Large-scale data analysis lies in the core of modern enterprises and scientific research. With the emergence of cloud computing, the use of an analytical query processing infrastructure can be directly associated with monetary cost. MapReduce has been a popular framework in the context of cloud computing, designed to serve long-running queries (jobs) which can be processed in batch mode. Taking into account that different jobs often perform similar work, there are many opportunities for sharing. In principle, sharing similar work reduces the overall amount of work, which can lead to reducing monetary charges for utilizing the processing infrastructure. In this article we present a sharing framework tailored to MapReduce, namely, <tt>MRShare</tt>. Our framework, <tt>MRShare</tt>, transforms a batch of queries into a new batch that will be executed more efficiently, by merging jobs into groups and evaluating each group as a single query. Based on our cost model for MapReduce, we define an optimization problem and we provide a solution that derives the optimal grouping of queries. Given the query grouping, we merge jobs appropriately and submit them to MapReduce for processing. A key property of <tt>MRShare</tt> is that it is independent of the MapReduce implementation. Experiments with our prototype, built on top of Hadoop, demonstrate the overall effectiveness of our approach. <tt>MRShare</tt> is primarily designed for handling I/O-intensive queries. However, with the development of high-level languages operating on top of MapReduce, user queries executed in this model become more complex and CPU intensive. Commonly, executed queries can be modeled as evaluating pipelines of CPU-expensive filters over the input stream. Examples of such filters include, but are not limited to, index probes, or certain types of joins. In this article we adapt some of the standard techniques for filter ordering used in relational and stream databases, propose their extensions, and implement them through <tt>MRAdaptiveFilter</tt>, an extension of <tt>MRShare</tt> for expensive filter ordering tailored to MapReduce, which allows one to handle both single- and batch-query execution modes. We present an experimental evaluation that demonstrates additional benefits of <tt>MRAdaptiveFilter</tt>, when executing CPU-intensive queries in <tt>MRShare</tt>.

We investigate the problem of refining SQL queries to satisfy cardinality constraints on the query result. This has applications to the many/few answers problems often faced by database users. We formalize the problem of query refinement and propose a framework to support it in a database system. We introduce an interactive model of refinement that incorporates user feedback to best capture user preferences. Our techniques are designed to handle queries having range and equality predicates on numerical and categorical attributes. We present an experimental evaluation of our framework implemented in an open source data manager and demonstrate the feasibility and practical utility of our approach.

Tools for generating test queries for databases do not explicitly take into account the actual data in the database. As a consequence, such tools cannot guarantee suitable coverage of test cases commonly required for database testing. In this paper, we investigate the problem of generating queries that satisfy cardinality constraints on intermediate subexpressions when executed on a given test database. Such queries are required to test the performance of a database system under different operating conditionsWe formally analyze this problem, quantify its difficulty and follow up this analysis with a description of a practical algorithm which utilizes sampling and space pruning techniques to quickly generate test queries that have desired properties. We present the results of an experimental evaluation of our approach as implemented in an open source data manager, demonstrating the utility of our proposal.

Medical education in the twentieth century was largely influenced by the Flexner Report, with significant proportions of instruction dedicated to the molecular underpinnings of the pathologic pathways and minimal mention of the socio-ecological determinants of health. When examining the predominant diseases of the twenty first century landscape, widening health disparities, and significant changes in the United States healthcare system, it is imperative to view wellness and sickness in a broader public health context rather than a singular focus of the biomedical model. While undergraduate opportunities to study public health are on the rise in the United States, there is a parallel urgency for medical curricula to recognize the importance of the complex interrelated socio-ecological root causes of health, well-being, and illness. In order to reduce the risk of non-communicable diseases and increase health equity, it is necessary for medical education to integrate core public health knowledge and competencies. Contemporary health challenges require a public health approach, in addition to clinical skills, for physicians to provide equitable care. The COVID-19 pandemic further underscores the necessity to mitigate the effects of socio-ecological determinants of health. Seven key recommendations are presented from a training to practice timeline emphasizing the important linkages between medical education, socio-ecological influences on health, and public health. As the health challenges in society and communities shift, so too must training of future physicians. There is a need and an opportunity for medicine and public health to address the shared health challenges of our global society.

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