Systems and Algorithms for Easy-to-use Federated Data Science

数据科学研究和技术对认知神经科学、个性化营销和政府欺诈等应用中的数据洞察产生了重大影响。在这些应用中，需要在多个科学学科和跨地理边界的各种组织维护的数据集上实现数据驱动的发现。这些应用程序需要创新的方式来交换、共享、处理和可视化大量数据。因此，数据科学家需要访问内部和外部数据源，这些数据源积累了大量地理分布和分散的数据集。定期访问这些数据集，以执行通常以管道方式组织的不同数据集成、提取和分析任务。正如最近在《建设创新者之国》中所报道的那样，加拿大投资5.7亿美元，以提高研究人员获得先进计算和大数据资源的机会。这项资助申请符合这一愿景，即使加拿大保持在技术需求最高的领域之一的最前沿，即数据科学。

Data science research and technology drive a significant impact on creating insights from data in applications, such as cognitive neuroscience, personalized marketing, and government fraud. In these applications, there is a need to enable data-driven discoveries on multiple scientific disciplines, and datasets maintained by various organizations across geographical boundaries. These applications demand innovative ways to exchange, share, process, and visualize huge amounts of data. Consequently, data scientists need access to internal and external data sources that accumulate vast amounts of geo-distributed and decentralized datasets. These datasets are accessed on a regular basis to perform different data integration, extraction, and analytics tasks that are usually organized in a pipeline fashion. As reported recently in "Building a Nation of Innovators", Canada invests $570 million to enhance researchers' access to advanced computing and big data resources. This grant application is aligned with this vision to keep Canada at the forefront of one of the highest-demand sectors in technology, i.e., data science. My research program has a long-term vision to develop an easy-to-use federated data science platform, including advanced algorithms to support data science applications on geo-distributed datasets. This grant application proposes four short-term objectives: (a) natural language interfaces for data science platforms, (b) data science versioning control to enhance collaboration while maintaining accuracy and fairness, (c) a federated engine for managing and optimizing data science workloads over geo-distributed datasets, and (d) technology transfer to apply our findings to neuroscience and integrate our systems into existing data science platforms. This grant application focuses on geo-distributed datasets in the form of knowledge graphs and linked web data. We will leverage cloud resources and parallel architectures to provide a collaborative workspace, in which data scientists can work together and scale up their data science workloads. This grant application aims to advance the state of the art in a highly competitive area that bridges the gap between data systems and data-driven intelligence on extracting insight from a vast pool of geo-distributed datasets. The proposed research and its main outcomes will support Canada in competing in the global innovation race. Employment in the data science market is rapidly growing. This application supports training that provides highly qualified personnel in this area to avoid anticipated shortage in the Canadian labour market. This application plans to train several students in the theoretical and practical aspects of data science, and help various Canadian startups to create business value from their data. The whole spectrum of scientific disciplines includes promising real applications for our research program. This will help the students to demonstrate their systems using real use-cases.