Intelligent Placement of Apps and Users on Qlik Analytics Engines

业务分析工具提供直观、可视化的分析解决方案，使组织能够发现存在于数据中的故事。商业智能解决方案越来越多地应用于各种行业，包括医疗保健、金融服务、零售和生命科学。在这种情况下，需要一种机器学习解决方案，可用于优化基于包容性系统的分析应用程序的资源使用。在寻找最佳智能分析应用程序放置解决方案时，需要考虑几个因素。首先，应用程序在内存中的行为和CPU使用是由许多因素决定的，比如包含的数据量、数据的基数、操作的复杂性（例如聚合或广泛排序）、数据模型的性质，以及数据源的大小和位置。此外，不同类型用户的使用模式可能有很大差异。例如，按钮式知识工作者的使用可能比数据科学家的资源密集程度要低得多，数据科学家通常会执行高级的、特别的分析。该项目关注机器智能算法的发展，通过构建自适应解决方案来极大地改善基于云平台的分析应用程序的放置策略，以解决这些问题。

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.