Christian Thomsen

Modern wind turbines are monitored by hundreds of sensors that can perform sub-second measurements. Efficient management of this volume and velocity of time series requires to address several challenges: slow ingestion, limited bandwidth, high storage costs and low data quality after compression. Currently used solutions in industry only partially address these challenges. Thus, in this paper, we evaluate the time series management system ModelarDB as a solution that addresses the challenges more efficiently. We evaluate ModelarDB in a realistic edge-to-cloud scenario based on real-world requirements and data by varying different aspects of compression: massive real-life datasets, sampling interval and error bound.

Data cubes are used for analyzing large data sets usually contained in data warehouses. The most popular data cube tools use graphical user interfaces (GUI) to do the data analysis. Traditionally this was fine since data analysts were not expected to be technical people. However, in the subsequent decades the data landscape changed dramatically requiring companies to employ large teams of highly technical data scientists in order to manage and use the ever increasing amount of data. These data scientists generally use tools like Python, interactive notebooks, pandas, etc. while modern data cube tools are still GUI based. This paper proposes a Python-based data cube tool called pyCube. pyCube is able to semi-automatically create data cubes for data stored in an RDBMS and manages the data cube metadata. pyCube's programmatic interface enables data scientist to query data cubes by specifying the expected metadata of the result. pyCube is experimentally evaluated on Star Schema Benchmark (SSB). The results show that pyCube vastly outperforms different implementations of SSB queries in pandas in both runtime and memory while being easier to read and write.

Modern Renewable Energy System (RES) installations, e.g., wind turbines, produce petabytes of high-frequency time series. State-of-the-art systems cannot cope with such amounts of data. Thus, practitioners generally store simple aggregates, e.g., 10-min averages. Based on discussions with practitioners, we present requirements and our vision for a next-generation time series management system that can efficiently manage vast amounts of time series across edge, cloud, and client.

To ensure critical infrastructure is operating as expected, high-quality sensors are increasingly installed. However, due to the enormous amounts of high-frequency time series they produce, it is impossible or infeasible to transfer or even store these time series in the cloud when using state-of-the-practice compression methods. Thus, simple aggregates, e.g., 1–10-minutes averages, are stored instead of the raw time series. However, by only storing these simple aggregates, informative outliers and fluctuations are lost. Many Time Series Management System (TSMS) have been proposed to efficiently manage time series, but they are generally designed for either the edge or the cloud. In this paper, we describe a new version of the open-source model-based TSMS ModelarDB. The system is designed to be modular and the same binary can be efficiently deployed on the edge and in the cloud. It also supports …

According to the 2021 energy efficiency report of the European Union (EU), 75% of the existing buildings in the EU have been assessed as energy-inefficient. Internet of Things (IoT) services are developed to increase energy efficiency in buildings. The W3C recommends the use of the W3C Web of Things (WoT) standard to enable IoT interoperability on the Web. However, the ability to discover IoT devices available in the WoT remains a challenge due to the lack of ontologies integrating WoT Thing Descriptions in smart buildings. We present in this paper the domOS Common Ontology (dCO) to achieve the W3C WoT discovery in smart residential buildings in 5 demonstration sites of the H2020 EU domOS project. This ontology integrates the WoT Thing Description with IoT concepts, i.e. IoT devices and building topology, in order to leverage the W3C WoT Discovery. We made the WoT Discovery implementation …

Smart energy services deployed in buildings have the potential to increase their energy efficiency and to turn them into active nodes of energy grids, with limited costs and in the short term. Today, smart services are deployed by manufacturers of energy appliances as independent silo solutions. The lack of a common approach prevents the deployment of unified multi-appliance, multi-service solutions. This paper presents the domOS ecosystem specification, a guideline for a unified organisation of energy services where multiple applications can access multiple on-line appliances and devices, if permitted. The specification leverages legacy IoT technologies and can be implemented with a limited effort on any existing IoT platform. A compliant IoT platform acts as an “operating system” for the building, effectively decoupling the application plane and the building infrastructure plane. The domOS ecosystem specification builds upon the Web of Things (WoT) architecture defined by W3C. Compliant buildings feature a digital nameplate called Building Description (BD). The BD is a document readable by machines and humans that contains relevant metadata (eg, construction type, size, energy system…) and provides handles to monitor and control local energy processes. The domOS ecosystem specification leads to a unified and standardised approach of energy services in buildings.

Buildings are the largest energy consumers in Europe and are responsible for approximately 40% of EU energy consumption and 36% of the greenhouse gas emissions in Europe. Two-thirds of the building consumption is for residential buildings. To achieve energy efficiency, buildings are being integrated with IoT devices through the use of smart IoT services. For instance, a smart space heating service reduces energy consumption by dynamically heating apartments based on indoor and outdoor temperatures. The W3C recommends the use of the Web of Things (WoT) standard to enable IoT interoperability on the Web. However, in the context of a smart building, the ability to search and discover building metadata and IoT devices available in the WoT ecosystems remains a challenge due to the limitation of the current WoT Discovery, which only includes a directory containing only IoT devices metadata without …

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and predictions are also steadily increasing. In this paper, we provide an overview of a novel end-to-end platform that provides efficient ingestion, compression, transfer, query processing, and machine learning-based analytics for high-frequency and high-volume time series from IoT. The performance of the platform is evaluated using real-world dataset from RES installations. The results show the importance of high-frequency analytics and the surprisingly positive impact of error bounded lossy compression on machine learning in the form of AutoML. For example, when detecting yaw misalignments in wind turbines, an improvement of 9% in accuracy was observed for …