Intelligent Placement of Apps and Users on Qlik Analytics Engines

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Business analytics tools provide intuitive, visual analytics solutions to allow organizations to discover the storythat lives within their data. Increasingly, business intelligence solutions are utilized across a variety ofindustries, including, healthcare, financial services, retail and life sciences. In this setting, there is a need formachine learning solutions that may be used to optimize the resource-usage of analytics applications incloud-based systems. There are several factors which need to be taken into consideration when aiming to findthe optimal intelligent analytics application placement solutions. Firstly, the applications' behaviours inmemory, and the CPU usages, are determined by a number of factors such as the amount of data included, thecardinality of the data, the complexity of the operations (e.g. aggregation or extensive sorting), the nature of thedata model, as well as the sizes and locations of the data sources. In addition, the usage patterns of differenttypes of users may vary considerably. For instance, push-button knowledge workers' usages may be far lessresource-intensive than that of data scientists, who would typically perform advanced, ad hoc analytics. Thisproject concerns the development of machine intelligence algorithms to address these issues, by buildingadaptive solutions to greatly improve placement policies of analytics applications across cloud-based platforms.