Development and innovation of enterprise knowledge management strategies using big data neural networks technology

To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms a...
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Autor*in:	Yuanjun Zhao [verfasserIn] Subin Wen [verfasserIn] Tengjun Zhou [verfasserIn] Wei Liu [verfasserIn] Hongxin Yu [verfasserIn] Hongwei Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Enterprise knowledge Management strategies Big data Neural networks technology

Übergeordnetes Werk:	In: Journal of Innovation & Knowledge - Elsevier, 2017, 7(2022), 4, Seite 100273-
Übergeordnetes Werk:	volume:7 ; year:2022 ; number:4 ; pages:100273-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jik.2022.100273

Katalog-ID:	DOAJ086511696

Internformat


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520			\|a To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.
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publishDate	2022
allfields	10.1016/j.jik.2022.100273 doi (DE-627)DOAJ086511696 (DE-599)DOAJ99b3a77d953a4d9ab6f8d2b13b7ef43a DE-627 ger DE-627 rakwb eng AZ20-999 H1-99 Yuanjun Zhao verfasserin aut Development and innovation of enterprise knowledge management strategies using big data neural networks technology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference. Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General) Subin Wen verfasserin aut Tengjun Zhou verfasserin aut Wei Liu verfasserin aut Hongxin Yu verfasserin aut Hongwei Xu verfasserin aut In Journal of Innovation & Knowledge Elsevier, 2017 7(2022), 4, Seite 100273- (DE-627)880796871 (DE-600)2885454-8 2444569X nnns volume:7 year:2022 number:4 pages:100273- https://doi.org/10.1016/j.jik.2022.100273 kostenfrei https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a kostenfrei http://www.sciencedirect.com/science/article/pii/S2444569X22001081 kostenfrei https://doaj.org/toc/2444-569X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 4 100273-
spelling	10.1016/j.jik.2022.100273 doi (DE-627)DOAJ086511696 (DE-599)DOAJ99b3a77d953a4d9ab6f8d2b13b7ef43a DE-627 ger DE-627 rakwb eng AZ20-999 H1-99 Yuanjun Zhao verfasserin aut Development and innovation of enterprise knowledge management strategies using big data neural networks technology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference. Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General) Subin Wen verfasserin aut Tengjun Zhou verfasserin aut Wei Liu verfasserin aut Hongxin Yu verfasserin aut Hongwei Xu verfasserin aut In Journal of Innovation & Knowledge Elsevier, 2017 7(2022), 4, Seite 100273- (DE-627)880796871 (DE-600)2885454-8 2444569X nnns volume:7 year:2022 number:4 pages:100273- https://doi.org/10.1016/j.jik.2022.100273 kostenfrei https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a kostenfrei http://www.sciencedirect.com/science/article/pii/S2444569X22001081 kostenfrei https://doaj.org/toc/2444-569X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 4 100273-
allfields_unstemmed	10.1016/j.jik.2022.100273 doi (DE-627)DOAJ086511696 (DE-599)DOAJ99b3a77d953a4d9ab6f8d2b13b7ef43a DE-627 ger DE-627 rakwb eng AZ20-999 H1-99 Yuanjun Zhao verfasserin aut Development and innovation of enterprise knowledge management strategies using big data neural networks technology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference. Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General) Subin Wen verfasserin aut Tengjun Zhou verfasserin aut Wei Liu verfasserin aut Hongxin Yu verfasserin aut Hongwei Xu verfasserin aut In Journal of Innovation & Knowledge Elsevier, 2017 7(2022), 4, Seite 100273- (DE-627)880796871 (DE-600)2885454-8 2444569X nnns volume:7 year:2022 number:4 pages:100273- https://doi.org/10.1016/j.jik.2022.100273 kostenfrei https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a kostenfrei http://www.sciencedirect.com/science/article/pii/S2444569X22001081 kostenfrei https://doaj.org/toc/2444-569X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 4 100273-
allfieldsGer	10.1016/j.jik.2022.100273 doi (DE-627)DOAJ086511696 (DE-599)DOAJ99b3a77d953a4d9ab6f8d2b13b7ef43a DE-627 ger DE-627 rakwb eng AZ20-999 H1-99 Yuanjun Zhao verfasserin aut Development and innovation of enterprise knowledge management strategies using big data neural networks technology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference. Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General) Subin Wen verfasserin aut Tengjun Zhou verfasserin aut Wei Liu verfasserin aut Hongxin Yu verfasserin aut Hongwei Xu verfasserin aut In Journal of Innovation & Knowledge Elsevier, 2017 7(2022), 4, Seite 100273- (DE-627)880796871 (DE-600)2885454-8 2444569X nnns volume:7 year:2022 number:4 pages:100273- https://doi.org/10.1016/j.jik.2022.100273 kostenfrei https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a kostenfrei http://www.sciencedirect.com/science/article/pii/S2444569X22001081 kostenfrei https://doaj.org/toc/2444-569X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 4 100273-
allfieldsSound	10.1016/j.jik.2022.100273 doi (DE-627)DOAJ086511696 (DE-599)DOAJ99b3a77d953a4d9ab6f8d2b13b7ef43a DE-627 ger DE-627 rakwb eng AZ20-999 H1-99 Yuanjun Zhao verfasserin aut Development and innovation of enterprise knowledge management strategies using big data neural networks technology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference. Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General) Subin Wen verfasserin aut Tengjun Zhou verfasserin aut Wei Liu verfasserin aut Hongxin Yu verfasserin aut Hongwei Xu verfasserin aut In Journal of Innovation & Knowledge Elsevier, 2017 7(2022), 4, Seite 100273- (DE-627)880796871 (DE-600)2885454-8 2444569X nnns volume:7 year:2022 number:4 pages:100273- https://doi.org/10.1016/j.jik.2022.100273 kostenfrei https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a kostenfrei http://www.sciencedirect.com/science/article/pii/S2444569X22001081 kostenfrei https://doaj.org/toc/2444-569X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 4 100273-
language	English
source	In Journal of Innovation & Knowledge 7(2022), 4, Seite 100273- volume:7 year:2022 number:4 pages:100273-
sourceStr	In Journal of Innovation & Knowledge 7(2022), 4, Seite 100273- volume:7 year:2022 number:4 pages:100273-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Enterprise knowledge Management strategies Big data Neural networks technology History of scholarship and learning. The humanities Social sciences (General)
isfreeaccess_bool	true
container_title	Journal of Innovation & Knowledge
authorswithroles_txt_mv	Yuanjun Zhao @@aut@@ Subin Wen @@aut@@ Tengjun Zhou @@aut@@ Wei Liu @@aut@@ Hongxin Yu @@aut@@ Hongwei Xu @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	880796871
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language_de	englisch
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callnumber-first	A - General Works
author	Yuanjun Zhao
spellingShingle	Yuanjun Zhao misc AZ20-999 misc H1-99 misc Enterprise knowledge misc Management strategies misc Big data misc Neural networks technology misc History of scholarship and learning. The humanities misc Social sciences (General) Development and innovation of enterprise knowledge management strategies using big data neural networks technology
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title	Development and innovation of enterprise knowledge management strategies using big data neural networks technology
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title_sort	development and innovation of enterprise knowledge management strategies using big data neural networks technology
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title_auth	Development and innovation of enterprise knowledge management strategies using big data neural networks technology
abstract	To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.
abstractGer	To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.
abstract_unstemmed	To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.
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title_short	Development and innovation of enterprise knowledge management strategies using big data neural networks technology
url	https://doi.org/10.1016/j.jik.2022.100273 https://doaj.org/article/99b3a77d953a4d9ab6f8d2b13b7ef43a http://www.sciencedirect.com/science/article/pii/S2444569X22001081 https://doaj.org/toc/2444-569X
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The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Enterprise knowledge</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Management strategies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">History of scholarship and learning. 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