@article{MathiasSchmiedGrossmann2020, author = {Mathias, Selvine George and Schmied, Sebastian and Großmann, Daniel}, title = {A framework for monitoring multiple databases in industries using OPC UA}, volume = {12}, journal = {Journal of Ambient Intelligence and Humanized Computing}, number = {1}, publisher = {Springer}, address = {Berlin}, issn = {1868-5145}, doi = {https://doi.org/10.1007/s12652-020-02850-x}, pages = {47 -- 56}, year = {2020}, abstract = {Database management and monitoring is an inseparable part of any industry. A uniform scheme of monitoring relational databases without explicit user access to database servers is not much explored outside the database environment. In this paper, we present an information distribution scheme related to databases using Open Platform Communication Unified Architecture (OPC UA) servers to clients when multiple databases are involved in a factory. The aim is for external, but relevant clients, to be able to monitor this information mesh independent of explicit access to user schemas. A methodology to dispense data from, as well as check changes in databases using SQL queries and events is outlined and implemented using OPC UA servers. The structure can be used as a remote viewing application for multiple databases in one address space of an OPC UA server.}, language = {en} } @article{SchmiedGrossmannMathiasetal.2020, author = {Schmied, Sebastian and Großmann, Daniel and Mathias, Selvine George and Mueller, Ralph}, title = {Integration of manufacturing information via dynamic information model aggregation}, volume = {8}, journal = {Vietnam Journal of Computer Science}, number = {2}, publisher = {Singapore}, address = {World Scientific}, issn = {2196-8896}, doi = {https://doi.org/10.1142/S219688882150010X}, pages = {245 -- 262}, year = {2020}, abstract = {An important part of the industry 4.0 concept is the horizontal and vertical integration of manufacturing systems. Information exchange in traditional production environments happens through interfaces that are connections between strictly defined senders and receivers. This limits the possibility for changing and extending the manufacturing system. A possible approach to enable the information exchange between all system entities uniformly are information models. Such models are semantic descriptions of the available data. The creation of these models needs to follow the manufacturing process, but also requires certain standardization to improve efficiency. Another challenge is the actual technical integration of the information into a common address space. This paper connects an approach for information modeling with a concept for dynamic aggregation. The approach is described with the help of a continuous example that uses OPC UA as a middleware technology.}, language = {en} } @article{MathiasGrossmannBhanja2021, author = {Mathias, Selvine George and Großmann, Daniel and Bhanja, Tapanta}, title = {Exploring distance based approaches for reducing sensor data in defect related prognosis}, volume = {2021}, journal = {Procedia Computer Science}, number = {184}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2021.03.076}, pages = {614 -- 621}, year = {2021}, abstract = {Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors.}, language = {en} } @article{MathiasSchmiedGrossmann2020, author = {Mathias, Selvine George and Schmied, Sebastian and Großmann, Daniel}, title = {An investigation on database connections in OPC UA applications}, volume = {2020}, journal = {Procedia Computer Science}, number = {170}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2020.03.132}, pages = {602 -- 609}, year = {2020}, abstract = {Open Platform Communications - Unified Architecture (OPC UA) as a communication protocol has been one of the front running IoT enablers in recent years. Many use cases in manufacturing domains demonstrate the vast inter-operability and cross-platform connectivity strengths of OPC UA. However, to consolidate its position in the IoT world, OPC UA needs to be able to bridge certain gaps, like interconnections across multiple database engines over different networks. This paper aims to investigate aggregation dynamics of OPC UA consisting of multiple database servers from a specific environment such as manufacturing. Prevailing databases over restricted networks such as production floors cannot be accessed by respective clients outside the system. In these cases, OPC UA services can provide an a selective but imperative view of data from these databases to the clients. The investigations in this paper are demonstrated with two different implementations of databases related to manufacturing with OPC UA. These applications show that using OPC UA as a means of connection and distribution of information from databases can be an effective solution to a common data view of all connections not necessarily of factory floor network.}, language = {en} } @inproceedings{KnollmeyerCaymazerKovaletal.2024, author = {Knollmeyer, Simon and Caymazer, Oğuz and Koval, Leonid and Akmal, Muhammad Uzair and Asif, Saara and Mathias, Selvine George and Großmann, Daniel}, title = {Benchmarking of Retrieval Augmented Generation: A Comprehensive Systematic Literature Review on Evaluation Dimensions, Evaluation Metrics and Datasets}, booktitle = {Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3}, editor = {Gruenwald, Le and Masciari, Elio and Bernardino, Jorge}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-716-0}, doi = {https://doi.org/10.5220/0013065700003838}, pages = {137 -- 148}, year = {2024}, abstract = {Despite the rapid advancements in the field of Large Language Models (LLM), traditional benchmarks have proven to be inadequate for assessing the performance of Retrieval Augmented Generation (RAG) systems. Therefore, this paper presents a comprehensive systematic literature review of evaluation dimensions, metrics, and datasets for RAG systems. This review identifies key evaluation dimensions such as context relevance, faithfulness, answer relevance, correctness, and citation quality. For each evaluation dimension, several metrics and evaluators are proposed on how to assess them. This paper synthesizes the findings from 12 relevant papers and presents a concept matrix that categorizes each evaluation approach. The results provide a foundation for the development of robust evaluation frameworks and suitable datasets that are essential for the effective implementation and deployment of RAG systems in real-world applications.}, language = {en} } @article{KovalKnollmeyerMathiasetal.2024, author = {Koval, Leonid and Knollmeyer, Simon and Mathias, Selvine George and Asif, Saara and Akmal, Muhammad Uzair and Großmann, Daniel and Bregulla, Markus}, title = {Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2024.3406020}, pages = {80266 -- 80282}, year = {2024}, abstract = {Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of "Production IT." Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.}, language = {en} } @inproceedings{ThakurBeckMostaghimetal.2022, author = {Thakur, Akshay and Beck, Robert and Mostaghim, Sanaz and Großmann, Daniel and Kuttler, Moritz}, title = {Prediction of Top Key Performance Indicator in Automotive Production System using Data Mining}, booktitle = {WCSE 2022 Spring Event: Proceedings of 2022 9th International Conference on Industrial Engineering and Applications}, editor = {Tang, Loon-Ching}, publisher = {SCIEI}, address = {Rowland Heights}, isbn = {978-981-18-5852-9}, doi = {https://doi.org/10.18178/wcse.2022.04.079}, pages = {675 -- 684}, year = {2022}, language = {en} } @inproceedings{MuellerGrossmannSchmiedetal.2021, author = {Mueller, Ralph and Großmann, Daniel and Schmied, Sebastian and Mathias, Selvine George}, title = {Architecture of a model in the middle approach for virtual commissioning and integration of production entities}, booktitle = {Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2989-1}, doi = {https://doi.org/10.1109/ETFA45728.2021.9613619}, year = {2021}, language = {en} } @inproceedings{SchmiedGrossmannMathiasetal.2021, author = {Schmied, Sebastian and Großmann, Daniel and Mathias, Selvine George and Mueller, Ralph}, title = {A concept towards the evolution and versioning of aggregated information models}, booktitle = {Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2989-1}, doi = {https://doi.org/10.1109/ETFA45728.2021.9613193}, year = {2021}, language = {en} } @article{SchmiedMathiasGrossmannetal.2021, author = {Schmied, Sebastian and Mathias, Selvine George and Großmann, Daniel and Jumar, Ulrich}, title = {Integration of existing cyber-physical manufacturing systems into a common information model}, volume = {53}, journal = {IFAC-PapersOnLine}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8963}, doi = {https://doi.org/10.1016/j.ifacol.2020.12.2827}, pages = {10905 -- 10910}, year = {2021}, abstract = {In order to be able to serve constantly new customer requirements, manufacturing systems must be able to adapt to frequent changes. In addition, repeatedly objects are removed or added to the network. To control and monitor such a constantly changing system a mapping of existing manufacturing systems into a common information model is necessary. This model describes information that is produced and stored in different entities of the complete system. To create a common address space and expose the relations between the devices an aggregation of every element in the system is needed. This paper describes a methodology for the creation of an information model for a complete manufacturing environment, followed by an approach for the aggregation of the singular system entities. The concept of this paper is illustrated with a demonstrator. The results of this approach have been discussed in the following sections along with the proposal for further directions.}, language = {en} } @inproceedings{ThakurBeckMostaghimetal.2020, author = {Thakur, Akshay and Beck, Robert and Mostaghim, Sanaz and Großmann, Daniel}, title = {Survey into predictive key performance indicator analysis from data mining perspective}, booktitle = {Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-8956-7}, doi = {https://doi.org/10.1109/ETFA46521.2020.9212111}, pages = {476 -- 483}, year = {2020}, language = {en} } @inproceedings{MathiasGrossmannSchmiedetal.2022, author = {Mathias, Selvine George and Großmann, Daniel and Schmied, Sebastian and Mueller, Ralph}, title = {Decentralized Integration Approach of Disjoint Modules in Factory Environments}, booktitle = {IEIM 2022: 2022 The 3rd International Conference on Industrial Engineering and Industrial Management}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-9569-4}, doi = {https://doi.org/10.1145/3524338.3524368}, pages = {195 -- 199}, year = {2022}, language = {en} } @inproceedings{MuellerGrossmannSchmied2022, author = {Mueller, Ralph and Großmann, Daniel and Schmied, Sebastian}, title = {Information model-based virtual commissioning for the testing and co-simulation of real production entities}, booktitle = {IEIM 2022: 2022 The 3rd International Conference on Industrial Engineering and Industrial Management}, subtitle = {Enabling of a logic for the virtual commissioning of information models using middleware technologies}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-9569-4}, doi = {https://doi.org/10.1145/3524338.3524380}, pages = {271 -- 276}, year = {2022}, language = {en} } @inproceedings{KampaMuellerGrossmann2022, author = {Kampa, Thomas and M{\"u}ller, Christian Klaus and Großmann, Daniel}, title = {IP-based Architecture for an Edge Cloud enabled Factory}, booktitle = {2022 IEEE 18th International Conference on Factory Communication Systems (WFCS)}, subtitle = {Concept and Requirements}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-1086-1}, doi = {https://doi.org/10.1109/WFCS53837.2022.9779162}, year = {2022}, language = {en} } @article{NothdurftEppleSchroederetal.2018, author = {Nothdurft, Lars and Epple, Ulrich and Schr{\"o}der, Tizian and Diedrich, Christian and Großmann, Daniel and Banerjee, Suprateek and Schmied, Sebastian and Iatrou, Chris Paul and Graube, Markus and Urbas, Leon and Henrichs, Tim Peter and Erben, Stefan}, title = {NOA Demonstratoren Special}, volume = {60}, journal = {atp magazin}, number = {1-2}, publisher = {Vulkan}, address = {Essen}, issn = {2190‑4111}, doi = {https://doi.org/10.17560/atp.v60i01-02.2348}, pages = {44 -- 69}, year = {2018}, language = {de} } @inproceedings{AmbrosyKampaJumaretal.2022, author = {Ambrosy, Niklas and Kampa, Thomas and Jumar, Ulrich and Großmann, Daniel}, title = {5G and DetNet: Towards holistic determinism in industrial networks}, booktitle = {2022 IEEE International Conference on Industrial Technology (ICIT)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-72811-948-9}, doi = {https://doi.org/10.1109/ICIT48603.2022.10002776}, year = {2022}, language = {en} } @inproceedings{MirandaCabralBanerjeeetal.2017, author = {Miranda, Jorge and Cabral, Jorge and Banerjee, Suprateek and Großmann, Daniel and Fischer Pedersen, Christian and Wagner, Stefan}, title = {Analysis of OPC unified architecture for healthcare applications}, booktitle = {ETFA'2017: IEEE 22nd International Conference on Emerging Technologies \& Factory Automation}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-6505-9}, doi = {https://doi.org/10.1109/ETFA.2017.8247771}, year = {2017}, language = {en} } @inproceedings{BanerjeeGrossmann2016, author = {Banerjee, Suprateek and Großmann, Daniel}, title = {An Electronic Device Description Language based approach for communication with dbms and file system in an industrial automation scenario}, booktitle = {ETFA'2016: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-1314-2}, doi = {https://doi.org/10.1109/ETFA.2016.7733682}, year = {2016}, language = {en} } @inproceedings{GrossmannBregullaBanerjeeetal.2014, author = {Großmann, Daniel and Bregulla, Markus and Banerjee, Suprateek and Schulz, Dirk and Braun, Roland}, title = {OPC UA server aggregation — The foundation for an internet of portals}, booktitle = {ETFA'2014: 19th IEEE International Conference on Emerging Technologies and Factory Automation}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-4846-8}, doi = {https://doi.org/10.1109/ETFA.2014.7005354}, year = {2014}, language = {en} } @inproceedings{MathiasGrossmannSequeira2019, author = {Mathias, Selvine George and Großmann, Daniel and Sequeira, Gerald Joy}, title = {A Comparison of Clustering Measures on Raw Signals of Welding Production Data}, booktitle = {Proceedings 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2914-3}, doi = {https://doi.org/10.1109/Deep-ML.2019.00019}, pages = {55 -- 60}, year = {2019}, language = {en} }