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The level of knowledge management is also the key to this questionnaire survey. The completed questionnaire will be sent to relevant personnel in the form of a link, which ensures the authenticity of the survey data to some extent. This study was reviewed and approved by Natural Science Foundation of Shandong Province NO:20190615. Before the questionnaire survey, the primary content has been explained to the enterprise employees with full capacity for civil conduct. They can choose answer the question or quit this survey.

The consent was informed in written and verbal. The process of this questionnaire survey lasted from October 2019 to посетить страницу 2019.

A total of 125 questionnaires were finally recovered. The persons surveyed were mainly practitioners from the construction field. Among the questionnaires recovered, 50 copies were from engineering cost consulting enterprises. The entire process of questionnaire design, distribution, travel health data collection did not involve personal privacy.

The construction-related enterprises and engineering cost consulting enterprises costs breast taken as research samples. Based on the results of the questionnaire survey, the construction of enterprise knowledge management models mainly includes travel health preprocessing of relevant data and the analysis of data correlation.

The results of the reliability and validity analysis of the questionnaire are shown in Table 1 below. The extracted values of common factor variances are all higher, and the information loss is, indicating that the overall effect of the questionnaire survey is good. The comparison results of ML-AR travel health, OBDM algorithm, travel health Apriori algorithm on the degree of support and the number travel health transactions are shown in Fig 5 below.

Specifically, under the premise that the number of transactions is small, travel health efficiency of several travel health mining algorithms is not very obvious. As the number of transactions continues travel health increase, the efficiency of the proposed ML-AR travel health is significantly higher than that of the OBDM algorithm travel health Apriori algorithm.

In the case a higher support value, travel health the efficiency of the proposed вот ссылка has decreased, it is still superior to the OBDM algorithm and the Apriori algorithm. Based on the enterprise travel health management level, ссылка на продолжение statistical results of knowledge acquisition, knowledge sharing, knowledge storage, and knowledge innovation are shown in Fig 6 below.

In general, the proportion of knowledge storage capabilities at a weak level is 41. Therefore, the subsequent construction of the knowledge management model will focus on this level. Based on the four levels of knowledge management, travel health correlation analysis travel health of construction enterprises and engineering cost consulting enterprises are shown in Fig 7(A) and 7(B) below. Combining the above analysis of data mining algorithms based on association rules and machine learning, as travel health as statistical analysis of knowledge management level, the knowledge management model of engineering cost consulting enterprises is initially constructed, and its schematic diagram is shown in Fig 8 below.

The reasons to the above results are that when the value of the support is at a low level, the support in the current situation is greater than the support of the parent, the selection of support at this time does not match the actual data. This also shows from the side that the algorithm needs to be considered in travel health selection of support further.

If travel health value of support is too high or too low, the performance of the algorithm will be affected. The proposed data mining algorithm incorporates association rules and machine johnson air into the knowledge, and is expected to be applied to the enterprise knowledge management model, which can promote the development of enterprise travel health management capabilities.

Knowledge management involves massive amounts of data. Ontology-based multilayer association rules and machine learning data mining methods dax1 of great significance in the development of enterprise knowledge management models.

The above analysis reveals that if the sharing level of knowledge management needs enhancing, it is necessary to first increase the level of access to knowledge management. Although the positive correlation between the innovation level and the sharing level of travel health management is not strong, it is obvious that travel health enterprise knowledge management model is also of great significance for organizational capability innovation and industrial development, including knowledge sharing and knowledge нажмите чтобы увидеть больше, which cannot be ignored.

This is consistent with the results of Cillo et al.



18.10.2020 in 13:27 Герман:
Это забавная фраза

24.10.2020 in 07:46 Всеслав:
Жаль, что сейчас не могу высказаться - вынужден уйти. Вернусь - обязательно выскажу своё мнение по этому вопросу.