A Smart Big Data Analytics and Knowledge Management Framework

智能大数据分析和知识管理框架

• 批准号: RGPIN-2018-05550
• 负责人: Zulkernine, Farhana
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750632
• 关键词: Smart; Big; Data; Analytics; Knowledge

According to an IBM analysis, we create 2.5 quintillion bytes of data every day. We face an enormous challenge in analyzing this large volume of many different types of data being generated at a high speed, and managing the knowledge extracted from data analytics. Currently we need experts for recognizing the data, and thereby, selecting the right analytic tools, workflows, and storage systems. Based on cognitive theories, humans create and link profiles of data after birth, which builds layers of knowledge and enables them to learn and query the knowledge for decision making. We need a data analytic framework that can recognize the data like a human expert, create data profiles by using the right analytic tools and workflows, and store and link this knowledge to be able to retrieve it efficiently for goal based decision making. For example, when deciding on a patient's treatment course, a physician is required to consult the patient's medical records containing structured data such as height and weight, unstructured text notes, relevant MRI-image, and refer to a disease database to investigate diseases that have similar symptoms. We propose creating a smart big data analytics and knowledge management framework that will apply machine learning models and a variety of data analytics tools to recognize the data type from structure and semantic content, and thereby, apply the right analytic workflow to extract knowledge to create and link data profiles. The knowledge management part of the framework will store the extracted knowledge into distributed and hybrid knowledge repositories and create a multi-level graph indexing structure to efficiently retrieve the right knowledge pieces for decision support based on goal, knowledge association, context and situation. Due to the necessity of ubiquitous access and elastic resources for big data, we will implement the framework on cloud resources and make it accessible as a data analytic and knowledge service on the cloud. The prototype of the framework will be validated for three interdisciplinary use case scenarios: 1) predicting market status of the clients of our industry partner using news and financial data analytics; 2) defining and diagnosing PTSD (Post Traumatic Stress Disorder) in primary care electronic medical record (EMR) data, and 3) analyzing heart rate variability from streaming monitoring data from the intensive care unit at the Kingston General Hospital. The proposed research program will train 3 PhD, 3 MSc and 2 undergraduate students in using cutting edge big data analytics systems and developing data science skills that are highly sought after in the global market today. As evident from the use cases, the research will directly impact the technology industry and health care, build the backbone for the future intelligent decision support systems, and sustain Canada's leadership in building a smart digital world.

根据IBM的分析，我们每天创建2.5兆字节的数据。我们在分析高速生成的大量不同类型的数据以及管理从数据分析中提取的知识方面面临着巨大的挑战。目前，我们需要专家来识别数据，从而选择正确的分析工具、工作流和存储系统。基于认知理论，人类在出生后创建和链接数据的配置文件，这构建了知识层，使他们能够学习和查询决策知识。我们需要一个数据分析框架，它可以像人类专家一样识别数据，通过使用正确的分析工具和工作流创建数据配置文件，并存储和链接这些知识，以便能够有效地检索它，以实现基于目标的决策。例如，在决定患者的治疗过程时，医生需要查阅患者的医疗记录，其中包含结构化数据，如身高和体重，非结构化文本注释，相关MRI图像，并参考疾病数据库以调查具有类似症状的疾病。 我们建议创建一个智能大数据分析和知识管理框架，该框架将应用机器学习模型和各种数据分析工具来识别结构和语义内容的数据类型，从而应用正确的分析工作流程来提取知识以创建和链接数据配置文件。该框架的知识管理部分将提取的知识存储到分布式和混合式知识库中，并创建一个多级图索引结构，以有效地检索正确的知识片段，用于基于目标，知识关联，上下文和情况的决策支持。由于大数据无处不在的访问和弹性资源的必要性，我们将在云资源上实现框架，并将其作为云上的数据分析和知识服务。该框架的原型将在三个跨学科的用例场景中进行验证：1）使用新闻和金融数据分析来预测我们行业合作伙伴客户的市场状况; 2）PTSD的定义和诊断（创伤后应激障碍）在初级保健电子病历（EMR）数据，以及3）从来自金斯顿综合医院的重症监护室的流监测数据分析心率变异性。 拟议的研究计划将培养3名博士，3名硕士和2名本科生使用尖端的大数据分析系统，并开发当今全球市场上备受追捧的数据科学技能。从用例中可以看出，该研究将直接影响技术行业和医疗保健，为未来的智能决策支持系统建立骨干，并保持加拿大在建设智能数字世界方面的领导地位。