Grant: Discovery Grants Program - Individual

The rising data volumes, underscored by the term Big Data, and the advent of complex data-driven applications provide the impetus for sophisticated analytics with interactive response times. However, traditional database management system (DBMS) is unable to meet the challenges of advanced analytics, particularly, when multi-dimensional data such as spatio-temporal data is involved. This research proposal addresses some of the challenges associated with scalable data processing, in light of the emergence of novel applications, continuous upgrading of existing software and services and ever-changing landscape of modern hardware. One of the objectives of this research will be to advance the state-of-the-art in terms of database query processing capabilities. We will explore how to leverage adaptive Just-In-Time (JIT) compilation techniques for efficient main memory query processing. The real value of data comes from its potential to help companies improve operations and make faster, more intelligent decisions. It is important to improve the productivity of end-users and reduce the cost of IT operations. To that end, aggregation queries, involving aggregate functions, can offer summarized information that can be used to obtain actionable insights from data. However, it is impractical to precompute enough aggregates for interactive query latency. Therefore, in our research we plan to explore how to support online aggregation over arbitrary polygonal regions that can provide approximate answers with an estimated error. With the recent technological advances, large volumes of three dimensional (3D) geospatial data is being collected. While this is poised to drive technologies such as Digital Twins, autonomous driving and immersive virtual geo-environments (or Metaverse), existing 2D spatial query processing techniques are not suitable for complex 3D objects. In this research we will develop novel techniques for fast processing of 3D spatial queries for emerging applications. 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 ICT, energy, transportation to agriculture and emergency response services. Moreover, this research with facilitate HQP training in Big Data technologies that are vital for Canadian IT industry. In addition to promoting job growth, the research will promote the generation of intellectual property and help strengthen Canada's global technology leadership.