With an ongoing digital transformation towards industry 4.0 and the corresponding growth of collected sensor data based on cyberphysical systems, the need for automatic data analysis in industrial production lines has increased drastically. One relevant application scenario is the usage of intelligent approaches to anticipate upcoming failures for maintenance. In this paper, we present a novel approach for anomaly detection regarding predictive maintenance in an industrial data-intensive environment. In particular, we are focusing on historical sensor data from a real reflow oven that is used for soldering surface mount electronic components to printed circuit boards. The sensor data, which is provided within the scope of the EU-Project COMPOSITION (under grant no. 723145), comprises information about the heat and the power consumption of individual fans inside a reflow oven. The data set contains timeannotated sensor measurements in combination with additional process information over a period of more than seven years.

Data are a central phenomenon in our digital information age. They impact the way we live, work, and play and provide unprecedented opportunities to simplify our daily life and behavior. They implicate enormous potential and impact society, economy, and science. Due to the advancement of cyber-physical systems and Internet of Things technologies, it is expected that the majority of real-time data will be generated from devices interconnected within the Internet of Things by the year 2025. In this paper, we tackle the problem of managing Internet of Things data in an efficient way. To this end, we introduce the metric approach for storing and querying Internet of Things data and investigate the ability of pivot-based tables for indexing and searching this type of data. Along with the introduction of two real-world, large-scale Internet of Things datasets from the EU projects COMPOSITION and MONSOON (under grant no. 723145 and 723650), we show that the metric approach facilitates efficient data access in the Internet of Things.

3D motion capture data is a specific type of data arising in the Internet of Things. It is widely used in science and industry for recording the movements of humans, animals, or objects over time. In order to facilitate efficient spatio-temporal access into large 3D motion capture databases collected via internet-of-things technology, we propose an efficient 2-Phase Point-based Trajectory Search Algorithm (2PPTSA) which is built on top of a compact in-memory spatial access method. The 2PPTSA is fundamental to any type of pattern-based investigation and enables fast and scalable point-based pattern search in 3D motion capture databases. Our empirical evaluation shows that the 2PPTSA is able to retrieve the most similar trajectories for a given point-based query pattern in a few milliseconds with a comparatively low number of I/O accesses.

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In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution-hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.