The KDD-CHASER project was launched in February 2018 to investigate the potential of a new concept where people who have collected data about themselves can analyse it in collaboration with experts. The data collection method may be, for example, a wearable fitness tracker, which are growing in popularity and provide the user with a wealth of information about their physical activity and sleep. In theory, the user could extract additional knowledge from the data using data analysis tools, but in practice, the average user does not have the required expertise. The central idea of KDD-CHASER was to study how such people could be brought together with people who do have the expertise and are willing to help.
Analysing personal data to discover useful knowledge about the individuals concerned is usually viewed as something done by corporations with access to large quantities of customer data, such as Google or Facebook. Thus, when exploitation of personal data is viewed from the perspective of the individual, it is typically seen as something that needs to be regulated in order to ensure that corporations do not abuse the power that comes with the possession of data about people. The perspective that this data could also be something that the people themselves exploit for their own personal benefit tends to get overlooked; the significance of the collaboration concept studied in KDD-CHASER is that it would enable individuals to achieve this by working together with other individuals instead of handing control of their data over to a company.
The overall objectives of KDD-CHASER were to build a model of the process of collaborative data analysis, to develop a software platform to support this type of collaboration, and to demonstrate the viability of the process model and the software platform by running a trial. The successful execution of the trial shows that the collaboration process is feasible and that the software platform can be used to support it in a real-world environment, although the usability, stability and performance of the software still require substantial improvement. Furthermore, the results of a survey conducted among the participants of the trial suggest that there is interest in this type of collaboration and that many people could gain useful information about themselves through collaborative analysis of their personal data.
