TY - JOUR A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel T1 - A framework for monitoring multiple databases in industries using OPC UA JF - Journal of Ambient Intelligence and Humanized Computing N2 - 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. UR - https://doi.org/10.1007/s12652-020-02850-x KW - OPC UA KW - Relational databases KW - Servers KW - Clients KW - Monitoring KW - Aggregation KW - SQL Y1 - 2020 UR - https://doi.org/10.1007/s12652-020-02850-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9464 SN - 1868-5145 VL - 12 IS - 1 SP - 47 EP - 56 PB - Springer CY - Berlin ER - TY - JOUR A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - Integration of manufacturing information via dynamic information model aggregation JF - Vietnam Journal of Computer Science N2 - 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. UR - https://doi.org/10.1142/S219688882150010X KW - Integration KW - OPC UA KW - interoperability KW - aggregation Y1 - 2020 UR - https://doi.org/10.1142/S219688882150010X UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9495 SN - 2196-8896 VL - 8 IS - 2 SP - 245 EP - 262 PB - Singapore CY - World Scientific ER - TY - JOUR A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Bhanja, Tapanta T1 - Exploring distance based approaches for reducing sensor data in defect related prognosis JF - Procedia Computer Science N2 - 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. UR - https://doi.org/10.1016/j.procs.2021.03.076 KW - sensors KW - data KW - reduced distance KW - machine learning KW - accuracy scores Y1 - 2021 UR - https://doi.org/10.1016/j.procs.2021.03.076 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13111 SN - 1877-0509 VL - 2021 IS - 184 SP - 614 EP - 621 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel T1 - An investigation on database connections in OPC UA applications JF - Procedia Computer Science N2 - 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. UR - https://doi.org/10.1016/j.procs.2020.03.132 KW - OPC UA KW - databases KW - implementations KW - SQL server KW - clients Y1 - 2020 UR - https://doi.org/10.1016/j.procs.2020.03.132 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12349 SN - 1877-0509 VL - 2020 IS - 170 SP - 602 EP - 609 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Knollmeyer, Simon A1 - Caymazer, Oğuz A1 - Koval, Leonid A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Gruenwald, Le ED - Masciari, Elio ED - Bernardino, Jorge T1 - Benchmarking of Retrieval Augmented Generation: A Comprehensive Systematic Literature Review on Evaluation Dimensions, Evaluation Metrics and Datasets T2 - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3 N2 - 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. UR - https://doi.org/10.5220/0013065700003838 Y1 - 2024 UR - https://doi.org/10.5220/0013065700003838 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58342 SN - 978-989-758-716-0 SP - 137 EP - 148 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - 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 JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2024.3406020 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3406020 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49240 SN - 2169-3536 VL - 12 SP - 80266 EP - 80282 PB - IEEE CY - New York ER - TY - CHAP A1 - Thakur, Akshay A1 - Beck, Robert A1 - Mostaghim, Sanaz A1 - Großmann, Daniel A1 - Kuttler, Moritz ED - Tang, Loon-Ching T1 - Prediction of Top Key Performance Indicator in Automotive Production System using Data Mining T2 - WCSE 2022 Spring Event: Proceedings of 2022 9th International Conference on Industrial Engineering and Applications UR - https://doi.org/10.18178/wcse.2022.04.079 Y1 - 2022 UR - https://doi.org/10.18178/wcse.2022.04.079 SN - 978-981-18-5852-9 SP - 675 EP - 684 PB - SCIEI CY - Rowland Heights ER - TY - CHAP A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mathias, Selvine George T1 - Architecture of a model in the middle approach for virtual commissioning and integration of production entities T2 - Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA45728.2021.9613619 KW - virtual commissioning KW - information model KW - information model management KW - cyber physical system KW - digital twin KW - machine communication KW - aggregation Y1 - 2021 UR - https://doi.org/10.1109/ETFA45728.2021.9613619 SN - 978-1-7281-2989-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - A concept towards the evolution and versioning of aggregated information models T2 - Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA45728.2021.9613193 KW - information modelling KW - versioning KW - evolution of models Y1 - 2021 UR - https://doi.org/10.1109/ETFA45728.2021.9613193 SN - 978-1-7281-2989-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schmied, Sebastian A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Jumar, Ulrich T1 - Integration of existing cyber-physical manufacturing systems into a common information model JF - IFAC-PapersOnLine N2 - 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. UR - https://doi.org/10.1016/j.ifacol.2020.12.2827 KW - Information Model KW - Aggregation KW - OPC UA KW - Cyber-physical manufacturing systems Y1 - 2021 UR - https://doi.org/10.1016/j.ifacol.2020.12.2827 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13309 SN - 2405-8963 VL - 53 IS - 2 SP - 10905 EP - 10910 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Thakur, Akshay A1 - Beck, Robert A1 - Mostaghim, Sanaz A1 - Großmann, Daniel T1 - Survey into predictive key performance indicator analysis from data mining perspective T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9212111 KW - KPI selection KW - KPI relationship KW - key performance indicators KW - predictive analysis KW - survey KW - data mining KW - best practices Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9212111 SN - 978-1-7281-8956-7 SP - 476 EP - 483 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mueller, Ralph T1 - Decentralized Integration Approach of Disjoint Modules in Factory Environments T2 - IEIM 2022: 2022 The 3rd International Conference on Industrial Engineering and Industrial Management UR - https://doi.org/10.1145/3524338.3524368 KW - Data Acquisition KW - OPC UA KW - Comunication KW - Instruments Y1 - 2022 UR - https://doi.org/10.1145/3524338.3524368 SN - 978-1-4503-9569-4 SP - 195 EP - 199 PB - ACM CY - New York ER - TY - CHAP A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Schmied, Sebastian T1 - Information model-based virtual commissioning for the testing and co-simulation of real production entities BT - Enabling of a logic for the virtual commissioning of information models using middleware technologies T2 - IEIM 2022: 2022 The 3rd International Conference on Industrial Engineering and Industrial Management UR - https://doi.org/10.1145/3524338.3524380 KW - Virtual Commissioning KW - Information Model KW - Co-Simulation KW - Cyber Physikal System KW - Machine Communication Y1 - 2022 UR - https://doi.org/10.1145/3524338.3524380 SN - 978-1-4503-9569-4 SP - 271 EP - 276 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Kampa, Thomas A1 - Müller, Christian Klaus A1 - Großmann, Daniel T1 - IP-based Architecture for an Edge Cloud enabled Factory BT - Concept and Requirements T2 - 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS) UR - https://doi.org/10.1109/WFCS53837.2022.9779162 KW - industrial internet of things KW - virtualization KW - brownfield deployment KW - industrial network Y1 - 2022 UR - https://doi.org/10.1109/WFCS53837.2022.9779162 SN - 978-1-6654-1086-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Nothdurft, Lars A1 - Epple, Ulrich A1 - Schröder, Tizian A1 - Diedrich, Christian A1 - Großmann, Daniel A1 - Banerjee, Suprateek A1 - Schmied, Sebastian A1 - Iatrou, Chris Paul A1 - Graube, Markus A1 - Urbas, Leon A1 - Henrichs, Tim Peter A1 - Erben, Stefan T1 - NOA Demonstratoren Special JF - atp magazin UR - https://doi.org/10.17560/atp.v60i01-02.2348 KW - Namur KW - Offene Architekturen KW - Automatisierung KW - IoT KW - OPC UA KW - Monitoring KW - Optimization Y1 - 2018 UR - https://doi.org/10.17560/atp.v60i01-02.2348 SN - 2190‑4111 VL - 60 IS - 1-2 SP - 44 EP - 69 PB - Vulkan CY - Essen ER - TY - CHAP A1 - Ambrosy, Niklas A1 - Kampa, Thomas A1 - Jumar, Ulrich A1 - Großmann, Daniel T1 - 5G and DetNet: Towards holistic determinism in industrial networks T2 - 2022 IEEE International Conference on Industrial Technology (ICIT) UR - https://doi.org/10.1109/ICIT48603.2022.10002776 KW - 5G KW - Deterministic Networking KW - Time-Sensitive Networking KW - Software-Defined Networking KW - industrial communication Y1 - 2022 UR - https://doi.org/10.1109/ICIT48603.2022.10002776 SN - 978-1-72811-948-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Miranda, Jorge A1 - Cabral, Jorge A1 - Banerjee, Suprateek A1 - Großmann, Daniel A1 - Fischer Pedersen, Christian A1 - Wagner, Stefan T1 - Analysis of OPC unified architecture for healthcare applications T2 - ETFA'2017: IEEE 22nd International Conference on Emerging Technologies & Factory Automation UR - https://doi.org/10.1109/ETFA.2017.8247771 KW - ambient assisted living KW - pervasive healthcare KW - industry 4.0 KW - OPC UA KW - HL7 KW - wireless sensor networks KW - cloud computing KW - infrastructure Y1 - 2017 UR - https://doi.org/10.1109/ETFA.2017.8247771 SN - 978-1-5090-6505-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Banerjee, Suprateek A1 - Großmann, Daniel T1 - An Electronic Device Description Language based approach for communication with dbms and file system in an industrial automation scenario T2 - ETFA'2016: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA.2016.7733682 KW - Databases KW - File systems KW - Servers KW - Production KW - Automation KW - Standards KW - Middleware Y1 - 2016 UR - https://doi.org/10.1109/ETFA.2016.7733682 SN - 978-1-5090-1314-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Großmann, Daniel A1 - Bregulla, Markus A1 - Banerjee, Suprateek A1 - Schulz, Dirk A1 - Braun, Roland T1 - OPC UA server aggregation — The foundation for an internet of portals T2 - ETFA'2014: 19th IEEE International Conference on Emerging Technologies and Factory Automation UR - https://doi.org/10.1109/ETFA.2014.7005354 KW - Servers KW - Automation KW - Aggregates KW - Security KW - Prototypes KW - Computer architecture KW - Market research Y1 - 2014 UR - https://doi.org/10.1109/ETFA.2014.7005354 SN - 978-1-4799-4846-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Sequeira, Gerald Joy T1 - A Comparison of Clustering Measures on Raw Signals of Welding Production Data T2 - Proceedings 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) UR - https://doi.org/10.1109/Deep-ML.2019.00019 KW - clustering KW - k-means KW - kmeans KW - hierarchical KW - metric evaluation Y1 - 2019 UR - https://doi.org/10.1109/Deep-ML.2019.00019 SN - 978-1-7281-2914-3 SP - 55 EP - 60 PB - IEEE CY - Piscataway ER -