## 2 Approach and Implementation
### User Interface and User Workflow  
Ensuring success of the platform requires strenuous detail applied to the user interface to allow seamless collaborative workflow allowing rapid and easy user adoption. The primary incentive into the development of the user interface was to provide a simple and easy to use interface with minimal start up time for new user bases. Figure 4 illustrates the user workflow for the typical use case scenario of generating new sessions and migrating existing session information. With the seamless integration of session creation with existing data source migration, rapid user adaption can be achieved.  
Figure 3: Visualisation of a typical user workflow and application for aggregation of inhomogeneous datasets in the context of system stack interaction.  
The primary objective was to provide a collaborative and highly socialised platform for continuous, distributed and seamless integration for research and high performance sport environments with multiple members. With focus on operator centric development, providing the ability for multiple operators at varying access levels to collaborate, process, index and export various supported datasets. Providing functionality for indexing and searching raw datasets, as well as collaborative data processing, forms a repository of existing work.  
Once an operator has been granted access to the system, either through self-registration or registration via a lab manager, the operator is presented with a list of sessions. The encapsulation of a session is considered an experiment, allowing an operator to register various data sources encapsulated as datasets for a created session (Figure 5a). An operator is provided with the means of registering additional metadata to both session information and specific datasets. The operator can associate any supported data source as a dataset, which is provided as a basis for further data processing and collaboration with other operators. The operator with administrative privileges of a dataset has the ability to search for operators and assign various access levels for a defined session.  
Key features for the platform were the ability to index and search inhomogeneous data structures from various data sources for a defined captured session. The primary method for indexing includes assigned tags to various components of a session. This includes the ability for operators to assign tags to sessions, datasets and various index regions of time-series data, referred to as events. Searchable indexes into specific areas of time-series data allow operators to store information for later processing, allowing collaborative data analysis with multiple operators (Figure 5b). Any operator assigned to a session has the ability to export datasets in various formats, including archived versions of the original uploaded file, and homogenously structured data conformed to the previous discussed structure in a variety of languages (Figure 5c) in addition to universal standardised formats such as JavaScript Object Notation (JSON) for currently non-supported languages.  
A key requirement of the system is the migration process of the users for importing existing datasets to the new collaborative cloud based system. Major challenges for the design and implementation for the import functionality was the incentive of users to fill out information in a convenient and efficient manner. This was resolved by the segmentation of information required through an \"import wizard\", allowing users to stage multiple datasets efficiently through an iterative process.  
Figure 4: Illustration of a typical user workflow and application for aggregation of inhomogeneous datasets.  
Figure 5: _Cont._  
Figure 5: (**a**) Operator uploading several data sources for a newly created session; (**b**) operator session with six data sources shared with multiple operators; and (**c**) detailed dataset information with time-series dataset rendering.