@inproceedings{SchultheisJTSB2026,
  author       = {Alexander Schultheis and David Jilg and Ralph Traphöner and Roman Schander and Ralph Bergmann},
  title        = {{Applying Distributed Case-Based Reasoning in the Edge-Cloud Continuum}},
  booktitle    = {Artificial Intelligence {XLIII} - 46th {SGAI} International Conference on Artificial Intelligence, {AI} 2026, Cambridge, UK, December 15-17, 2026, Proceedings},
  series       = {Lecture Notes in Computer Science},
  publisher    = {Springer},
  year         = {2026},
  notes        = {{Accepted for Publication.}},
  abstract     = {Case-Based Reasoning (CBR) is well suited for recurring but context-dependent industrial decisions and is therefore a promising artificial intelligence approach for the Edge-Cloud Continuum (ECC). The ECC denotes a distributed infrastructure in which tasks are placed dynamically across resource-constrained nodes near data sources and centralized cloud resources. However, the systematic transfer of CBR to distributed edge-cloud settings has received little attention. This paper presents a project-grounded approach for operationalizing CBR in the ECC. Based on experience from the EASY project and exchanges with industry and research partners, industrial application scenarios are structured, load balancing metrics are adopted, and an architecture for distributed CBR is introduced. The paper further discusses opportunities, challenges, and cross-organizational perspectives. To demonstrate technical feasibility, a ProCAKE-based prototype is implemented and investigated in a distributed case base scenario. The results demonstrate that distributed CBR in the ECC is technically feasible, while highlighting the need to assess potential runtime benefits in relation to case base characteristics, heterogeneous processing capabilities, and distribution overhead.},
  keywords     = {Distributed Case-Based Reasoning, Edge-Cloud Continuum, Load Balancing, ProCAKE}
}

{"_id":"J8wpPxBpRLypYstnh","bibbaseid":"schultheis-jilg-traphner-schander-bergmann-applyingdistributedcasebasedreasoningintheedgecloudcontinuum-2026","author_short":["Schultheis, A.","Jilg, D.","Traphöner, R.","Schander, R.","Bergmann, R."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Alexander"],"propositions":[],"lastnames":["Schultheis"],"suffixes":[]},{"firstnames":["David"],"propositions":[],"lastnames":["Jilg"],"suffixes":[]},{"firstnames":["Ralph"],"propositions":[],"lastnames":["Traphöner"],"suffixes":[]},{"firstnames":["Roman"],"propositions":[],"lastnames":["Schander"],"suffixes":[]},{"firstnames":["Ralph"],"propositions":[],"lastnames":["Bergmann"],"suffixes":[]}],"title":"Applying Distributed Case-Based Reasoning in the Edge-Cloud Continuum","booktitle":"Artificial Intelligence XLIII - 46th SGAI International Conference on Artificial Intelligence, AI 2026, Cambridge, UK, December 15-17, 2026, Proceedings","series":"Lecture Notes in Computer Science","publisher":"Springer","year":"2026","notes":"Accepted for Publication.","abstract":"Case-Based Reasoning (CBR) is well suited for recurring but context-dependent industrial decisions and is therefore a promising artificial intelligence approach for the Edge-Cloud Continuum (ECC). The ECC denotes a distributed infrastructure in which tasks are placed dynamically across resource-constrained nodes near data sources and centralized cloud resources. However, the systematic transfer of CBR to distributed edge-cloud settings has received little attention. This paper presents a project-grounded approach for operationalizing CBR in the ECC. Based on experience from the EASY project and exchanges with industry and research partners, industrial application scenarios are structured, load balancing metrics are adopted, and an architecture for distributed CBR is introduced. The paper further discusses opportunities, challenges, and cross-organizational perspectives. To demonstrate technical feasibility, a ProCAKE-based prototype is implemented and investigated in a distributed case base scenario. The results demonstrate that distributed CBR in the ECC is technically feasible, while highlighting the need to assess potential runtime benefits in relation to case base characteristics, heterogeneous processing capabilities, and distribution overhead.","keywords":"Distributed Case-Based Reasoning, Edge-Cloud Continuum, Load Balancing, ProCAKE","bibtex":"@inproceedings{SchultheisJTSB2026,\n author = {Alexander Schultheis and David Jilg and Ralph Traphöner and Roman Schander and Ralph Bergmann},\n title = {{Applying Distributed Case-Based Reasoning in the Edge-Cloud Continuum}},\n booktitle = {Artificial Intelligence {XLIII} - 46th {SGAI} International Conference on Artificial Intelligence, {AI} 2026, Cambridge, UK, December 15-17, 2026, Proceedings},\n series = {Lecture Notes in Computer Science},\n publisher = {Springer},\n year = {2026},\n notes = {{Accepted for Publication.}},\n abstract = {Case-Based Reasoning (CBR) is well suited for recurring but context-dependent industrial decisions and is therefore a promising artificial intelligence approach for the Edge-Cloud Continuum (ECC). The ECC denotes a distributed infrastructure in which tasks are placed dynamically across resource-constrained nodes near data sources and centralized cloud resources. However, the systematic transfer of CBR to distributed edge-cloud settings has received little attention. This paper presents a project-grounded approach for operationalizing CBR in the ECC. Based on experience from the EASY project and exchanges with industry and research partners, industrial application scenarios are structured, load balancing metrics are adopted, and an architecture for distributed CBR is introduced. The paper further discusses opportunities, challenges, and cross-organizational perspectives. To demonstrate technical feasibility, a ProCAKE-based prototype is implemented and investigated in a distributed case base scenario. The results demonstrate that distributed CBR in the ECC is technically feasible, while highlighting the need to assess potential runtime benefits in relation to case base characteristics, heterogeneous processing capabilities, and distribution overhead.},\n keywords = {Distributed Case-Based Reasoning, Edge-Cloud Continuum, Load Balancing, ProCAKE}\n}\n\n","author_short":["Schultheis, A.","Jilg, D.","Traphöner, R.","Schander, R.","Bergmann, R."],"key":"SchultheisJTSB2026","id":"SchultheisJTSB2026","bibbaseid":"schultheis-jilg-traphner-schander-bergmann-applyingdistributedcasebasedreasoningintheedgecloudcontinuum-2026","role":"author","urls":{},"keyword":["Distributed Case-Based Reasoning","Edge-Cloud Continuum","Load Balancing","ProCAKE"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen_IoT.bib","dataSources":["MSp3DzP4ToPojqkFy","Td7BJ334QwxWK4vLW"],"keywords":["distributed case-based reasoning","edge-cloud continuum","load balancing","procake"],"search_terms":["applying","distributed","case","based","reasoning","edge","cloud","continuum","schultheis","jilg","traphöner","schander","bergmann"],"title":"Applying Distributed Case-Based Reasoning in the Edge-Cloud Continuum","year":2026}