Frontiers of Business Intelligence and Analytics
To understand definitions regarding the taxonomy of BI, read this paper, where an example of the methodology in the research process is used. It also discusses how the taxonomy for BI and analysis was developed, how it is applied, and an analysis of the current status with predicted development for the next wave or 3.0 of BI, as well as potential gaps. A clear diagram of the taxonomy development process is shown in Figure 6. While a picture is worth a thousand words, sometimes you must explain complex processes narratively.
Results
Data privacy
Since big data research often implies data privacy issues and promises to be a compelling research field, we finally analyzed all research papers in scope regarding its explicit naming of data privacy risks and the presentation for a solution. We are aware that the regarded papers commonly have no explicit focus on data privacy. However, against the background of the extensive public discourse on data privacy leaks, it is surprising that 52 out of 75 papers did not even mention privacy risks that possibly arise when the research artifact at hand is applied. We further analyzed all papers regarding its relevance of data privacy. In total, we classify 46 research outcomes as relevant in terms of data privacy regulations. Thirteen papers mention data privacy risks that come along with applying the current research artifact. In the following, we focus on introducing those eleven papers that explicitly name a solution for data privacy.
The presented solutions for addressing data privacy risks contain procedures to transfer aggregated and non-personal data. For instance, Gkatziaki et al. analyzes social media broadcasts to divide a city into smaller subspaces and identifying urban centers. The introduced system solely transfers aggregated data to prevent personalization.
Other research papers contain a data anonymization approach or an explicit spatial collection of data that requires a minimum of user permissions to protect personal data. Mihale-Wilson et al. conduct a conjoint analysis to analyze data privacy concerns of a ubiquitous personal assistant, such as a speech assistant. Provost et al. develop a system that analyzes the similarities of mobile devices based on visited locations. The introduced system should consider data privacy by design, such as preventing to store personal data about mobile users. The authors perceive personal data as "nonanonymized identifiers, demographics, geographics, psychographics, etc."