Parallel Query Processing for Cloud Computing Environments - Momani, Zaid (Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, 2018)
Massive datasets of unprecedented sizes are emerging in diverse fields. These datasets require tools to be effectively analyzed in order to extract value. Massively parallel tools have emerged to address these challenges. Most importantly, core emphasis must be focused on enabling tasks to be parallelized to the optimal. Thus the aim of this thesis is to provide a method of answering queries in a single round of MapReduce. With special focus on input, that is, binary relations and ”fat” relations. A multiway join algorithm is implemented on the two types of inputs and comparisons are made, where we find that "fat" relations achieve better process time due to the nature of these datasets. In the case of "fat" relations it is studied in the context of data integration, that is, an application is proposed to crosschecking multiple data sources with conflicting information. The case of binary relation is also studied with regard to RDF datasets, which is a significantly important application in the realm of management of semantic web data, and achieve significant increase in the speed of query processing.