Graph-Based Spatial-Temporal Cluster Evolution: Representation, Analysis, and Implementation

Spatial-temporal data are information about real-world entities that exist in a location, the spatial dimension, and during a period of time, the temporal dimension. These real-world entities, such as vehicles, people, or parcels and called spatial-temporal objects, may move, group, and continue the movement together, forming clusters. Although there have been significant research efforts to understand clusters, there is a lack of research that provides methods and software tools to support the representation, analysis, and implementation of graph-based spatial-temporal cluster evolution. Understanding this evolution is critical for dealing with spatial-temporal problems encountered in domains, such as service supply and demand, supply chain management, traffic and travel flows, human mobility, and city planning. This thesis presents an approach to graph-based cluster evolution and its representation, analysis, and implementation. The proposed solution introduces a representation of the structure of a spatial-temporal cluster with the identification of the cluster at several timestamps and linkages, and a representation of 14 spatial-temporal relationships clusters have during their existence. The proposed solution also introduces a graph representation of cluster evolution with nodes acting as clusters and edges as relationships. This solution provides analysis methods for the structure of spatial-temporal clusters that monitor the cluster changes in both location and size over time, and analysis methods for the spatial-temporal cluster relationships the clusters have during existence that calculate the frequency or density of such relationships in specific locations. The solution also provides analysis methods for a graph-based representation of spatial-temporal cluster evolution including integrated results that examine spatial-temporal clusters and their connections, and can provide, for example, aggregated results at a location or time of the day, identify ever-increasing or ever-decreasing regions, growth or decay rates, and measure the similarity between the evolution of two clusters. The approach also provides an implementation of the proposed representation and analysis methods. The effectiveness of the approach is evaluated through four case studies using different spatial-temporal datasets to show the results that can be produced, which include, exploratory analyses and specific analyses on ever-increasing and ever-decreasing regions, similarity values, and the movements the clusters represent. Overall, the proposed approach advances research in the spatial-temporal domain by providing novel representation and analysis methods as well as implementation tools that can improve the understanding about how clusters evolve in space and time. Such results can lead to many advantages such as higher income, reduced costs, and better transportation services, as well as the discovery of trends in cluster movement and improved decision-making processes in city planning

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc