###### Abstract  
Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Diversify, a collaborative project between industry and academia, investigated how event and anomaly detection can be performed on time-series data in such a hybrid setting. We built a proof-of-concept analysis platform, using a microservice architecture to ensure scalability and fault-tolerance. The platform comprises time-series ingestion, long term storage, data semantization, event detection using data-driven and semantic techniques, dynamic visualization, and user feedback. In this work, we describe the system architecture of this hybrid analysis platform and give an overview of the different components and their interactions. As such, the main contribution of this work is an experience report with challenges faced and lessons learned. Keywords:time series; data analytics; machine learning; semantic web; reasoning; microservice architecture +
Footnote †: journal: Medical  
architecture that combines knowledge-driven and data-driven algorithms and takes into account all the above requirements does not exist. In the imec.icon Dyversify project ([[https://www.imec-int.com/en/what-we-offer/research-portfolio/dyversify](https://www.imec-int.com/en/what-we-offer/research-portfolio/dyversify)]([https://www.imec-int.com/en/what-we-offer/research-portfolio/dyversify](https://www.imec-int.com/en/what-we-offer/research-portfolio/dyversify)), (accessed on 7 December 2021)), academia and industry partners collaborated to investigate how (data-driven) machine learning and semantic (knowledge-based) techniques can be combined for improved event and anomaly detection on time series originating from IoT sensors. We envision that by fusing semantics and machine learning, amongst others, state-of-the-art data-driven analysis systems can be improved, and expert feedback can enable automatic filtering of false positives and/or enable the shift from unsupervised towards supervised learning. In this article, we present the resulting Dyversify architecture that enables this fusion of data-driven and semantic techniques. We also provide high-level descriptions of the individual components, explain design choices made, and discuss open questions and challenges. A working, full-stack prototype combining all of the requirements listed has been created and validated using two use cases with real world data provided by our industry partners: Renson, Televic Rail, and Cumul.io. To the best of our knowledge, we are the first to present an architectural design and full-stack, hybrid implementation for (i) fusion of semantic technologies and machine learning and (ii) composed of solely open-source building blocks. This article focuses on the Renson use case, where air quality metrics from consumer owned ventilation units are used for event and anomaly detection. While we do discuss the underlying data analysis techniques, they are not the focus of this article and mainly serve to give the reader a better understanding of the system. Instead we refer to dedicated works discussing those techniques where appropriate. The innovations in this work entail: (1) the description of a full-stack, hybrid time-series analysis microservice architecture and (2) the description of the experiences and challenges encountered while designing and testing this system. As such, we believe this work will be most interesting for parties who seek to build any type of hybrid data analysis pipeline going from sensor to dashboard but who lack relevant experience in designing such a system. The remainder of this work is structured as follows. We start by giving background information about the Dyversify project and the Renson use case in Section 2, as well as a brief introduction in the Semantic Web domain.