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From Sensors to Users - Using Microservices for the Handling of Measurement Data., , , , and . EnviroInfo, page 203-213. Springer, (2017)A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based Metaheuristics., , , , , and . PPAM (1), volume 12043 of Lecture Notes in Computer Science, page 432-444. Springer, (2019)An extended Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualizationz Technologies., , , , and . Internet Things, (2021)Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments using Web and Microservice Technologies., , , , and . MEDES, page 80-87. ACM, (2019)Descriptive Statistics Time-based Meta Features (DSTMF): Constructing a better Set of Meta Features for Model Selection in Energy Time Series Forecasting., , , and . APPIS, page 25:1-25:6. ACM, (2020)A Lightweight Web Components Framework for Accessing Generic Data Services in Environmental Information Systems., , , , and . EnviroInfo, page 191-201. Springer, (2017)Concept and Implementation of the Internet Site on Life Cycle Assessment Tools, Databases and Services, and on Life Cycle Data in Support of the European Integrated Product Policy., , , and . EnviroInfo, page 475-478. Shaker Verlag, Aachen, (2006)Dynamic Optimization of Energy Hubs with Evolutionary Algorithms Using Adaptive Time Segments and Varying Resolution., , , , , and . IDEAL, volume 13756 of Lecture Notes in Computer Science, page 513-524. Springer, (2022)A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics., , , , and . CoRR, (2020)Superlinear Speedup of Parallel Population-Based Metaheuristics: A Microservices and Container Virtualization Approach., , , , , and . IDEAL (1), volume 11871 of Lecture Notes in Computer Science, page 386-393. Springer, (2019)