Semantic Data Lake for Biomedical Research

利用由规模极大且不断增长的 持续产生的生物医学数据的数量、速度和种类是一个重大挑战。这个 开发和日益广泛地采用若干新技术，包括Next 世代基因测序、电子健康记录和临床试验系统以及研究数据 仓库意味着我们正处于数据生产的真正爆炸性增长之中。这又反过来 导致科学生产力的瓶颈转移到数据管理和 解读：迫切需要工具来帮助癌症研究人员组装、整合、 对这些大数据集进行转型和分析。在这个项目中，我们建议开发语义 用于生物医学研究的数据湖(SDL-BR)系统，这是一个集群计算软件环境， 支持快速数据获取、多方面数据建模、逻辑和语义查询以及数据 转型和智能资源发现。SDL-BR基于数据湖的概念，即 分布式存储，不对传入数据的结构做出任何假设，并且 延迟建模决策，直到使用数据。该项目增加了数据湖范例方法 用于语义数据建模、集成和查询，以及基于学习的资源发现 用户和数据资源之间的关系。

Capitalizing on the transformative opportunities afforded by the extremely large and ever-growing volume, velocity, and variety of biomedical data being continuously produced is a major challenge. The development and increasingly widespread adoption of several new technologies, including next generation genetic sequencing, electronic health records and clinical trials systems, and research data warehouses means that we are in the midst of a veritable explosion in data production. This in turn results in the migration of the bottleneck in scientific productivity into data management and interpretation: tools are urgently needed to assist cancer researchers in the assembly, integration, transformation, and analysis of these Big Data sets. In this project, we propose to develop the Semantic Data Lake for Biomedical Research (SDL-BR) system, a cluster-computing software environment that enables rapid data ingestion, multifaceted data modeling, logical and semantic querying and data transformation, and intelligent resource discovery. SDL-BR is based on the idea of a data lake, a distributed store that does not make any assumptions about the structure of incoming data, and that delays modeling decisions until data is to be used. This project adds to the data lake paradigm methods for semantic data modeling, integration, and querying, and for resource discovery based on learned relationships between users and data resources.