A framework for information integration using ontological foundations

With the increasing amount of data, ability to integrate information has always been a competitive advantage in information management. Semantic heterogeneity reconciliation is an important challenge of many information interoperability applications such as data exchange and data integration. In spite of a large amount of research in this area, the lack of theoretical foundations behind semantic heterogeneity reconciliation techniques has resulted in many ad-hoc approaches. In this thesis, I address this issue by providing ontological foundations for semantic heterogeneity reconciliation in information integration. In particular, I investigate fundamental semantic relations between properties from an ontological point of view and show how one of the basic and natural relations between properties – inferring implicit properties from existing properties – can be used to enhance information integration. These ontological foundations have been exploited in four aspects of information integration. First, I propose novel algorithms for semantic enrichment of schema mappings. Second, using correspondences between similar properties at different levels of abstraction, I propose a configurable data integration system, in which query rewriting techniques allows the tradeoff between accuracy and completeness in query answering. Third, to keep the semantics in data exchange, I propose an entity preserving data exchange approach that reflects source entities in the target independent of classification of entities. Finally, to improve the efficiency of the data exchange approach proposed in this thesis, I propose an extended model of the column-store model called sliced column store. Working prototypes of the techniques proposed in this thesis are implemented to show the feasibility of realizing these techniques. Experiments that have been performed using various datasets show the techniques proposed in this thesis outperform many existing techniques in terms of ability to handle semantic heterogeneities and performance of information exchange

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability

Theorizing Precarization and Racialization as Social Determinants of Health: A Case Study Investigating Work in Long-Term Residential Care

This thesis uses anti-racist and feminist political economy of health perspectives that intersect with immigrant status, in order to analyze the findings from a single-case study investigating the social determinants of health and work precarization in a residential long-term care (LTC) facility in Toronto, Ontario. Throughout this dissertation, I use mixed methods case study to investigate social, political, and economic implications in the lives of health care workers. Observation, interview, and survey methods were utilized to investigate workers health in relation to the precarization of work. Specifically, I used the concept of precarization as a lens to track the ways in which work relations impact the other social determinants of health. The main areas of focus include the intersections of gender, work, and occupational health with race, immigrant status, and culture; the ways in which precarization affects employees in this specific health care sector; the implications of precarization in the health and wellbeing of workers and their families; the role of (un)paid care work and social support provided by family members; and the exercise of strength, resilience, resistance, agency, and coping strategies. Broadly, I will argue that precarization in LTC is an increasingly experienced phenomenon, and that various levels of precarization are experienced by particular workers who are women, racialized persons, and immigrants. This study contributes to our understanding of racialization as a social determinant of health, and analyzes the health impacts of workplace inequality through the lens of precarization. The study makes the case for closer attention to racism and precarity both on and as social determinants of health

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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