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Abstract
Most organizations provide a portfolio of multiple applications from different vendors (with different data structures) for supporting computer-mediated collaboration among their employees. The heterogeneous format of the activity logs and the content data is challenging for the analysis of cross-platform collaboration and for comparing results from the different systems. To address these issues, we develop and demonstrate a prototype for a semantic data lake. Our prototype is based on Hadoop and ingests data from the source systems in near real-time. Ontology-based data access is implemented based on a mapping between an ontology and the ingested data. Thus, an analyst only needs to be familiar with the ontology and can use identical SPARQL queries to obtain the same information from the source systems. We demonstrate the ontology-database mapping and show how concepts from Web Science can solve practical problems in the Information Systems discipline, specifically for the area of Business Intelligence.
Recommended Citation
Schwade, Florian and Schubert, Petra, "A Semantic Data Lake for Harmonizing Data from Cross-Platform Digital Workspaces using Ontology-Based Data Access" (2020). AMCIS 2020 Proceedings. 2.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/2
A Semantic Data Lake for Harmonizing Data from Cross-Platform Digital Workspaces using Ontology-Based Data Access
Most organizations provide a portfolio of multiple applications from different vendors (with different data structures) for supporting computer-mediated collaboration among their employees. The heterogeneous format of the activity logs and the content data is challenging for the analysis of cross-platform collaboration and for comparing results from the different systems. To address these issues, we develop and demonstrate a prototype for a semantic data lake. Our prototype is based on Hadoop and ingests data from the source systems in near real-time. Ontology-based data access is implemented based on a mapping between an ontology and the ingested data. Thus, an analyst only needs to be familiar with the ontology and can use identical SPARQL queries to obtain the same information from the source systems. We demonstrate the ontology-database mapping and show how concepts from Web Science can solve practical problems in the Information Systems discipline, specifically for the area of Business Intelligence.
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