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2020 put the entire world upside down in its call for racial equity and justice. During these unveiling times, minorities in America have come forward in protest of racial and ethnic stereotypes which Hollywood still plays into. This research paper capitalized on the removal and protest of these characters and dove more specifically into South Asian stereotypes which have been reinforced by television show and movie characters. The claim in these cries of disapproval for these stereotypes was that it caused internalized racism in people who were brought up watching these stereotypes forced upon them. As building a body of knowledge progressed, the unmade connection of stereotypes to internalized racism in formal research became apparent, and therefore the research paper intended to see if there was a correlation between South Asian stereotypes and internalized racism. In order to identify internalized racism among South Asian teenagers, an open-ended survey was used as a method, followed by a mixed analysis to identify self-stereotyping indicators which would denote internalized racism. The research in the end did find a staunch correlation between the South Asian stereotypes and internalized racism in today’s South Asian teenagers. While today’s teenagers cannot rid themselves of the stereotypes already imposed on them, future South Asian children might possibly have accurate and fair representation in subsequent years.

Modern big data systems run on cloud environments where resources are shared amongst several users and applications. As a result, declarative user queries in these environments need to be optimized and executed over resources that constantly change and are provisioned on demand for each job. This requires us to rethink traditional query optimizers designed for systems that run on dedicated resources. In this paper, we show evidence that the choice of query plans depends heavily on the available resources, and the current practice of choosing query plans before picking the resources could lead to significant performance loss in two popular big data systems, namely Hive and SparkSQL. Therefore, we make a case for Resource and Query Optimization (or RAQO), i.e., choosing both the query plan and the resource configuration at the same time. We describe rule-based RAQO and present alternate decisions trees to make resource-aware query planning in Hive and Spark. We further present costbased RAQO that integrates resource planning within a query planner, and show techniques to significantly reduce the resource planning overheads. We evaluate cost-based RAQO using stateof-the-art System R query planner as well as a recently proposed multi-objective query planner. Our evaluation on TPC-H and randomly generated schemas show that: (i) we can reduce the resource planning overhead by up to 16x, and (ii) RAQO can scale to schemas as large as 100 table joins as well as clusters as big as 100K containers with 100GB each.