High performance Big Data Systems for spatial, spatio-temporal and graph data management

用于空间、时空和图形数据管理的高性能大数据系统

• 批准号: RGPIN-2016-03787
• 负责人: Ray, Suprio
• 金额: $ 1.6万
• 依托单位: University of New Brunswick
• 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=750283
• 关键词: performance; Big; Data; Systems; spatial

Big Data is a transformative technological trend that is poised to change how businesses operate, governments function, and people work. The explosive growth of data is fueled by applications like Twitter and Facebook, and the proliferation of devices, mobiles phones, satellite imagery, software logs, cameras, and RFID readers. Traditional database techniques are inadequate to deal with this data deluge. It is necessary to devise fundamentally new approaches to data management, tools and techniques.This research proposal addresses the key challenges of Big Data: volume, velocity, variety and complexity. Many spatio-temporal applications, such as Location-Based Services (LBS), are characterized by high frequency of location data updates received from numerous GPS-enabled devices. Traditional relational databases are unable to sustain the high update and query throughput typical in LBS. To address this, my research will develop a scalable and high throughput distributed main-memory data store, capable of supporting millions of location updates and thousands of queries per second. The proposed system will exploit fast in-memory processing over high speed networks.Spatial data, stored in Geographic Information Systems (GIS) and satellite imagery, are characterized by volume, variety and complexity of processing. Existing database approaches either do not support parallel spatial query processing or cannot deal with its variety: vector, raster and microdata formats. As a solution, I am building a parallel database system for heterogeneous spatial Big Data.The real value of Big Data comes from its potential to help companies improve operations and make faster, more intelligent decisions. It is important to improve the productivity of software developers and reduce the cost of IT operations. To that end, my research will conduct a thorough re-examination of the complete stack of Big Data processing frameworks, with a view to develop an efficient analytics framework with an intuitive programming model.Actionable insights derived from data analytics can be critically important in healthcare services. Current data management approaches to Electronic Medical Records (EMR) fall short when it comes to improve therapeutic decision making and treatment. To address this issue, this research will develop a semantic graph database for healthcare analytics. The idea is to improve the ability to retrieve medical records that are “similar to” a present case and thus it can serve as a valuable resource to improve the quality of decision.This research has strong potential to provide economic and social benefits to Canada. The improved capability to handle data, with high volume, velocity and complexity, will impact various industries from LBS, energy, transportation to agriculture and healthcare. Moreover, this research with offer training to HQP in how to deal with the challenges of Big Data.

大数据是一种变革性的技术趋势，被毒死以改变企业的运作方式，政府运作和人员工作。 Twitter和Facebook等应用程序以及设备，手机手机，卫星图像，软件日志，相机和RFID读者的扩散促进了数据的爆炸性增长。传统的数据库技术不足以处理此数据洪水。有必要从根本上设计出用于数据管理，工具和技术的新方法。该研究提案解决了大数据的关键挑战：数量，速度，多样性和复杂性。许多时空应用程序（例如基于位置的服务（LB））的特征是从众多启用GPS的设备接收到的位置数据更新的高频。传统的关系数据库无法维持LBS中典型的典型更新和查询吞吐量。为了解决这个问题，我的研究将开发可扩展且高吞吐量分布的主内存数据存储，能够支持数百万个位置更新和每秒数千个查询。所提出的系统将利用高速网络上的快速内存处理。存储在地理信息系统（GIS）和卫星图像中的空间数据的特征是数量，多样性和复杂性处理。现有的数据库方法要么不支持并行的空间查询处理，要么无法处理其多样性：向量，栅格和微数据格式。作为解决方案，我正在建立一个用于异质空间大数据的并行数据库系统。重要的是提高软件开发人员的生产率并降低IT运营的成本。为此，我的研究将对完整的大数据处理框架进行彻底重新检查，以期通过直观的编程模型开发有效的分析框架。从数据分析中获得的可相互作用在医疗保健服务中至关重要。在改善治疗决策和治疗方面，当前的电子病历（EMR）的数据管理方法缺乏。为了解决这个问题，这项研究将开发用于医疗保健分析的语义图数据库。这个想法是提高检索“类似于”当前病例的病历的能力，因此可以作为提高决策质量的宝贵资源。这项研究具有为加拿大提供经济和社会福利的强大潜力。具有大量，速度和复杂性的数据的能力的提高将影响来自LB，能源，运输到农业和医疗保健的各个行业。此外，这项研究通过向HQP提供培训，以应对大数据的挑战。