@inproceedings{malburg_gpuretrieval_2021,
	title        = {{Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning}},
	author       = {Malburg, Lukas and Hoffmann, Maximilian and Trumm, Simon and Bergmann, Ralph},
	year         = 2021,
	booktitle    = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA},
	doi          = {10.32473/flairs.v34i1.128345},
	url          = {https://doi.org/10.32473/flairs.v34i1.128345},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf},
	note         = {Best Student Paper.},
	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.}
}

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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.","bibtex":"@inproceedings{malburg_gpuretrieval_2021,\n\ttitle = {{Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning}},\n\tauthor = {Malburg, Lukas and Hoffmann, Maximilian and Trumm, Simon and Bergmann, Ralph},\n\tyear = 2021,\n\tbooktitle = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA},\n\tdoi = {10.32473/flairs.v34i1.128345},\n\turl = {https://doi.org/10.32473/flairs.v34i1.128345},\n\turl = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf},\n\tnote = {Best Student Paper.},\n\tabstract = {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.}\n}\n\n","author_short":["Malburg, L.","Hoffmann, M.","Trumm, S.","Bergmann, R."],"key":"malburg_gpuretrieval_2021","id":"malburg_gpuretrieval_2021","bibbaseid":"malburg-hoffmann-trumm-bergmann-improvingsimilaritybasedretrievalefficiencybyusinggraphicprocessingunitsincasebasedreasoning-2021","role":"author","urls":{"Paper":"http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf"},"metadata":{"authorlinks":{"bergmann, r":"https://bibbase.org/show?bib=www.wi2.uni-trier.de/publications/WI2Publikationen.bib&group0=year&css=www.wi2.uni-trier.de/publications/WI2.css&filter=authors:Bergmann","malburg, l":"https://bibbase.org/show?bib=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FPublicationsMalburg.bib&group0=year&css=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FWI2.css","hoffmann, m":"https://bibbase.org/show?bib=www.wi2.uni-trier.de/publications/WI2Publikationen.bib&group0=year&css=www.wi2.uni-trier.de/publications/WI2.css&filter=author_short:Hoffmann,%20M."}},"downloads":56},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/PublicationsMalburg.bib","creationDate":"2021-03-17T07:12:05.909Z","downloads":56,"keywords":[],"search_terms":["improving","similarity","based","retrieval","efficiency","using","graphic","processing","units","case","based","reasoning","malburg","hoffmann","trumm","bergmann"],"title":"Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning","year":2021,"dataSources":["HPSXnFwLpXtbkmbx7","vtdjwAo6eNiqRLfnG","CnwPa99ZchEF4SZgh","j2fC6S9dR4Pu3hmnz","nZxfXH3fRFhwWejKL","MSp3DzP4ToPojqkFy","J3orK6zvpR7d8vDmC"]}