Publications

@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},

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

volume = {14775},

pages = {50–66},

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 = {{Process-Oriented Case-Based Reasoning, Adaptation Operators}, Adaptive Workflow Management, Drools Rule Engine, Rule-Based Adaptation},

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},

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), Mérida, 

 Mexico, July 1, 2024},

volume = {3708},

pages = {3–18},

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 = {{Case-Based Reasoning, Knowledge Acquisition Bottleneck, Knowledge Engineering, Large Language Models, Prompting}},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Schultheis.2024,

title = {Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance},

author = {Alexander Schultheis},

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

year  = {2024},

date = {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), Merida, 

 Mexico, July 1, 2024},

volume = {3708},

pages = {230–235},

publisher = {CEUR-WS.org.},

series = {CEUR Workshop Proceedings},

abstract = {Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer. 

 While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.},

keywords = {{Temporal Case-Based Reasoning, Explainable Case-Based Reasoning, Hybrid Case-Based Reasoning, Internet of Things, Predictive Maintenance}, Time Series Data},

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 Grüger and Justin Weich and Yannis Bertrand and Ralph Bergmann and Estefanía 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},

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

volume = {14775},

pages = {240–257},

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 = {{Temporal Case-Based Reasoning, Data Quality Issues}, Time Series Data, Time Series Similarity Measures},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoffmann_ltr_2023,

title = {Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning},

author = {Maximilian Hoffmann and Ralph Bergmann},

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

doi = {10.32473/flairs.36.133039},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2023, Clearwater Beach, Florida, USA},

abstract = {In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pauli_transfer_learning_2023,

title = {Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning},

author = {Johannes Pauli and Maximilian Hoffmann and Ralph Bergmann},

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

doi = {10.32473/flairs.36.133040},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2023, Clearwater Beach, Florida, USA},

abstract = {Similarity-based retrieval of semantic graphs is a crucial task of Process-Oriented Case-Based Reasoning (POCBR) that is usually complex and time-consuming, as it requires some kind of inexact graph matching. Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. The experimental evaluation examines the quality and performance of the models based on TL compared to the GNN models from previous work for three semantic graph domains with various properties. The results show the great potential of the proposed approach for reducing the similarity prediction error and the training time.},

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},

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 = {81–96},

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 = {{Case-Based Reasoning, Internet of Things, ProCAKE}, Temporal Case-Based Reasoning, Time Series Data},

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 = {53–68},

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 = {Explainable Case-Based Reasoning, Explanation, Process-Oriented Case-Based Reasoning, Similarity, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SchultheisZB2023,

title = {An Overview and Comparison of Case-Based Reasoning Frameworks},

author = {Alexander Schultheis and Christian Zeyen and Ralph Bergmann},

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

doi = {10.1007/978-3-031-40177-0_21},

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 = {327–343},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {Case-Based Reasoning (CBR) is a methodology with many applications in industrial and scientific domains. Over the past decades, various frameworks have been developed to facilitate the development of CBR applications. For practitioners and researchers, it is challenging to overview the landscape of existing frameworks with their specific scope and features. This makes it difficult to choose the most suitable framework for specific requirements. To address this issue, this work provides an overview and comparison of CBR frameworks, focusing on five recent, open-source CBR frameworks: CloodCBR, eXiT*CBR, jColibri, myCBR, and ProCAKE. They are compared by supported CBR types, knowledge containers, CBR phases, interfaces, and special features.},

keywords = {Case-Based Reasoning, CBR Applications, CBR Framework, CloodCBR, eXiT*CBR, jColibri, myCBR, ProCAKE},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zeyen_astar_2020,

title = {A*-Based Similarity Assessment of Semantic Graphs},

author = {Christian Zeyen and Ralph Bergmann},

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

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

doi = {10.1007/978-3-030-58342-2_2},

year  = {2020},

date = {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 = {17–32},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{schake_2020,

title = {A Time-Series Similarity Measure for Case-Based Deviation Management to Support Flexible Workflow Execution},

author = {Erik Schake and Lisa Grumbach and Ralph Bergmann},

editor = {Ian Watson and Rosina Weber},

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

year  = {2020},

date = {2020-01-01},

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

publisher = {Springer},

series = {Lecture Notes in Artificial Intelligence},

note = {Accepted for publication. The original publication will be available at www.springerlink.com},

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 = {156–161},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {This paper presents ProCAKE – 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 = {{Knowledge Management, Case-Based Reasoning}, Process Management},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{muller_cookingcake:_2015,

title = {CookingCAKE: A Framework for the adaptation of cooking recipes represented as workflows},

author = {Gilbert Müller and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/publications/2015_MuellerBergmannCCC.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Computer Cooking Contest, Workshop Proceedings ICCBR 2015},

address = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_collaborative_2014,

title = {The Collaborative Agile Knowledge Engine CAKE},

author = {Ralph Bergmann and Sarah Gessinger and Sebastian Görg and Gilbert Müller},

url = {http://doi.acm.org/10.1145/2660398.2663771},

doi = {10.1145/2660398.2663771},

isbn = {978-1-4503-3043-5},

year  = {2014},

date = {2014-01-01},

urldate = {2014-12-11},

booktitle = {Proceedings of the 18th International Conference on Supporting Group Work},

pages = {281–284},

publisher = {ACM},

address = {New York, NY, USA},

series = {GROUP '14},

abstract = {The Collaborative Agile Knowledge Engine (CAKE) is a prototypical generic software system for integrated process and knowledge management. CAKE integrates recent research results on agile workflows, process-oriented case-based reasoning, and web technologies into a common platform that can be configured to different application domains and needs. We describe the main concepts and the architecture of CAKE and sketch three example applications.},

keywords = {agile workflows, Case-Based Reasoning, Knowledge Management, Process Management},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{muller_compositional_2014,

title = {Compositional Adaptation of Cooking Recipes using Workflow Streams},

author = {Gilbert Müller and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannCCC.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {Computer Cooking Contest, Workshop Proceedings ICCBR 2014},

address = {Springer},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{muller_workflow_2014,

title = {Workflow Streams: A Means for Compositional Adaptation in Process-Oriented CBR},

author = {Gilbert Müller and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannICCBR.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {Case-Based Reasoning Research and Development, 22th International Conference on Case-Based Reasoning, ICCBR 2014},

address = {Springer},

note = {Best phd student paper award. The original publication is available at www.springerlink.com},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{muller_cluster-based_2014,

title = {A Cluster-Based Approach to Improve Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning},

author = {Gilbert Müller and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/publications/2014_MuellerBergmannECAI.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {20th European Conference on Artificial Intelligence (ECAI 2014)},

address = {IOS Press},

note = {Preprint of this article available for download.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gorg_realtime_2013,

title = {Real-Time Collaboration and Experience Reuse For Cloud - Based Workflow Management Systems},

author = {Sebastian Görg and Ralph Bergmann and Sarah Gessinger and Mirjam Minor},

url = {http://www.wi2.uni-trier.de/publications/2013_SBM_Workshop_GoergEtAl.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Workshop on Social Business Process Management (SBM 2013)},

publisher = {IEEE Computer Society Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_workflow_2013,

title = {Workflow Clustering Using Semantic Similarity Measures},

author = {Ralph Bergmann and Gilbert Müller and Daniel Wittkowsky},

editor = {Ingo Timm},

url = {http://www.wi2.uni-trier.de/publications/2013_BergmannMuellerWittkowskyKI.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {KI 2013: Advances in Artificial Intelligence},

publisher = {Springer},

address = {Koblenz},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_mac/fac_2013,

title = {MAC/FAC Retrieval of Semantic Workflows},

author = {Ralph Bergmann and Alexander Stromer},

url = {http://www.wi2.uni-trier.de/publications/2013_FLAIRS_BergmannStromer.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, St. Pete Beach, May 22-25, 2013.},

pages = {357 – 362},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_ralph_scaling_2012,

title = {Scaling similarity-based retrieval of semantic workflows},

author = {Ralph Bergmann and Mirjam Minor and Siblee Islam and Pol Schumacher and Alexander Stromer},

editor = {Luc Lamontagne and Juan A. Recio-Garcia},

url = {http://www.wi2.uni-trier.de/publications/2012_ICCBR_POCBR_Bergmann_etal.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {ICCBR-Workshop on Process-oriented Case-Based Reasoning},

pages = {15–24},

address = {Lyon},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{schumacher_extraction_2012,

title = {Extraction of Procedural Knowledge from the Web},

author = {Pol Schumacher and Mirjam Minor and Kirstin Walter and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/publications/2012_WWW_SchumacherEtal.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {Workshop Proceedings: WWW'12},

address = {Lyon, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{minor_adaptive_2011,

title = {Adaptive Workflow Management in the Cloud -Towards a Novel Platform as a Service},

author = {Mirjam Minor and Ralph Bergmann and Sebastian Görg},

url = {http://www.wi2.uni-trier.de/publications/2011_POCBR_MinorEtal.pdf},

year  = {2011},

date = {2011-01-01},

booktitle = {Proceedings of the ICCBR 2011 Workshops},

pages = {131–138},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{minor_reasoning_2011,

title = {Reasoning on Business Processes to Support Change Reuse},

author = {Mirjam Minor and Ralph Bergmann and Sebastian Görg and Kirstin Walter},

url = {http://www.wi2.uni-trier.de/publications/2011_CEC11_Minor_etal},

isbn = {978-1-4577-1542-6},

year  = {2011},

date = {2011-01-01},

booktitle = {13th IEEE Conference on Commerce and Enterprise Computing, CEC 2011, Luxembourg-Kirchberg, Luxembourg, September 5-7, 2011},

pages = {18–25},

publisher = {IEEE Computer Society},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_retrieval_2011,

title = {Retrieval of Semantic Workfows with Knowledge Intensive Similarity Measures},

author = {Ralph Bergmann and Yolanda Gil},

url = {http://www.wi2.uni-trier.de/publications/2011_BergmannGilICCBR2011CRC.pdf},

year  = {2011},

date = {2011-01-01},

booktitle = {Case-Based Reasoning. Research and Development, 19th International Conference on Case-Based Reasoning, ICCBR 2011},

volume = {6880},

pages = {17–31},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{minor_towards_2010,

title = {Towards Case-Based Adaptation of Workflows},

author = {Mirjam Minor and Ralph Bergmann and Sebastian Görg and Kirstin Walter},

editor = {Stefania Montani and Isabelle Bichindaritz},

url = {http://www.wi2.uni-trier.de/publications/2010_MinorEtalICCBR.pdf},

year  = {2010},

date = {2010-01-01},

booktitle = {Case-Based Reasoning. Research and Development, 18th International Conference on Case-Based Reasoning, ICCBR 2010, Alessandria, Italy, July 19-22, 2010. Proceedings},

pages = {421–435},

publisher = {Springer},

series = {LNAI 6176},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{minor_xml-based_2008,

title = {XML-based Representation of Agile Workflows},

author = {Mirjam Minor and Daniel Schmalen and Ralph Bergmann},

editor = {Martin Bichler and Thomas Hess and Helmut Krcmar and Ulrike Lechner and Florian Matthes and Arnold Picot and Benjamin Speitkamp and Petra Wolf},

url = {http://www.wi2.uni-trier.de/publications/2008_MKWI_MinorEtal.pdf},

year  = {2008},

date = {2008-01-01},

booktitle = {Multikonferenz Wirtschaftsinformatik 2008},

pages = {439–440},

publisher = {GITO-Verlag Berlin},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{minor_introspection_2008,

title = {Introspection into an Agile Workflow Engine for Long-Term Processes},

author = {Mirjam Minor and Daniel Schmalen and Jakob Weidlich and Andreas Koldehoff},

url = {http://www.wi2.uni-trier.de/publications/2008_WETICE_MinorEtal.pdf},

isbn = {978-0-7695-3315-5},

year  = {2008},

date = {2008-01-01},

booktitle = {17th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, WETICE 2008, Rome, Italy, June 23-25, 2008, Proceedings},

pages = {241–246},

publisher = {IEEE Computer Society},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sauer_agent-oriented_2008,

title = {An Agent-Oriented System for Workflow Enactment Tracking},

author = {Thomas Sauer and Mirjam Minor and Sascha Werno},

url = {http://www.wi2.uni-trier.de/publications/2008_WETICE08.pdf},

doi = {10.1109/WETICE.2008.28},

isbn = {978-0-7695-3315-5},

year  = {2008},

date = {2008-01-01},

booktitle = {Proceedings of the 2008 IEEE 17th Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises},

pages = {235–240},

publisher = {IEEE Computer Society},

address = {Washington, DC, USA},

keywords = {Multi-Agent Systems, Workflow Enactment Tracking, Workflow Management},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bergmann_case-based_2006,

title = {Case-Based Support for Collaborative Business},

author = {Ralph Bergmann and Andrea Freßmann and Kerstin Maximini and Rainer Maximini and Thomas Sauer},

editor = {Thomas Roth-Berghofer and Mehmet H. Göker and H. Altay Güvenir},

url = {http://www.wi2.uni-trier.de/publications/2006_ECCBR_bergmann.pdf},

isbn = {3-540-36843-4},

year  = {2006},

date = {2006-01-01},

booktitle = {Advances in Case-Based Reasoning, 8th European Conference, ECCBR 2006, Fethiye, Turkey, September 4-7, 2006, Proceedings},

volume = {4106},

pages = {519–533},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fresmann_collaborative_2005,

title = {Collaborative Agent-Based Knowledge Support for Empirical and Knowledge-Intense Processes},

author = {Andrea Freßmann and Kerstin Maximini and Rainer Maximini and Thomas Sauer},

editor = {Torsten Eymann and Franziska Klügl and Winfried Lamersdorf and Matthias Klusch and Michael N. Huhns},

url = {http://www.wi2.uni-trier.de/publications/2005_MATES_fressmann.pdf},

isbn = {3-540-28740-X},

year  = {2005},

date = {2005-01-01},

booktitle = {Multiagent System Technologies, Third German Conference, MATES 2005, Koblenz, Germany, September 11-13, 2005, Proceedings},

volume = {3550},

pages = {235–236},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fresmann_cbr-based_2005,

title = {CBR-based Execution and Planning Support for Collaborative Workflows},

author = {Andrea Freßmann and Kerstin Maximini and Rainer Maximini and Thomas Sauer},

editor = {Stefanie Brüninghaus},

url = {http://www.wi2.uni-trier.de/publications/2005_ICCBR05_fressmann.pdf},

year  = {2005},

date = {2005-01-01},

booktitle = {6th International Conference on Case-Based Reasoning, ICCBR 2005, Chicago, IL, USA, August 23-26, 2005, Workshop Proceedings},

pages = {271–280},

publisher = {Springer},

note = {3075},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}