Frontiers of Business Intelligence and Analytics

Three emerging research areas received less attention in the past: Data science foundations, Text analytics, and Human computer interaction (HCI). As Data science foundations comprise research results regarding the optimization and storage of large data sets, we suggest investigating data merging and filtering algorithms and its requirements for the storage of relevant data. Since the data science foundations are also addressed by other scientific disciplines, such as informatics and computer science, its under-representation in BI & A 3.0 topics is reasonable to some extent. Nevertheless, IS research might produce fruitful contributions regarding the investigation of data science foundations for BI & A 3.0. The identified underrepresentation of Text analytics is surprising, since this research area has had several promotors in the IS research community. In addition, the underrepresentation of research results in the area of HCI and BI & A 3.0 indicates a further research gap. Since HCI is a well-established research track in top IS conferences, such as ICIS and ECIS, we can solely guess that this topic is currently not in focus of BI & A researchers.

In the application area of Smart health, the question of how to store and to filter health data generated by smart health devices, such as fitness tracker or fitness apps needs to be answered (7). The results might deliver the foundations for developing a prototype that creates diagnoses for certain illnesses. By applying an Action research approach, such prototype can be evaluated.

In contrast to Data science foundations, Text analytics aims at understanding and interpreting textual content. In the area of E-Government & politics, we suggest answering the question of how the usage behavior of certain messenger apps influences voter turnout rates (8). To answer this question, we suggest using existing textual social media communication before a certain election to train a neural net model that predicts turnout rates of an upcoming election. For inspiring research in the area of Human computer interaction, applied in Security and public safety, we suggest answering the question of "how does the police interact with a system that enables the profiling of thieves based on smart home data" (9). We believe that the massive amount of smart home data (such as smoke detectors, window open detectors, or motion detectors) has the potential to produce detailed profiles of thieves, which in turn may support the work of the police.