This paper presents a joint effort between a group of computer scientists and bioinformaticians to take an important step towards a general big data platform for genome analysis pipelines. The key goals of this study are to develop a thorough understanding of the strengths and limitations of big data technology for genomic data analysis, and to identify the key questions that the research community could address to realize the vision of personalized genomic medicine. Our platform, called Gesall, is based on the new "Wrapper Technology" that supports existing genomic data analysis programs in their native forms, without having to rewrite them. To do so, our system provides several layers of software, including a new Genome Data Parallel Toolkit (GDPT), which can be used to "wrap" existing data analysis programs. This platform offers a concrete context for evaluating big data technology for genomics: we report on super-linear speedup and sublinear speedup for various tasks, as well as the reasons why a parallel program could produce different results from those of a serial program. These results lead to key research questions that require a synergy between genomics scientists and computer scientists to find solutions.

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants' experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools. We strive to ensure that our project is patient-centered; nurtures collaboration among all stakeholders; develops scalable solutions facilitating growth and connections; chooses simple, elegant solutions wherever possible; and explores ways to streamline the administrative and regulatory approval process across sites.

We see a trend toward extending object-oriented languages in the direction of databases, and, at the same time, toward extending database systems with object-oriented ideas. On the surface, these two activities seem to be moving in a consistent direction. However, at a deeper level, we see difficulties that may inhibit their ending up at the same point. We feel that many of these difficulties are a result of the underlying assumptions that are inherent in the fields of programming language and database systems research. Many of these assumptions are historical and contribute to a set of cultural biases that often prevent the two communities from interacting as effectively as possible.

In recent years, many high-level synchronization constructs have been proposed. Each claims to satisfy criteria such as expressive power, ease of use, and modifiability. Because these terms are so imprecise, we have no good methods for evaluating how well these mechanisms actually meet such requirements. This paper presents a methodology for performing such an evaluation. Synchronization problems are categorized according to some basic properties, and this categorization is used in formulating more precise definitions of the criteria mentioned, and in devising techniques for assessing how well those criteria are met.