{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:11:09Z","timestamp":1781482269738,"version":"3.54.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"MINECO research project","award":["TIN2017-83964-R"],"award-info":[{"award-number":["TIN2017-83964-R"]}]},{"name":"Junta de Andalucia research project","award":["P20 00809"],"award-info":[{"award-number":["P20 00809"]}]},{"name":"University of Thessaly Central Library"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Parallel Prog"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Algorithms for answering the <jats:italic>k<\/jats:italic> nearest-neighbor (<jats:italic>k<\/jats:italic>-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating feature class. GPU devices have significantly more processing cores than CPUs and faster device memory than the main memory accessed by CPUs, thus, providing higher computing power for processing demanding queries like the <jats:italic>k<\/jats:italic>-NN. However, since device and\/or main memory may not be able to host an entire, rather big, reference and query datasets, storing these datasets in a fast secondary device, like a solid state disk (SSD), and partially retrieve the required, at each stage, partitions is, in many practical cases, a feasible solution. We propose and implement the first GPU-based algorithms for processing the <jats:italic>k<\/jats:italic>-NN query for big reference and query spatial data stored on SSDs. Based on 3d synthetic and real big spatial data, we experimentally compare these algorithms and highlight the most efficient algorithmic variation. This variation utilizes a CUDA feature known as Concurrent Kernel Execution, to further improve its performance.<\/jats:p>","DOI":"10.1007\/s10766-023-00755-8","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T20:25:44Z","timestamp":1689971144000},"page":"275-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Spatial Data Using Partitioning and Concurrent Kernel Execution"],"prefix":"10.1007","volume":"51","author":[{"given":"Polychronis","family":"Velentzas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Vassilakopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Corral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Antonopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"key":"755_CR1","volume-title":"Multicore and GPU Programming: An Integrated Approach","author":"G Barlas","year":"2014","unstructured":"Barlas, G.: Multicore and GPU Programming: An Integrated Approach, 1st edn. Morgan Kaufmann, Los Altos (2014)","edition":"1"},{"key":"755_CR2","doi-asserted-by":"publisher","unstructured":"Velentzas, P., Vassilakopoulos, M., Corral, A.: GPU-based algorithms for processing the $$k$$ nearest-neighbor query on disk-resident data. In: MEDI Conference, pp. 264\u2013278 (2021). https:\/\/doi.org\/10.1007\/978-3-030-78428-7_21","DOI":"10.1007\/978-3-030-78428-7_21"},{"issue":"6","key":"755_CR3","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s10766-017-0502-5","volume":"46","author":"DP Singh","year":"2018","unstructured":"Singh, D.P., Joshi, I., Choudhary, J.: Survey of GPU based sorting algorithms. Int. J. Parallel Prog. 46(6), 1017\u20131034 (2018). https:\/\/doi.org\/10.1007\/s10766-017-0502-5","journal-title":"Int. J. Parallel Prog."},{"key":"755_CR4","doi-asserted-by":"publisher","unstructured":"Garcia, V., Debreuve, E., Barlaud, M.: Fast k nearest neighbor search using GPU. In: CVPR Workshops, pp. 1\u20136 (2008). https:\/\/doi.org\/10.1109\/CVPRW.2008.4563100","DOI":"10.1109\/CVPRW.2008.4563100"},{"key":"755_CR5","unstructured":"Kuang, Q., Zhao, L.: A practical GPU based kNN algorithm. In: SCSCT Conference, pp. 151\u2013155 (2009)"},{"key":"755_CR6","doi-asserted-by":"publisher","unstructured":"Liang, S., Wang, C., Liu, Y., Jian, L.: CUKNN: a parallel implementation of k-nearest neighbor on CUDA-enabled GPU. In: YC-ICT Conference, pp. 415\u2013418 (2009). https:\/\/doi.org\/10.1109\/YCICT.2009.5382329","DOI":"10.1109\/YCICT.2009.5382329"},{"key":"755_CR7","doi-asserted-by":"publisher","unstructured":"Garcia, V., Debreuve, E., Nielsen, F., Barlaud, M.: K-nearest neighbor search: fast GPU-based implementations and application to high-dimensional feature matching. In: ICIP Conference, pp. 3757\u20133760 (2010). https:\/\/doi.org\/10.1109\/ICIP.2010.5654017","DOI":"10.1109\/ICIP.2010.5654017"},{"key":"755_CR8","doi-asserted-by":"publisher","unstructured":"Barrientos, R.J., G\u00f3mez, J.I., Tenllado, C., Prieto-Mat\u00edas, M., Mar\u00edn, M.: kNN query processing in metric spaces using GPUs. In: Euro-Par Conference, pp. 380\u2013392 (2011). https:\/\/doi.org\/10.1007\/978-3-642-23400-2_35","DOI":"10.1007\/978-3-642-23400-2_35"},{"issue":"8","key":"755_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0044000","volume":"7","author":"AS Arefin","year":"2012","unstructured":"Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs. PLoS ONE 7(8), 1\u201313 (2012). https:\/\/doi.org\/10.1371\/journal.pone.0044000","journal-title":"PLoS ONE"},{"issue":"5","key":"755_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0092409","volume":"9","author":"I Komarov","year":"2014","unstructured":"Komarov, I., Dashti, A., D\u2019Souza, R.M.: Fast k-NNG construction with GPU-based quick multi-select. PLoS ONE 9(5), 1\u20139 (2014). https:\/\/doi.org\/10.1371\/journal.pone.0092409","journal-title":"PLoS ONE"},{"key":"755_CR11","doi-asserted-by":"publisher","unstructured":"Li, S., Amenta, N.: Brute-force k-nearest neighbors search on the GPU. In: SISAP Conference, pp. 259\u2013270 (2015). https:\/\/doi.org\/10.1007\/978-3-319-25087-8_25","DOI":"10.1007\/978-3-319-25087-8_25"},{"key":"755_CR12","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.ins.2016.08.089","volume":"373","author":"PD Guti\u00e9rrez","year":"2016","unstructured":"Guti\u00e9rrez, P.D., Lastra, M., Bacardit, J., Ben\u00edtez, J.M., Herrera, F.: GPU-SME-kNN: scalable and memory efficient kNN and lazy learning using GPUs. Inf. Sci. 373, 165\u2013182 (2016). https:\/\/doi.org\/10.1016\/j.ins.2016.08.089","journal-title":"Inf. Sci."},{"issue":"10","key":"755_CR13","doi-asserted-by":"publisher","first-page":"4611","DOI":"10.1007\/s11227-017-2110-y","volume":"73","author":"RJ Barrientos","year":"2017","unstructured":"Barrientos, R.J., Millaguir, F., S\u00e1nchez, J.L., Arias, E.: GPU-based exhaustive algorithms processing kNN queries. J. Supercomput. 73(10), 4611\u20134634 (2017). https:\/\/doi.org\/10.1007\/s11227-017-2110-y","journal-title":"J. Supercomput."},{"key":"755_CR14","doi-asserted-by":"publisher","unstructured":"Riquelme, J.A., Barrientos, R.J., Hern\u00e1ndez-Garc\u00eda, R., Navarro, C.A.: An exhaustive algorithm based on GPU to process a kNN query. In: SCCC Conference, pp. 1\u20138 (2020). https:\/\/doi.org\/10.1109\/SCCC51225.2020.9281231","DOI":"10.1109\/SCCC51225.2020.9281231"},{"issue":"2","key":"755_CR15","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1007\/s11227-021-03975-2","volume":"78","author":"RJ Barrientos","year":"2022","unstructured":"Barrientos, R.J., Riquelme, J.A., Navarro, R.H.-G.C.A., Soto-Silva, W.: Fast kNN query processing over a multi-node GPU environment. J. Supercomput. 78(2), 3045\u20133071 (2022). https:\/\/doi.org\/10.1007\/s11227-021-03975-2","journal-title":"J. Supercomput."},{"key":"755_CR16","doi-asserted-by":"publisher","unstructured":"Velentzas, P., Vassilakopoulos, M., Corral, A.: In-memory k nearest neighbor GPU-based query processing. In: GISTAM Conference, pp. 310\u2013317 (2020). https:\/\/doi.org\/10.5220\/0009781903100317","DOI":"10.5220\/0009781903100317"},{"key":"755_CR17","series-title":"Morgan Kaufmann Series in Data Management Systems","volume-title":"Foundations of Multidimensional and Metric Data Structures","author":"H Samet","year":"2006","unstructured":"Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Series in Data Management Systems, Academic Press, London (2006)"},{"issue":"9","key":"755_CR18","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509\u2013517 (1975). https:\/\/doi.org\/10.1145\/361002.361007","journal-title":"Commun. ACM"},{"issue":"5","key":"755_CR19","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1145\/1409060.1409079","volume":"27","author":"K Zhou","year":"2008","unstructured":"Zhou, K., Hou, Q., Wang, R., Guo, B.: Real-time kd-tree construction on graphics hardware. ACM Trans. Graph. 27(5), 126 (2008). https:\/\/doi.org\/10.1145\/1409060.1409079","journal-title":"ACM Trans. Graph."},{"key":"755_CR20","unstructured":"Gieseke, F., Heinermann, J., Oancea, C.E., Igel, C.: Buffer k-d trees: processing massive nearest neighbor queries on GPUs. In: ICML Conference, pp. 172\u2013180 (2014)"},{"issue":"3","key":"755_CR21","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s10766-011-0184-3","volume":"40","author":"PJS Leite","year":"2012","unstructured":"Leite, P.J.S., Teixeira, J.M.X.N., Farias, T.S.M.C., Reis, B., Teichrieb, V., Kelner, J.: Nearest neighbor searches on the GPU\u2014a massively parallel approach for dynamic point clouds. Int. J. Parallel Prog. 40(3), 313\u2013330 (2012). https:\/\/doi.org\/10.1007\/s10766-011-0184-3","journal-title":"Int. J. Parallel Prog."},{"issue":"1","key":"755_CR22","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1186\/s40064-016-3035-2","volume":"5","author":"G Mei","year":"2016","unstructured":"Mei, G., Xu, N., Xu, L.: Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search. Springerplus 5(1), 1389 (2016). https:\/\/doi.org\/10.1186\/s40064-016-3035-2","journal-title":"Springerplus"},{"key":"755_CR23","doi-asserted-by":"publisher","unstructured":"Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD Conference, pp. 47\u201357 (1984). https:\/\/doi.org\/10.1145\/602259.602266","DOI":"10.1145\/602259.602266"},{"key":"755_CR24","doi-asserted-by":"publisher","unstructured":"You, S., Zhang, J., Gruenwald, L.: Parallel spatial query processing on GPUs using r-trees. In: BigSpatial@SIGSPATIAL Workshop, pp. 23\u201331 (2013). https:\/\/doi.org\/10.1145\/2534921.2534949","DOI":"10.1145\/2534921.2534949"},{"key":"755_CR25","doi-asserted-by":"publisher","unstructured":"Nam, M., Kim, J., Nam, B.: Parallel tree traversal for nearest neighbor query on the GPU. In: ICPP Conference, pp. 113\u2013122 (2016). https:\/\/doi.org\/10.1109\/ICPP.2016.20","DOI":"10.1109\/ICPP.2016.20"},{"key":"755_CR26","doi-asserted-by":"publisher","unstructured":"White, D.A., Jain, R.C.: Similarity indexing with the SS-tree. In: ICDE Conference, pp. 516\u2013523 (1996). https:\/\/doi.org\/10.1109\/ICDE.1996.492202","DOI":"10.1109\/ICDE.1996.492202"},{"key":"755_CR27","unstructured":"Aji, A., Vo, H., Wang, F.: Effective spatial data partitioning for scalable query processing. CoRR 1\u201312 (2015). arXiv:1509.00910"},{"key":"755_CR28","doi-asserted-by":"publisher","unstructured":"Velentzas, P., Vassilakopoulos, M., Corral, A.: A partitioning GPU-based algorithm for processing the k nearest-neighbor query. In: MEDES Conference, pp. 2\u20139 (2020). https:\/\/doi.org\/10.1145\/3415958.3433071","DOI":"10.1145\/3415958.3433071"},{"issue":"3","key":"755_CR29","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2021","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535\u2013547 (2021). https:\/\/doi.org\/10.1109\/TBDATA.2019.2921572","journal-title":"IEEE Trans. Big Data"},{"key":"755_CR30","doi-asserted-by":"publisher","unstructured":"Wang, L., Huang, M., El-Ghazawi, T.A.: Exploiting concurrent kernel execution on graphic processing units. In: HPCS Conference, pp. 24\u201332 (2011). https:\/\/doi.org\/10.1109\/HPCSim.2011.5999803","DOI":"10.1109\/HPCSim.2011.5999803"},{"key":"755_CR31","doi-asserted-by":"publisher","unstructured":"Wende, F., Cordes, F., Steinke, T.: On improving the performance of multi-threaded CUDA applications with concurrent kernel execution by kernel reordering. In: SAAHPC Conference, pp. 74\u201383 (2012). https:\/\/doi.org\/10.1109\/SAAHPC.2012.12","DOI":"10.1109\/SAAHPC.2012.12"},{"key":"755_CR32","doi-asserted-by":"publisher","unstructured":"Jiao, Q., Lu, M., Huynh, H.P., Mitra, T.: Improving GPGPU energy-efficiency through concurrent kernel execution and DVFS. In: CGO Conference, pp. 1\u201311 (2015). https:\/\/doi.org\/10.1109\/CGO.2015.7054182","DOI":"10.1109\/CGO.2015.7054182"},{"key":"755_CR33","doi-asserted-by":"publisher","unstructured":"Dai, H., Lin, Z., Li, C., Zhao, C., Wang, F., Zheng, N., Zhou, H.: Accelerate GPU concurrent kernel execution by mitigating memory pipeline stalls. In: HPCA Conference, pp. 208\u2013220 (2018). https:\/\/doi.org\/10.1109\/HPCA.2018.00027","DOI":"10.1109\/HPCA.2018.00027"},{"issue":"3","key":"755_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/3326124","volume":"16","author":"Z Lin","year":"2019","unstructured":"Lin, Z., Dai, H., Mantor, M., Zhou, H.: Coordinated CTA combination and bandwidth partitioning for GPU concurrent kernel execution. ACM Trans. Archit. Code Optim. 16(3), 23\u201312327 (2019). https:\/\/doi.org\/10.1145\/3326124","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"755_CR35","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.future.2020.05.023","volume":"112","author":"C Zhao","year":"2020","unstructured":"Zhao, C., Gao, W., Nie, F., Wang, F., Zhou, H.: Fair and cache blocking aware warp scheduling for concurrent kernel execution on GPU. Futur. Gener. Comput. Syst. 112, 1093\u20131105 (2020). https:\/\/doi.org\/10.1016\/j.future.2020.05.023","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"1","key":"755_CR36","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s11227-021-03819-z","volume":"78","author":"B L\u00f3pez-Albelda","year":"2022","unstructured":"L\u00f3pez-Albelda, B., Castro, F.M., Gonz\u00e1lez-Linares, J.M., Guil, N.: Flexsched: efficient scheduling techniques for concurrent kernel execution on GPUs. J. Supercomput. 78(1), 43\u201371 (2022). https:\/\/doi.org\/10.1007\/s11227-021-03819-z","journal-title":"J. Supercomput."},{"issue":"6","key":"755_CR37","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TPDS.2021.3115630","volume":"33","author":"C Zhao","year":"2022","unstructured":"Zhao, C., Gao, W., Nie, F., Zhou, H.: A survey of GPU multitasking methods supported by hardware architecture. IEEE Trans. Parallel Distrib. Syst. 33(6), 1451\u20131463 (2022). https:\/\/doi.org\/10.1109\/TPDS.2021.3115630","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"755_CR38","series-title":"Texts and Monographs in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1098-6","volume-title":"Computational Geometry\u2014An Introduction","author":"FP Preparata","year":"1985","unstructured":"Preparata, F.P., Shamos, M.I.: Computational Geometry\u2014An Introduction. Texts and Monographs in Computer Science, Springer, Berlin (1985)"},{"issue":"5","key":"755_CR39","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/0020-0190(88)90150-0","volume":"26","author":"KH Hinrichs","year":"1988","unstructured":"Hinrichs, K.H., Nievergelt, J., Schorn, P.: Plane-sweep solves the closest pair problem elegantly. Inf. Process. Lett. 26(5), 255\u2013261 (1988). https:\/\/doi.org\/10.1016\/0020-0190(88)90150-0","journal-title":"Inf. Process. Lett."},{"key":"755_CR40","doi-asserted-by":"publisher","first-page":"100428","DOI":"10.1016\/j.iot.2021.100428","volume":"15","author":"P Velentzas","year":"2021","unstructured":"Velentzas, P., Vassilakopoulos, M., Corral, A.: GPU-aided edge computing for processing the $${k}$$ nearest-neighbor query on SSD-resident data. Internet of Things 15, 100428 (2021). https:\/\/doi.org\/10.1016\/j.iot.2021.100428","journal-title":"Internet of Things"},{"key":"755_CR41","doi-asserted-by":"publisher","unstructured":"Velentzas, P., Moutafis, P., Mavrommatis, G.: An improved GPU-based algorithm for processing the k nearest neighbor query. In: PCI Conference, pp. 372\u2013375 (2020). https:\/\/doi.org\/10.1145\/3437120.3437343","DOI":"10.1145\/3437120.3437343"},{"key":"755_CR42","unstructured":"NVIDIA: CUDA 7 Streams Simplify Concurrency (2015). https:\/\/developer.nvidia.com\/blog\/gpu-pro-tip-cuda-7-streams-simplify-concurrency\/ Accessed 11 Jan 2021"},{"key":"755_CR43","doi-asserted-by":"publisher","unstructured":"Zhou, H., Bateni, S., Liu, C.: $$\\text{S}^{\\text{3dnn }}$$: Supervised streaming and scheduling for GPU-accelerated real-time DNN workloads. In: RTAS Conference, pp. 190\u2013201 (2018). https:\/\/doi.org\/10.1109\/RTAS.2018.00028","DOI":"10.1109\/RTAS.2018.00028"},{"key":"755_CR44","doi-asserted-by":"publisher","unstructured":"Katiyar, P., Vu, T., Eldawy, A., Migliorini, S., Belussi, A.: Spiderweb: a spatial data generator on the web. In: SIGSPATIAL Conference, pp. 465\u2013468 (2020). https:\/\/doi.org\/10.1145\/3397536.3422351","DOI":"10.1145\/3397536.3422351"},{"key":"755_CR45","doi-asserted-by":"publisher","unstructured":"Vu, T., Migliorini, S., Eldawy, A., Belussi, A.: Spatial data generators. In: SpatialGems\u2014SIGSPATIAL International Workshop on Spatial Gems, pp. 1\u20137 (2019). https:\/\/doi.org\/10.1145\/3391234.3421234","DOI":"10.1145\/3391234.3421234"},{"key":"755_CR46","doi-asserted-by":"publisher","unstructured":"Eldawy, A., Mokbel, M.F.: Spatialhadoop: a mapreduce framework for spatial data. In: ICDE Conference, pp. 1352\u20131363 (2015). https:\/\/doi.org\/10.1109\/ICDE.2015.7113382","DOI":"10.1109\/ICDE.2015.7113382"},{"issue":"3","key":"755_CR47","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1007\/s10586-019-03013-0","volume":"23","author":"G Roumelis","year":"2020","unstructured":"Roumelis, G., Velentzas, P., Vassilakopoulos, M., Corral, A., Fevgas, A., Manolopoulos, Y.: Parallel processing of spatial batch-queries using $$\\text{ xbr}^+$$-trees in solid-state drives. Clust. Comput. 23(3), 1555\u20131575 (2020). https:\/\/doi.org\/10.1007\/s10586-019-03013-0","journal-title":"Clust. Comput."},{"key":"755_CR48","doi-asserted-by":"publisher","unstructured":"Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Closest pair queries in spatial databases. In: ACM SIGMOD Conference, pp. 189\u2013200 (2000). https:\/\/doi.org\/10.1145\/342009.335414","DOI":"10.1145\/342009.335414"}],"container-title":["International Journal of Parallel Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-023-00755-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10766-023-00755-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-023-00755-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T21:01:56Z","timestamp":1698354116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10766-023-00755-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,21]]},"references-count":48,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["755"],"URL":"https:\/\/doi.org\/10.1007\/s10766-023-00755-8","relation":{},"ISSN":["0885-7458","1573-7640"],"issn-type":[{"value":"0885-7458","type":"print"},{"value":"1573-7640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,21]]},"assertion":[{"value":"8 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}