List of Publications

@inproceedings{Seiger.2024_MicroserviceSmartFactorySoftware,

title = {Revision of a Smart Factory Software Architecture from Monolith to Microservices},

author = {Ronny Seiger and Lukas Malburg},

url = {https://www.wi2.uni-trier.de/shared/publications/2024_EDOC_MicroserviceSmartFactorySoftware_SeigerMalburg.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Enterprise Design, Operations, and Computing. EDOC 2024 Forum, Vienna, Austria, September 10-13, 2024},

publisher = {Springer.},

series = {Lecture Notes in Business Information Processing, %pages = 50\textendash66, %volume = 14775, %doi = 10.1007/978-3-031-63646-2_4},

abstract = {Software architecture plays an important role in the development of modern, complex software systems as it influences a system’s quality attributes and ability to grow with future demand. Designing the software architecture of cyber-physical systems (CPS) becomes even more challenging due to their capability of directly influencing the physical world and thus introducing new non-functional requirements related to fault-tolerance, safety, and resource scarcity. Existing research focuses on systems engineering to achieve the vertical integration of CPS with an organization’s information systems and processes, but not on software architecture to horizontally extend existing systems with new CPS. In this report we describe the process of revising an existing monolithic software architecture for a smart factory towards a microservices-based architecture to meet these new requirements and prepare the factory to be extended with new CPS. For the revision of the existing architecture, we provide an analysis of its code base before and after changes, a description of the refactoring process, and discuss relevant new nonfunctional requirements and architecture options. We elaborate on the architectural decisions favoring microservices and analyze the new architecture regarding improved quality attributes to evaluate the system.},

note = {Accepted for Publication.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malburg.2024_AdaptationRulesInPOCBR,

title = {Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation},

author = {Lukas Malburg and Maxim Hotz and Ralph Bergmann},

url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_RuleBasedAdaptation_MalburgEtAl.pdf},

doi = {10.1007/978-3-031-63646-2_4},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings},

volume = {14775},

pages = {50\textendash66},

publisher = {Springer.},

series = {Lecture Notes in Computer Science},

abstract = {Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lenz.2024_CBRkit,

title = {CBRkit: An Intuitive Case-Based Reasoning Toolkit for Python},

author = {Mirko Lenz and Lukas Malburg and Ralph Bergmann},

url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_CBRKit_LenzEtAl.pdf},

doi = {10.1007/978-3-031-63646-2_19},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings},

volume = {14775},

pages = {289\textendash304},

publisher = {Springer.},

series = {Lecture Notes in Computer Science},

abstract = {Developing Case-Based Reasoning (CBR) applications is a complex and demanding task that requires a lot of experience and a deep understanding of users. Additionally, current CBR frameworks are not as usable as Machine Learning (ML) frameworks that can be deployed with only a few lines of code. To address these problems and allow users to easily build hybrid Artificial Intelligence (AI) systems by combining CBR with techniques such as ML, we present the CBRkit library in this paper. CBRkit is a Python-based framework that provides generic and easily extensible functions to simplify the creation of CBR applications with advanced similarity measures and case representations. The framework is available from GitHub and PyPI under the permissive MIT license. An initial user study indicates that it is easily possible even for non-CBR experts and users who only have limited Python programming skills to develop their own customized CBR application.},

note = {Best Student Paper.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Schultheis.2024_MissingSensorValues,

title = {Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing},

author = {Alexander Schultheis and Lukas Malburg and Joscha Gr\"{u}ger and Justin Weich and Yannis Bertrand and Ralph Bergmann and Estefan\'{i}a Serral Asensio},

url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_SchultheisEtAl.pdf},

doi = {10.1007/978-3-031-63646-2_16},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings},

volume = {14775},

pages = {240\textendash257},

publisher = {Springer.},

series = {Lecture Notes in Computer Science},

abstract = {Smart Manufacturing integrates methods of Artificial Intelligence and the Internet of Things into processes to enhance efficiency and flexibility. However, analysis of time series sensor data, crucial for process optimization, is susceptible to Data Quality Issues (DQIs) and can lead to operational problems. Traditional machine learning approaches struggle with limited error data availability in addressing DQIs. The knowledge-driven approach of Case-Based Reasoning targets this issue by reusing experiences regarding already identified DQIs. While some DQIs can be detected using conventional similarity measures, the common, frequently occurring DQI type of Missing Sensor Values pose challenges that cannot be solved using established measures. To address this, this paper proposes a weight-based extension of similarity measures for time series data. This extension aims at the identification and handling of missing sensor values in smart manufacturing processes. Furthermore, analog extensions of established time series measures are presented and possible areas of application outside the DQI domain are outlined.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Brand.2024_LLMKnowledgeEngineer,

title = {Large Language Models as Knowledge Engineers},

author = {Florian Brand and Lukas Malburg and Ralph Bergmann},

editor = {Lukas Malburg},

url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR-WS_LLMInCBR_BrandEtAl.pdf},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the Workshops at the 32nd International Conference 

 on Case-Based Reasoning (ICCBR-WS 2024) co-located with the 32nd 

 International Conference on Case-Based Reasoning (ICCBR 2024), M\'{e}rida, 

 Mexico, July 1, 2024},

volume = {3708},

pages = {3\textendash18},

publisher = {CEUR-WS.org.},

series = {CEUR Workshop Proceedings},

abstract = {Many Artificial Intelligence (AI) systems require human-engineered knowledge at their core to reason about new problems based on this knowledge, with Case-Based Reasoning (CBR) being no exception. However, the acquisition of this knowledge is a time-consuming and laborious task for the domain experts that provide the needed knowledge. We propose an approach to help in the creation of this knowledge by leveraging Large Language Models (LLMs) in conjunction with existing knowledge to create the vocabulary and case base for a complex real-world domain. We find that LLMs are capable of generating knowledge, with results improving by using natural language and instructions. Furthermore, permissively licensed models like CodeLlama and Mixtral perform similar or better than closed state-of-the-art models like GPT-3.5 Turbo and GPT-4 Turbo.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ehrendorfer.2023_IoPTRepository,

title = {Internet of Processes and Things: A Repository for IoT-Enriched 

 Event Logs in Smart Environments},

author = {Matthias Ehrendorfer and Yannis Bertrand and Lukas Malburg and Juergen Mangler and Joscha Gr\"{u}ger and Stefanie Rinderle-Ma and Ralph Bergmann and Estefan'ıa Serral Asensio},

editor = {Dirk Fahland and Andr\'{e}s Jim\'{e}nez-Ram'ırez and Akhil Kumar and Jan Mendling and Brian T. Pentland and Stefanie Rinderle-Ma and Tijs Slaats and Johan Versendaal and Barbara Weber and Mathias Weske and Karolin Winter},

url = {http://www.wi2.uni-trier.de/shared/publications/2023_Ehrendorfer_IoPTRepository.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration \& Resources Forum at BPM 2023 co-located with 21st International Conference on Business Process Management (BPM 2023), Utrecht, The Netherlands, September 11th to 15th, 2023},

volume = {3469},

pages = {92\textendash96},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {Current research efforts from the Business Processes Management and IoT communities, touching novel techniques such as object-centric process mining, predictive process monitoring or IoT based process enhancement suffer from a lack of real-world open data-sets from different domains to evaluate and refine their approaches. In this paper we present an online resource for the collection of IoT-enhanced process logs, hoping to inspire companies, organisations and researchers to cooperate in the release of well-understood high-quality data sets.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SchultheisHMB2023,

title = {Explanation of Similarities in Process-Oriented Case-Based Reasoning by Visualization},

author = {Alexander Schultheis and Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},

url = {https://www.wi2.uni-trier.de/shared/publications/2023_ICCBR_SchultheisHMB.pdf},

doi = {10.1007/978-3-031-40177-0_4},

year  = {2023},

date = {2023-01-01},

booktitle = {Case-Based Reasoning Research and Development - 31st International Conference, ICCBR 2023, Aberdeen, Scotland, July 17-20, 2023, Proceedings},

volume = {14141},

pages = {53\textendash68},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {Modeling similarity measures in Case-Based Reasoning is a knowledge-intensive, demanding, and error-prone task even for domain experts. Visualizations offer support for users, but are currently only available for certain subdomains and case representations. Currently, there are only visualizations that can be used for local attributes or specific case representations. However, there is no possibility to visualize similarities between complete processes accordingly so far, although complex domains may be present. Therefore, an extension of existing approaches or the design of new suitable concepts for this application domain is necessary. The contribution of this work is to enable a more profound understanding of similarity for knowledge engineers who create a similarity model and support them in this task by using visualization methods in Process-Oriented Case-Based Reasoning (POCBR). For this purpose, we present related approaches and evaluate them against derived requirements for visualizations in POCBR. On this basis, suitable visualizations are further developed as well as new approaches designed. Three such visualizations are created: (1) a graph mapping approach, (2) a merge graph, and (3) a visualization based on heatmaps. An evaluation of these approaches has been performed based on the requirements in which the domain experts determine the graph-mapping visualization as best-suited for engineering of similarity models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Brand.2023_RLForAdaptiveWorkflows,

title = {Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows},

author = {Florian Brand and Katharina Lott and Lukas Malburg and Maximilian Hoffmann and Ralph Bergmann},

editor = {Lukas Malburg and Deepika Verma},

url = {http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the Workshops at the 31st International Conference 

 on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st 

 International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, 

 Scotland, UK, July 17, 2023},

volume = {3438},

pages = {55\textendash70},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malburg.2023_TimeSeriesInCBR,

title = {Modeling and Using Complex IoT Time Series Data in Case-Based Reasoning: 

 From Application Scenarios to Implementations},

author = {Lukas Malburg and Alexander Schultheis and Ralph Bergmann},

editor = {Lukas Malburg and Deepika Verma},

url = {http://www.wi2.uni-trier.de/shared/publications/2023_Malburg_TimeSeriesInCBR.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the Workshops at the 31st International Conference 

 on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st 

 International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, 

 Scotland, UK, July 17, 2023},

volume = {3438},

pages = {81\textendash96},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {The research area of Internet of Things (IoT) is gaining more relevance for several domains and application areas, including Case-Based Reasoning (CBR). However, IoT data is characterized by high volumes and variance of data types, making the application of CBR methods difficult. Since only few works have been published in this area so far, the integration and consideration of complex IoT data such as time series data in CBR frameworks is still in its infancy. To catch up with the current state-of-the-art, we present a comprehensive literature review on Temporal Case-Based Reasoning and time series data in CBR as part of our contribution. Furthermore, we present typical application scenarios for using IoT time series data in practice that can be addressed in further research. To build suitable CBR implementations for that purpose, we define a procedure model that can be used for time series data in CBR. In this context, we address the implementation of the application scenarios in the ProCAKE CBR framework.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malburg.2023_AdaptiveWorkflowsByCBRAndPlanning,

title = {Adaptive Management of Cyber-Physical Workflows by Means of Case-Based Reasoning and Automated Planning},

author = {Lukas Malburg and Florian Brand and Ralph Bergmann},

editor = {Tiago Prince Sales and Henderik A. Proper and Giancarlo Guizzardi and Marco Montali and Fabrizio Maria Maggi and Claudenir M. Fonseca},

url = {http://www.wi2.uni-trier.de/shared/publications/2023_EDOC_MalburgEtAl_AdaptiveWorkflows_by_CBR_and_Planning.pdf},

doi = {10.1007/978-3-031-26886-1_5},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Enterprise Design, Operations, and Computing (EDOC) Workshops 2022},

volume = {466},

pages = {79\textendash95},

publisher = {Springer.},

series = {Lecture Notes in Business Information Processing},

abstract = {Today, it is difficult for companies to react to unforeseen events, e.g., global crises. Highly standardized manufacturing processes are particularly limited in their ability to react flexibly, creating a demand for more advanced workflow management techniques, e.g., extended by artificial intelligence methods. In this paper, we describe how Case-Based Reasoning (CBR) can be combined with automated planning to enhance flexibility in cyber-physical production workflows. We present a compositional adaptation method complemented with generative adaptation to resolve unexpected situations during workflow execution. This synergy is advantageous since CBR provides specific knowledge about already experienced situations, whereas planning assists with general knowledge about the domain. In an experimental evaluation, we show that CBR offers a good basis by reusing cases and by adapting them to better suit the current problem. The combination with automated planning further improves these results and, thus, contributes to enhance the flexibility of cyber-physical workflows.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoffmann_gpu_astar_2022,

title = {GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning},

author = {Maximilian Hoffmann and Lukas Malburg and Nico Bach and Ralph Bergmann},

editor = {Mark T. Keane and Nirmalie Wiratunga},

url = {http://www.wi2.uni-trier.de/shared/publications/2022_ICCBR__A_Star_GPU.pdf},

doi = {10.1007/978-3-031-14923-8_16},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Case-Based Reasoning Research and Development - 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, Proceedings},

volume = {13405},

pages = {240\textendash255},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {In Process-Oriented Case-Based Reasoning (POCBR), determining the similarity between cases represented as semantic graphs often requires some kind of inexact graph matching, which generally is an NP-hard problem. Heuristic search algorithms such as A* search have been successfully applied for this task, but the computational performance is still a limiting factor for large case bases. As related work shows a great potential for accelerating A* search by using GPUs, we propose a novel approach called AMonG for efficiently computing graph similarities with an A* graph matching process involving GPU computing. The three-phased matching process distributes the search process over multiple search instances running in parallel on the GPU. We develop and examine different strategies within these phases that allow to customize the matching process adjusted to the problem situation to be solved. The experimental evaluation compares the proposed GPU-based approach with a pure CPU-based one. The results clearly demonstrate that the GPU-based approach significantly outperforms the CPU-based approach in a retrieval scenario, leading to an average speedup factor of 16.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hoffmann.2022_ProGAN,

title = {ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks},

author = {Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},

editor = {Andrea Marrella and Barbara Weber},

url = {http://www.wi2.uni-trier.de/shared/publications/2022_AI4BPM_HoffmannEtal_ProGAN.pdf},

doi = {10.1007/978-3-030-94343-1_4},

year  = {2022},

date = {2022-01-01},

urldate = {2021-01-01},

booktitle = {Business Process Management Workshops - BPM 2021, Rome, Italy, September 6 - 10, 2021},

volume = {436},

pages = {43\textendash55},

publisher = {Springer.},

series = {Lecture Notes in Business Information Processing},

abstract = {Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kumar_dependencyretrieval_2022,

title = {Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning},

author = {Rahol Kumar and Alexander Schultheis and Lukas Malburg and Maximilian Hoffmann and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/shared/publications/2022_Kumar_FLAIRS.pdf},

doi = {10.32473/flairs.v35i.130680},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA},

abstract = {In Case-Based Reasoning (CBR), knowledge gained from previously experienced problem-solving situations is stored as cases that can be used to solve similar upcoming problems. Although these cases act as independent knowledge entities, dependencies between cases are common in real-world scenarios, despite being only rarely considered during case retrieval or other CBR phases. In this paper, we introduce so-called inter-case dependencies, which are considered in the context of Process-Oriented CBR (POCBR). Therefore, we 1) derive requirements that must be satisfied for considering dependencies during the retrieval phase, 2) analyze which knowledge representations are suitable for representing dependencies between cases, and, 3) present our approach for Dependency-Guided Retrieval (DGR) that considers these dependencies between cases during the retrieval phase. In the experimental evaluation, the proposed DGR approach is compared to a regular CBR approach in case retrieval scenarios from the cooking domain. The results demonstrate that the use of the DGR approach leads to significantly reduced times for human problem-solving compared to regular CBR.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malburg_AdaptiveWorkflowManagement_2022,

title = {Towards Adaptive Workflow Management by Case-Based Reasoning and Automated Planning},

author = {Lukas Malburg and Ralph Bergmann},

editor = {Pascal Reuss and Jakob Sch\"{o}nborn},

url = {http://www.wi2.uni-trier.de/shared/publications/2023_Malburg_ICCBR.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {Workshops Proceedings for the Thirtieth International Conference on Case-Based Reasoning co-located with the Thirtieth International Conference on Case-Based Reasoning (ICCBR 2022), Nancy, France, September 12-15, 2022},

volume = {3389},

pages = {211\textendash220},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {Adaptive workflow management is an important topic in recent years, as increasing dynamics due to growing customer demands and faster changing market conditions require more flexibility in workflows. This is especially the case for rigid and rather standardized production processes that cannot be easily modified, if, for example, a breakdown occurs in one of the production lines. The goal of the Fourth Industrial Revolution (Industry 4.0), is to provide, among others, more flexible and cost-effective processes in companies by using Artificial Intelligence (AI) methods. In this paper, we present 1) a framework for adaptive workflow management for IoT-enhanced manufacturing processes and 2) an idea for a new adaptation method that combines Case-Based Reasoning (CBR) and automated planning. In this context, we discuss the benefits of such a synergistic combination and introduce the framework and the individual phases according to the 4R (Retrieve, Reuse, Revise, Retain) CBR cycle. In addition, we present our physical factory simulation model that can be used to evaluate the suitability of developed research artifacts.},

note = {Accepted for Publication.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{malburg_gpuretrieval_2021,

title = {Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning},

author = {Lukas Malburg and Maximilian Hoffmann and Simon Trumm and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf},

doi = {10.32473/flairs.v34i1.128345},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA},

abstract = {The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.},

note = {Best Student Paper.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{malburg_BPMResearch_2020,

title = {Using Physical Factory Simulation Models for Business Process Management Research},

author = {Lukas Malburg and Ronny Seiger and Ralph Bergmann and Barbara Weber},

editor = {Adela del-R\'{i}o-Ortega and Henrik Leopold and Flavia M. Santoro},

url = {http://www.wi2.uni-trier.de/shared/publications/2020_MalburgEtAl_BPM.pdf},

doi = {10.1007/978-3-030-66498-5_8},

year  = {2020},

date = {2020-01-01},

booktitle = {Business Process Management Workshops - BPM 2020 International Workshops, Sevilla, Spain, September 13 - 18, 2020},

volume = {397},

pages = {95\textendash107},

publisher = {Springer.},

series = {Lecture Notes in Business Information Processing},

abstract = {The production and manufacturing industries are currently transitioning towards more autonomous and intelligent production lines within the Fourth Industrial Revolution (Industry 4.0). Learning Factories as small scale physical models of real shop floors are realistic platforms to conduct research in the smart manufacturing area without depending on expensive real world production lines or completely simulated data. In this work, we propose to use learning factories for conducting research in the context of Business Process Management (BPM) and Internet of Things (IoT) as this combination promises to be mutually beneficial for both research areas. We introduce our physical Fischertechnik factory models simulating a complex production line and three exemplary use cases of combining BPM and IoT, namely the implementation of a BPM abstraction stack on top of a learning factory, the experience-based adaptation and optimization of manufacturing processes, and the stream processing-based conformance checking of IoT-enabled processes.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoffmann_graph_embedding_2020,

title = {Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning},

author = {Maximilian Hoffmann and Lukas Malburg and Patrick Klein and Ralph Bergmann},

editor = {Ian Watson and Rosina O. Weber},

url = {http://www.wi2.uni-trier.de/shared/publications/2020_ICCBR__Workflow_Graph_Embedding.pdf},

doi = {10.1007/978-3-030-58342-2_15},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {Case-Based Reasoning Research and Development - 28th International Conference, ICCBR 2020, Salamanca, Spain, June 8-12, 2020, Proceedings},

volume = {12311},

pages = {229\textendash244},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {Similarity-based retrieval of semantic graphs is widely used in real-world scenarios, e.g., in the domain of business workflows. To tackle the problem of complex and time-consuming graph similarity computations during retrieval, the MAC/FAC approach is used in Process- Oriented Case-Based Reasoning (POCBR), where similar graphs are extracted from a preselected set of candidate graphs. These graphs result from a similarity computation with a computationally inexpensive similarity measure. The contribution of this paper is a novel similarity measure where vector space embeddings generated by two siamese Graph Neural Networks (GNNs) are used to approximate the similarities of a precise but therefore computationally complex graph similarity measure. This includes a special scheme for encoding semantic graphs to be used in the neural networks. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of the graph similarity measure. The results show great potential of the approach for being used in a MAC/FAC scenario, either as a preselection model or as an approximation of the graph similarity measure.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{malburg_SemanticWebServices_2020,

title = {Semantic Web Services for AI-Research with Physical Factory Simulation Models in Industry 4.0},

author = {Lukas Malburg and Patrick Klein and Ralph Bergmann},

editor = {Herv\'{e} Panetto and Kurosh Madani and Alexander V. Smirnov},

url = {http://www.wi2.uni-trier.de/shared/publications/2020_IN4PL_Semantic_Web_Services_MalburgEtAl.pdf},

doi = {10.5220/0010135900320043},

isbn = {978-989-758-476-3},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2020, Budapest, Hungary, November 2-4, 2020},

pages = {32\textendash43},

publisher = {SCITEPRESS.},

abstract = {The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research with physical factory simulation models in Industry 4.0. Therefore, we developed 68 semantic web services based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a state-of-the-art workflow management system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{klein_ftonto_2019,

title = {FTOnto: A Domain Ontology for a Fischertechnik Simulation Production Factory by Reusing Existing Ontologies},

author = {Patrick Klein and Lukas Malburg and Ralph Bergmann},

editor = {Robert J\"{a}schke and Matthias Weidlich},

url = {http://www.wi2.uni-trier.de/shared/publications/2019_KleinMalburgBergmann_LWDA.pdf},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", Berlin, Germany, September 30 - October 2, 2019.},

volume = {2454},

pages = {253\textendash264},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {Nowadays, semantic information provided by an ontology is indispensable in the context of Industry 4.0, especially when using methods from Artificial Intelligence. The currently available ontologies do not satisfy the demands of simulation environments used for research purposes. For this reason, we develop an ontology customized to Fischertechnik simulation factories by reusing existing ontologies. The ontology has been created according to requirements from two use cases. In our evaluation, it is determined that the ontology is suitable to represent machine components and their relationships while satisfying the specified requirements.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_proCAKE_demo_2019,

title = {ProCAKE: A Process-Oriented Case-Based Reasoning Framework},

author = {Ralph Bergmann and Lisa Grumbach and Lukas Malburg and Christian Zeyen},

editor = {Stelios Kapetanakis and Hayley Borck},

url = {http://www.wi2.uni-trier.de/shared/publications/2019_BergmannGrumbachMalburgZeyen_ICCBR_Demo.pdf},

year  = {2019},

date = {2019-01-01},

booktitle = {Workshops Proceedings for the Twenty-seventh International Conference on Case-Based Reasoning co-located with the Twenty-seventh International Conference on Case-Based Reasoning (ICCBR 2019), Otzenhausen, Germany, September 8-12, 2019},

volume = {2567},

pages = {156\textendash161},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {This paper presents ProCAKE \textendash the process-oriented case-based knowledge engine of the CAKE framework, which has evolved from several research projects at the University of Trier over the years. ProCAKE constitutes a domain-independent framework that can be used to implement diverse structural or process-oriented case-based reasoning applications for integrated process and knowledge management. This paper gives an overview of the main components and demonstrates their application by examples.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{klein_workflow_embedding_2019,

title = {Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning},

author = {Patrick Klein and Lukas Malburg and Ralph Bergmann},

editor = {Kerstin Bach and Cindy Marling},

url = {http://www.wi2.uni-trier.de/shared/publications/2019_KleinMalburgBergmann_ICCBR.pdf},

doi = {10.1007/978-3-030-29249-2_13},

year  = {2019},

date = {2019-01-01},

booktitle = {Case-Based Reasoning Research and Development: 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8-12, 2019, Proceedings},

pages = {188\textendash203},

publisher = {Springer.},

series = {Lecture Notes in Computer Science},

abstract = {In process-oriented case-based reasoning, similarity-based retrieval of workflow cases from large case bases is still a difficult issue due to the computationally expensive similarity assessment. The two-phase MAC/FAC (“Many are called, but few are chosen") retrieval has been proven useful to reduce the retrieval time but comes at the cost of an additional modeling effort for implementing the MAC phase. In this paper, we present a new approach to implement the MAC phase for POCBR retrieval, which makes use of the StarSpace embedding algorithm to automatically learn a vector representation for workflows, which can be used to significantly speed-up the MAC retrieval phase. In an experimental evaluation in the domain of cooking workflows, we show that the presented approach outperforms two existing MAC/FAC approaches on the same data.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zeyen_scientificWF_adaptation_2019,

title = {Adaptation of Scientific Workflows by Means of Process-Oriented Case-Based Reasoning},

author = {Christian Zeyen and Lukas Malburg and Ralph Bergmann},

editor = {Kerstin Bach and Cindy Marling},

url = {http://www.wi2.uni-trier.de/shared/publications/2019_ZeyenMalburgBergmann_ICCBR.pdf},

doi = {10.1007/978-3-030-29249-2_26},

year  = {2019},

date = {2019-01-01},

booktitle = {Case-Based Reasoning Research and Development: 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8-12, 2019, Proceedings},

pages = {388\textendash403},

publisher = {Springer},

series = {Lecture Notes in Artificial Intelligence},

crossref = {DBLP:conf/iccbr/2019},

abstract = {This paper investigates automatic adaptation of scientific workflows in process-oriented case-based reasoning with the goal of providing modeling assistance. With regard to our previous work on the adaptation of business workflows, we discuss the differences between the workflow types and the implications for transferring the approaches to scientific workflows. An experimental evaluation with RapidMiner workflows demonstrates that the approaches can significantly improve workflows towards a given query while mostly maintaining their executability and semantic correctness.},

note = {The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{malburg_workflow_reuse_2018,

title = {Query Model and Similarity-Based Retrieval for Workflow Reuse in the Digital Humanities},

author = {Lukas Malburg and Nicolas M\"{u}nster and Christian Zeyen and Ralph Bergmann},

editor = {Rainer Gemulla and Simone Paolo Ponzetto and Christian Bizer and Margret Keuper and Heiner Stuckenschmidt},

url = {http://www.wi2.uni-trier.de/publications/2018_MalburgMuensterZeyenBergmann_LWDA.pdf},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", LWDA 2018},

volume = {2191},

pages = {251\textendash262},

publisher = {CEUR-WS.org},

abstract = {Scientific Workflows do not seem to be broadly used today in the Digital Humanities to perform text and data analysis. Although they have become established in e-Science, modeling new workflows is usually a demanding task, especially for novice users. Case-Based Reasoning (CBR) has been applied in the past to support the development of workflows as an experience-based activity by retrieving past workflows. A query language is needed for this purpose, but current languages do not sufficiently consider different user groups and the information they can provide. To address this issue, we present a query model to support novice as well as experienced users. We identify common expression elements from literature and integrate them in a prototypical CBR application named Reuse Assistant to support workflow reuse in the RapidMiner workflow tool. An experimental evaluation with non-expert users indicates the potential of the Reuse Assistant to facilitate workflow reuse and thus to simplify workflow development.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}