GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan. (Q9504)

Dataset of the Internet of Things: Online Anomaly Detection for Drinking Water Quality competition hosted atThe Genetic and Evolutionary Computation Conference (GECCO)July 15th-19th 2018, Kyoto, Japan The task of thecompetition wasto develop an anomaly detection algorithm for a water- and environmental data set. Included in zenodo: - dataset of water quality data - additional material and descriptions provided for the competition The competition was organized by: F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Kln) The dataset was provided by: Thringer Fernwasserversorgung andIMProvT research project GECCO Industrial Challenge: Internet of Things: Online Anomaly Detection for Drinking Water Quality Description: For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with Thringer Fernwasserversorgung which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low. Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation! Accepted Competition Entry Abstracts - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana) - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University) - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH) Official webpage: http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/