## 1 Introduction  
In the recent years the progress in technology and the increasing availability of fast connections has produced a migration of functionalities in Information Technology (IT) services, from static servers to distributed technologies. This phenomenon is commonly well known as _Cloud Computing_; the most exhaustive and official definition comes from the US National Institute of Standards and Technology (NIST) [1], which introduces all the fundamental concepts of the cloud systems, such as on-demand access to resources by the end user and offering services with minimal infrastructures and management effort.  
NIST definition points out that cloud computing includes data processing and data storage, both performed on remote servers.  
The arrival of cloud computing is also changing many core concepts in IT, defining new service models for distribution to final customers. Summarizing the definitions in [1]:  
* _Software as a Service (SaaS)_: the consumer can use various cloud devices to take advantage of a provider's application (web application) that is stored on a cloud infrastructure.
* _Platform as a Service (PaaS)_: business users can deploy and distribute their applications onto the cloud, taking advantage of the tools supported by the provider without having to manage the underlying infrastructure
* _Infrastructure as a Service (IaaS)_: in addition to all the functionalities of the PaaS model, the user can also control the operating system and the storage as well as select some network components (e.g., host firewalls).  
We can therefore understand the importance of cloud technology, primarily in business environment. The companies that choose to move their assets on the cloud will only take care of their core-business (e.g., software developing, for a software house). No more need for data management strategies (security, persistence, geographically scattered backups, etc.) nor hardware updating to guarantee adequate computing power and storage space.  
The end user, by accessing a SaaS from a common browser, benefits of all the software features without the need for an adequate hardware or installation/configuration steps.  
In research, often the set up of a proper work environment is a time consuming and costly activity. In order to guarantee enough storage space and computing power, new hardware has a short obsolescence time. In machine-learning and data-mining, the researchers often deal with High Performance Computing (HPC) and Big Data. These are the typical conditions where cloud technologies can offer the best advantages: large amounts of data and high computational power. Cloud architectures applied to these contests have been called Analytics-as-a-Service (AaaS) and have been largely treated in literature in the last years [2; 3; 4; 5; 6].  
In this article we will start describing some of the available systems and platforms for analytics and their specificities. Afterwards we will describe a case study where one of these tools has been tested to face a problem concerning a pathology called Hearth Failure (HF), very well described in literature. We used machine-learning to predict the presence of the disease, relying only on the Hearth Rate Variability (HRV) analysis.  
The following is a brief summary of the available analytics systems/platforms. We selected the top ten systems according to the rankings from [PERSON] [7], and basing on the review published by Butler Analytics [8].