Distributed spatial query processing and optimization

x, 76 leaves ; 29 cmApplications exist today that require the management of distributed spatial data. Since spatial data is more complex than non-spatial data, performing queries on it requires more local processing (i.e. CPU and I/O) time. Also, due to geographical distribution, data transmission costs must be considered. To reduce these costs, one can employ a distributed spatial semijoin as it eliminates unnecessary objects before their transmission to other sites and the query site. Most existing work propose different representations of the distributed spatial semijoin between two sites only, with very few works exploring its use for processing a query involving more than two sites. In this thesis, we propose both new approaches for representing the spatial semijoin in a distributed setting, and their use for processing a distributed query consisting of any number of sites. Two strategies are proposed for compactly representing the spatial semijoin that reduce both the data transmission and local processing (CPU+I/O) costs when applied in a distributed spatial query. A Global Encompassing Minimum Bounding Rectangle (GEMBR) is utilized, which is partitioned, mapped and applied in two different ways to approximate the objects in a spatial joining attribute. The first is partition indices, while the second is a bit array representation. Then each spatial semijoin is applied in a multi-site distributed spatial query processing strategy. In addition, the two-site spatial semijoin is extended to handle multiple sites so that we have a benchmark strategy for comparison purposes. We have tested the query processing algorithms for four sites, which are a part of an actual working distributed system. The algorithms are compared with respect to data transmission cost, CPU time, I/O time and false positive results. The algorithms are superior in many cases at optimizing the above criteria. The bit array representation, which is called Bloom Filter Based Spatial Semijoin (BFSJ), is evaluated with respect to different filter factors and found that the optimized algorithms perform significantly better than the Distributed Na¨ıve Spatial Semijoin strategy when synthetic data was used. Also the Partition and Mapping Based Spatial Semijoin (PMSJ) is 1.38 times faster than BFSJ with respect to processing cost while the BFSJ has a tranmission cost gain of 1.12 over PMSJ. Both algorithms are 18 times faster and have six times less transmission cost than Distributed Na¨ıve Spatial Semijoin (NSPJ). Finally, it is also observed that with the increase of hash functions and filter factor the false positive percentage increases

Health and Politics: Analyzing the Government of Alberta’s COVID-19 Communications

A multi--part paper for the panel on June 1st. We discuss a project to gather discourse on COVID-19 from press briefings, Twitter and other sources. We discuss how we have analyzed a first 6-month span of the gathered discourse and present some preliminary findings

A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence

The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created high expectations for industrial, commercial, and social value. Second, the emerging and growing concern for creating ethical and trusted AI systems, including compliance with regulatory principles to ensure transparency and trust. These two threads have created a kind of “perfect storm” of research activity, all motivated to create and deliver any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science and which provides a basis for the development of a framework for transparent XAI. We identify four foundational components, including the requirements for (1) explicit explanation knowledge representation, (2) delivery of alternative explanations, (3) adjusting explanations based on knowledge of the explainee, and (4) exploiting the advantage of interactive explanation. With those four components in mind, we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a basic history of XAI ideas, and then synthesize those ideas into a simple framework that can guide the design of AI systems that require XAI