruption measures, significantly and positively impact the cultural and social norms associated with anti-corruption. Additionally, insufficient cultural and social standards exert a notable and positive influence on the level of corruption in the public sector. The outcomes of this study provide valuable insights for developing effective policies and strategies that promote accountability, transparency, and good governance to combat corruption in Vietnam's public sector