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

大数据是一种变革性的技术趋势，它将改变企业运营、政府运作和人们工作的方式。 Twitter和Facebook等应用程序以及设备、移动电话、卫星图像、软件日志、相机和RFID读取器的激增推动了数据的爆炸性增长。传统的数据库技术不足以处理这种数据泛滥。有必要设计出全新的数据管理方法、工具和技术。本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。 诸如基于位置的服务（LBS）的许多时空应用的特征在于从众多GPS使能的设备接收的高频率的位置数据更新。传统的关系数据库无法支持LBS中典型的高更新和查询吞吐量。为了解决这个问题，我的研究将开发一个可扩展和高吞吐量的分布式主存数据存储，能够支持每秒数百万次的位置更新和数千次的查询。空间数据存储在地理信息系统（GIS）和卫星图像中，具有数据量大、种类多、处理复杂的特点。现有的数据库方法要么不支持并行空间查询处理，要么不能处理其多样性：矢量，栅格和微数据格式。作为解决方案，我正在为异构空间大数据构建并行数据库系统，大数据的真实的价值来自于它帮助企业改善运营，做出更快、更智能决策的潜力。提高软件开发人员的生产力并降低IT运营成本非常重要。为此，我的研究将对大数据处理框架的完整堆栈进行彻底的重新检查，以期开发一个具有直观编程模型的高效分析框架。从数据分析中获得的可操作见解在医疗保健服务中至关重要。当前电子病历（EMR）的数据管理方法在改善治疗决策和治疗方面存在不足。为了解决这个问题，本研究将开发一个用于医疗分析的语义图数据库。这个想法是为了提高检索医疗记录的能力，是“类似的”目前的情况下，因此它可以作为一个宝贵的资源，以提高决策的质量。这项研究具有强大的潜力，提供经济和社会效益，加拿大。 处理高容量、高速度和高复杂性数据的能力得到提高，将影响从LBS、能源、交通到农业和医疗保健的各个行业。此外，这项研究还为HQP提供了如何应对大数据挑战的培训。

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.