{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T15:48:59Z","timestamp":1749052139491},"reference-count":25,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today\u2019s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053\u2009s, which is much lower than the general database query time.<\/jats:p>","DOI":"10.1515\/comp-2022-0251","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T20:18:33Z","timestamp":1664309913000},"page":"300-313","source":"Crossref","is-referenced-by-count":3,"title":["Mass data processing and multidimensional database management based on deep learning"],"prefix":"10.1515","volume":"12","author":[{"given":"Haijie","family":"Shen","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Yangyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"}]},{"given":"Xinzhi","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Xiaofan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Caihong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Qian","family":"Bian","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Zhenduo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"},{"name":"Map\u00faa University , Manila 1002 , Philippines"}]},{"given":"Weihua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Xi\u2019an Siyuan University , Xi\u2019an 710038 , Shaanxi , China"}]}],"member":"374","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"2022092720182678183_j_comp-2022-0251_ref_001","doi-asserted-by":"crossref","unstructured":"X. Hao, G. Zhang, and S. Ma, \u201cDeep learning,\u201d Int. J. Semant. Comput., vol. 10, no. 3, pp. 417\u2013439, 2016.","DOI":"10.1142\/S1793351X16500045"},{"key":"2022092720182678183_j_comp-2022-0251_ref_002","doi-asserted-by":"crossref","unstructured":"T. Hermosilla, M. A. Wulder, J. C. White, N. C. Coops, G. W. Hobart, and L. B. Campbell, \u201cMass data processing of time series Landsat imagery: pixels to data products for forest monitoring,\u201d Int. J. Digital Earth, vol. 9, pp. 1\u201320, 2016.","DOI":"10.1080\/17538947.2016.1187673"},{"key":"2022092720182678183_j_comp-2022-0251_ref_003","doi-asserted-by":"crossref","unstructured":"D. Linstedt and M. Olschimke, \u201cMultidimensional database,\u201d Build. a Scalable Data Wareh. Data Vault 2.0, pp. 623\u2013647, 2016. 10.1016\/B978-0-12-802510-9.00015-5.","DOI":"10.1016\/B978-0-12-802510-9.00015-5"},{"key":"2022092720182678183_j_comp-2022-0251_ref_004","doi-asserted-by":"crossref","unstructured":"A. A. Alwan, H. Ibrahim, N. I. Udzir, and F. Sidi, \u201cAn efficient approach for processing skyline queries in incomplete multidimensional database,\u201d Arab. J. Ence. Eng., vol. 41, no. 8, pp. 2927\u20132943, 2016.","DOI":"10.1007\/s13369-016-2048-z"},{"key":"2022092720182678183_j_comp-2022-0251_ref_005","doi-asserted-by":"crossref","unstructured":"K. J. Fritzsching, M. Hong, and K. Schmidt-Rohr, \u201cConformationally selective multidimensional chemical shift ranges in proteins from a PACSY database purged using intrinsic quality criteria,\u201d J. Biomol. Nmr, vol. 64, no. 2, pp. 115\u2013130, 2016.","DOI":"10.1007\/s10858-016-0013-5"},{"key":"2022092720182678183_j_comp-2022-0251_ref_006","doi-asserted-by":"crossref","unstructured":"R. Cherniak, Q. Zhu, Y. Gu, and S. Prananik. [ACM Press the 21st International Database Engineering & Applications Symposium - Bristol, United Kingdom (2017.07.12-2017.07.14)], Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017 - Exploring Deletion Strategies for the BoND-Tree in Multidimensional Non-ordered Discrete Data Spaces, 2017, pp. 153\u2013160.","DOI":"10.1145\/3105831.3105840"},{"key":"2022092720182678183_j_comp-2022-0251_ref_007","doi-asserted-by":"crossref","unstructured":"F. E. Palominos, C. A. Dur\u00e1n, and F. M. C\u00f3rdova, \u201cImprove efficiency in multidimensional database queries through the use of additives aggregation functions,\u201d Procedia Comput. Sci., vol. 162, pp. 754\u2013761, 2019.","DOI":"10.1016\/j.procs.2019.12.047"},{"key":"2022092720182678183_j_comp-2022-0251_ref_008","doi-asserted-by":"crossref","unstructured":"W. R. Zhang, \u201cA multidimensional Choledoch Database and benchmarks for cholangiocarcinoma diagnosis,\u201d IEEE Access, vol. 7, pp. 1\u20131, 2019.","DOI":"10.1109\/ACCESS.2019.2947470"},{"key":"2022092720182678183_j_comp-2022-0251_ref_009","doi-asserted-by":"crossref","unstructured":"M. C. Tarr\u00e9s, N. A. Moscoloni, H. D. Navone, and A. L. D'ottavio, \u201cAnlisis multidimensional de una base de datos de mujeres pima multidimensional analysis from a database of pima women,\u201d BIOtecnia, vol. 18, no. 3, pp. 14\u201319, 2016.","DOI":"10.18633\/biotecnia.v18i3.330"},{"key":"2022092720182678183_j_comp-2022-0251_ref_010","doi-asserted-by":"crossref","unstructured":"T. Inoue, A. Krishna, and R. P. Gopalan, \u201c Approximate query processing on high dimensionality database tables using multidimensional cluster sampling view,\u201d J. Softw., vol. 11, no. 1, pp. 80\u201393, 2016.","DOI":"10.17706\/jsw.11.1.80-93"},{"key":"2022092720182678183_j_comp-2022-0251_ref_011","doi-asserted-by":"crossref","unstructured":"M. Appel, F. Lahn, W. Buytaert, and E. Pebesma, \u201cOpen and scalable analytics of large Earth observation datasets: From scenes to multidimensional arrays using SciDB and GDAL,\u201d ISPRS J. Photogramm. Remote. Sens., vol. 138, pp. 47\u201356, 2018.","DOI":"10.1016\/j.isprsjprs.2018.01.014"},{"key":"2022092720182678183_j_comp-2022-0251_ref_012","doi-asserted-by":"crossref","unstructured":"H. Liu, P. Van Oosterom, C. Hu, and W. Wang, \u201cManaging large multidimensional array hydrologic datasets: A case study comparing NetCDF and SciDB,\u201d Procedia Eng, vol. 154, pp. 207\u2013214, 2016.","DOI":"10.1016\/j.proeng.2016.07.449"},{"key":"2022092720182678183_j_comp-2022-0251_ref_013","doi-asserted-by":"crossref","unstructured":"W. Bittremieux, \u201cspectrum_utils: A Python package for mass spectrometry data processing and visualization,\u201d Anal. Chem., vol. 92, no. 1, pp. 659\u2013661, 2020.","DOI":"10.1021\/acs.analchem.9b04884"},{"key":"2022092720182678183_j_comp-2022-0251_ref_014","doi-asserted-by":"crossref","unstructured":"S. R. Massel, \u201c[Advanced series on ocean engineering] ocean surface waves (Their Physics and Prediction),\u201d Data Process. Simul. Tech., vol. 45, pp. 527\u2013552, 2017, 10.1142\/10666:645-672.","DOI":"10.1142\/10666"},{"key":"2022092720182678183_j_comp-2022-0251_ref_015","doi-asserted-by":"crossref","unstructured":"Z. Huo, K. Taylor, X. Zhang, S. Wang, and C. Pang, \u201cGenerating multidimensional schemata from relational aggregation queries,\u201d World Wide Web, vol. 23, no. 1, pp. 337\u2013359, 2020.","DOI":"10.1007\/s11280-019-00706-9"},{"key":"2022092720182678183_j_comp-2022-0251_ref_016","doi-asserted-by":"crossref","unstructured":"J. Tyrychtr and A. Vasilenko, \u201cTransformation econometric model to multidimensional databases to support the analytical systems in agriculture,\u201d AGRIS on-line Pap. Econ. Inform., vol. 7, no. 3, pp. 71\u201377, 2016.","DOI":"10.7160\/aol.2015.070307"},{"key":"2022092720182678183_j_comp-2022-0251_ref_017","doi-asserted-by":"crossref","unstructured":"H. Lustosa, F. Porto, P. Valduriez, and P. Blanco, \u201cDatabase system support of simulation data,\u201d Proc. Vldb Endowment, vol. 9, no. 13, pp. 1329\u20131340, 2016.","DOI":"10.14778\/3007263.3007271"},{"key":"2022092720182678183_j_comp-2022-0251_ref_018","doi-asserted-by":"crossref","unstructured":"C. E. Atay and G. Alp, \u201cModeling and querying multidimensional bitemporal data warehouses,\u201d Int. J. Comput. Commun. Eng., vol. 5, no. 2, pp. 110\u2013119, 2016.","DOI":"10.17706\/IJCCE.2016.5.2.110-119"},{"key":"2022092720182678183_j_comp-2022-0251_ref_019","doi-asserted-by":"crossref","unstructured":"A. G. Komilov, \u201cAlgorithm for multivariate solution of mathematical models in MATLAB to create a database of environmental parameters,\u201d Appl. Sol. Energy, vol. 56, no. 1, pp. 63\u201369, 2020.","DOI":"10.3103\/S0003701X20010077"},{"key":"2022092720182678183_j_comp-2022-0251_ref_020","doi-asserted-by":"crossref","unstructured":"A. Gupta, \u201cMultidimensional data formats,\u201d Encycl. Database Syst., pp. 1776\u20131777, 2016.","DOI":"10.1007\/978-0-387-39940-9_1309"},{"key":"2022092720182678183_j_comp-2022-0251_ref_021","doi-asserted-by":"crossref","unstructured":"C. R. Pretz, J. Kean, A. W. Heinemann, A. J. Kozlowski, R. K. Bode, and E. Gebhardt, \u201c A multidimensional Rasch analysis of the functional independence measure based on the national institute on disability, independent living, and rehabilitation research traumatic brain injury model systems national database,\u201d J. Neurotrauma, vol. 33, no. 14, pp. 1358\u20131362, 2016.","DOI":"10.1089\/neu.2015.4138"},{"key":"2022092720182678183_j_comp-2022-0251_ref_022","doi-asserted-by":"crossref","unstructured":"W. Xiaoming, L. Yanchun, and Y. Fang, \u201cAuthenticating multi-dimensional query results in outsourced database,\u201d Iet Inf. Security, vol. 10, no. 3, pp. 119\u2013124, 2016.","DOI":"10.1049\/iet-ifs.2014.0408"},{"key":"2022092720182678183_j_comp-2022-0251_ref_023","doi-asserted-by":"crossref","unstructured":"Y. Nakajima, H. Tani, T. Yamamoto, N. Murakami, S. Mitani, and K. Yamanaka, \u201c Contactless space debris detumbling: A database approach based on computational fluid dynamics,\u201d J. Guidance Control. Dyn., vol. 41, no. 9, pp. 1\u201313, 2018.","DOI":"10.2514\/1.G003451"},{"key":"2022092720182678183_j_comp-2022-0251_ref_024","doi-asserted-by":"crossref","unstructured":"H. Jiri, I. Igor, D. Michala, H. Bronislava, and B. Petr. [IEEE 2016 17th International Carpathian Control Conference (ICCC) - High Tatras, Slovakia (2016.5.29-2016.6.1)], 2016 17th International Carpathian Control Conference (ICCC) - Multidimensional database for crime prevention, 2016, pp. 242\u2013247.","DOI":"10.1109\/CarpathianCC.2016.7501102"},{"key":"2022092720182678183_j_comp-2022-0251_ref_025","doi-asserted-by":"crossref","unstructured":"A. A. Jarzabek, A. I. Moreno, J. M. Perales, and J. M. Vega, \u201cAerodynamic database error filtering via SVD-like methods,\u201d Aerosp. Sci. Technol., vol. 65, no. 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