Intelligent Placement of Apps and Users on Qlik Analytics Engines

Qlik Analytics Engine 上的应用程序和用户的智能放置

• 批准号: 520265-2017
• 负责人: Viktor, Herna
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640138
• 关键词: Intelligent; Placement; Apps; Users; Qlik

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.

业务分析工具提供直观的视觉分析解决方案，使组织能够发现其数据中的故事。越来越多的商业智能解决方案在各种各样的工业中都使用，包括医疗保健，金融服务，零售和生命科学。在这种情况下，有必要的官僚学习解决方案可以用来优化基于基于基的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解决方案时，需要考虑几个因素。首先，应用程序的行为inmemory和CPU用法取决于许多因素，例如包括数据量，数据的记录性，操作的复杂性（例如聚合或广泛的分类），Thedata模型的性质，以及数据源的大小和位置。此外，不同用户不同类型的使用模式可能会有很大差异。例如，按钮知识工作者的用法可能远低于数据科学家的使用量要小得多，数据科学家通常会执行高级的临时分析。 ThisProject涉及机器智能算法的开发来解决这些问题，通过建筑适应性解决方案，以极大地改善基于云平台的分析应用程序的放置策略。