Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Business analytics tools provide intuitive, visual analytics solutions to allow organizations to discover the storyx000D
that lives within their data. Increasingly, business intelligence solutions are utilized across a variety ofx000D
industries, including, healthcare, financial services, retail and life sciences. In this setting, there is a need forx000D
machine learning solutions that may be used to optimize the resource-usage of analytics applications inx000D
cloud-based systems. There are several factors which need to be taken into consideration when aiming to findx000D
the optimal intelligent analytics application placement solutions. Firstly, the applications' behaviours inx000D
memory, and the CPU usages, are determined by a number of factors such as the amount of data included, thex000D
cardinality of the data, the complexity of the operations (e.g. aggregation or extensive sorting), the nature of thex000D
data model, as well as the sizes and locations of the data sources. In addition, the usage patterns of differentx000D
types of users may vary considerably. For instance, push-button knowledge workers' usages may be far lessx000D
resource-intensive than that of data scientists, who would typically perform advanced, ad hoc analytics. Thisx000D
project concerns the development of machine intelligence algorithms to address these issues, by buildingx000D
adaptive solutions to greatly improve placement policies of analytics applications across cloud-based platforms.