# Intelligent Make Policy Support Systemic for Business Management Based on Big Data Analysis

Authors: Benli Shi; Shekhar Boers
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the development of message technology, big data is connected with business management. The research content and present situation of intelligent make policy support systemic in big data are introduced. Based on the knowledge-based systemic and its relationship with make policy-making process, the knowledge and knowledge processing systemic in IDSS are discussed. With the development of computer and its network, many enterprises have established their own message management systemics. How to observe the enterprise itself and the outside world in a flexible, real-time, vertical and horizontal, dynamic and slicing way, so as to capture relevant message for the development needs of the enterprise. The rapid development of big data has aroused widespread concern and attention at home and abroad. Scientific and effective analysis and processing of big data are the core issues in the field of big data. This paper summarizes the research status of big data analysis at home and abroad from three aspects: analysis as a service, big data analysis methods, and big data-driven science germination through literature review. By analyzing the statistical and semantic features of the existing data, we can find out the rules and then summarize them into an abstract data analysis model, thus providing the basis for data analysis, and the establishment of data graph and model can better reflect the specific content of the study. In the research of big data, it is found that both traditional data and big data have packet loss rates. According to the research, the packet loss rate of traditional data is as high as 70.9%, but that of big data is only 34.5%. Making a formula in the calculation of big data can help us to observe more. Through this research, it is found that big data has a more direct relationship with business management, and it controls various ways of enterprises. The era of big data has come, and all walks of life are faced with unprecedented data volume and data analysis requirements.


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## Body

## 1. Introduction

Modern enterprises increasingly rely on message technology to improve productivity and competitive advantage. The rapid development of message technology has had a profound and epoch-making impact on modern business management. On the contrary, the profound changes in modern business management have greatly promoted the development of enterprise message technology, especially the make policy support systemic [1]. With the development of computer and its network, many enterprises have established their own message management systemics. However, these systemics have obvious limitations, especially in supporting make policy-making, and the analysis and processing of message still remain in one-dimensional and two-dimensional space. At present, the work of safety management systemic in large- and medium-sized enterprises must be supported by electronic computers [2]. The management structure and governance structure of the company are inseparable. In an enterprise with orderly management and perfect governance, accounting must operate well, and it provides reliable and relevant make policy support systemics for internal and external make policy-makers [3]. Specifically, under the background of big data, how to realize the collection, storing, and analysis of big data is an urgent problem for people.Starting from the analysis and processing of big data, this paper briefly introduces the concept and characteristics of big data and emphatically analyzes the development status of the field of big data. Finally, it summarizes the existing problems in the analysis and processing of big data and makes a simple analysis of related problems. In the past, managers’ make policy-making originated from creativity, judgment, intuition, and experience, rather than systematic quantitative analysis based on scientific methods. Today, the external environment faced by management is changing rapidly. The intelligent make policy support systemic makes full use of the advantages of AI and expert systemic technology in qualitative analysis and uncertain reasoning and makes full use of human experience and knowledge in problem solving, thus providing a new way to solve practical problems [4]. This paper summarizes the research status of intelligent make policy support systemic and analyzes the challenges faced by IDSS and the further research direction. In enterprise safety management, not only a large number of statistical data but also a large number of mathematical models are needed for comprehensive analysis and judgment. With the improvement of computer hardware and software technology, various mathematical models have been adopted, gradually developing from basic operations to the formation of new message from existing data, which can predict moderate stochastic systemics. It also uses the spatial analysis ability to deal with the message about enterprise development and integrates other technologies to discuss the research and design of intelligent make policy support systemic for business management [5]. It also discusses the similarities and differences between big data analysis and intelligence analysis. Through comparison, find out the similarities between them, introduce the advantages of big data analysis, and strengthen intelligence analysis.Through big data analysis, it is known that combining make policy support systemic with AI technology and Internet greatly improves the flexibility and scalability of traditional make policy support systemic. Combined with advanced technologies such as data mining technology, knowledge discovery technology, fuzzy processing technology, and hypertext remote communication technology, it is applied to business management make policies. The use of big data analysis is the main way to manage the work of enterprises safely [6]. This method embodies the combination of systemic engineering experts and security experts, human brain and computer, and qualitative and quantitative methods. Combining big data can better manage enterprises.The main contributions of this paper are the following:(1)
Propose the processing systemic and model establishment based on big data analysis, which effectively realizes the convenience and dilemma of big data(2)
The advantages and disadvantages of big data can be better explained by establishing a model and proposing a comparison scheme in data analysis and processing(3)
The grouping method of make policy systemic is put forward, which is used to decorate the discovery and understanding objects between groupsThe rest of this paper is organized as follows: the second section introduces the analysis and processing of big data and also analyzes the store systemic. The third section introduces and explains the make policy-making systemic, mostly describing the intelligent make policy-making systemic and its application in management. The fourth section describes the business management. The last section summarizes the research methods, existing problems, and future forecast.


## 2. Related Work

Big data has the characteristics of large data volume, complex data structure, fast data generation, and low data value density, which increase the difficulty of effective analysis of big data. Big data analysis is the key task in the era of big data. Driven by the huge amount of data, the society is faced with a strong potential demand for data analysis. In reality, data is often presented in a disorderly way, which brings great challenges to big data analysis. Data quality has become an important factor to be considered in the process of big data analysis. Big data provides users with the ability to use ordinary computers to process and analyze multiple data sets and return the results in time. Cloud computing provides the underlying engine and infrastructure guarantee for big data through distributed data processing platforms. The analysis and processing results of big data are really understood by users to support make policy-making, and the interpretation of the results is necessary.In the analysis of big data, Tan thinks that in this process, there are two key technical problems: one is text analysis, and the other is machine learning [7]. Therefore, Alalwan et al. think that data analysis is based on the data generation mechanism, so they will collect and store data extensively and format and clean the data. Based on the big data analysis model and supported by the integrated big data analysis platform, they will use cloud computing technology to schedule computing and analysis resources and finally dig out the data analysis process of the patterns or laws behind big data [8]. Perilla believes that big data is changing the way of scientific research [9]. Kahraman et al. think that the premise of big data analysis must ensure the quality of data and discuss how to ensure the data quality in the era of big data from the perspectives of process, technology, and management [10]. Cruz-Cunha et al. think that we should fully understand these similarities and differences and give full play to the characteristics and advantages of intelligence analysis to meet the challenges brought by big data [11]. At present, the mainstream view is that big data and big data analysis bring great opportunities for the development of message science. Kehrer et al. think that the influence of big data on message science is multifaceted, and the most influential one is message analysis [12]. There is “correlation” in big data. Hung-Yi thinks that there is correlation between the former and the latter, or that the two are related [13].Big data analysis is the product of rethinking data science and exploring new models in the data-intensive environment. Cao believes that big data analysis is a data analysis process that collects and stores data extensively and formats and cleans the data according to the data generation mechanism. Based on the big data analysis model and supported by the integrated big data analysis platform, cloud computing technology is used to schedule computing and analysis resources, and finally, the patterns or laws behind big data are mined.


## 3. Big Data Analysis and Processing

### 3.1. Connotation and Characteristics of Big Data Analysis

What is “big data”? This issue has always been the focus of debate in the industry. Scholars and experts engaged in big data research have their own opinions on the definition of big data. Big data analysis is the product of rethinking data science and exploring new models in the data-intensive environment. Big data is a concept opposite to traditional data sets. The first thing that has to be said about the difference between traditional data is the amount of data, which is the key factor to distinguish big data from traditional data [14, 15]. Traditional data sets generally only reach GB, while big data has reached PB, EB, or even ZB. Big data analysis is a process that takes information and data as basic resources and research objects and effectively organizes, manages, analyzes, and mines information and data, so as to provide related services for users. Compare data with traditional data, as shown in Table 1.Table 1
Comparison between big data and traditional data.
Traditional dataBig dataAmount of dataKB, MB, and GBGB, PB, EB, and so onTransmission speedEvery hour, every day, etc.Minutes, seconds, etc.TypeDesignabilitySemistructured or unstructuredSourceSingle source, centralized distributionDiverse sourcesIntegratedSimpleDifficultySaveRMHDFrom the above table, we can know that it is quite difficult to label and store big data, and the labeling task of big data cannot be completed in most cases, so the future of big data development is limitless.


### 3.2. Analysis and Processing of Big Data

With the rapid development of message technology, data acquisition becomes easy and fast, and the amount of data expands rapidly in a short time. The store, query, and index of data are all facing unprecedented challenges [16]. The foundation of big data lies in data, which is characterized by huge data volume, various data types, low data value density, and fast processing speed. How to deal with super-large-scale network data, mobile data, radiofrequency acquisition data, and social computing data has become a key problem to be solved in scientific research and industry, and it is also the core problem to be solved in big data. The task of big data analysis is to classify the data redundantly, remove the rough and extract the fine, mine valuable message and knowledge from the data, and turn big data into small data through quantitative analysis. In the data, both traditional data and big data have packet loss rates. Based on this, a data table is made to compare the packet loss rates as shown in Figure 1.Figure 1
Comparison of data packet loss rate.According to the above table, the packet loss rate of traditional data is as high as 70.9%, while that of big data is only 34.5%.Any kind of message structure is related to each other according to certain rules. Analyzing and revealing the laws and rules of message correlation (i.e., correlation) is the basis for effective organization, retrieval, analysis, and mining of message, knowledge, and intelligence. Text classification technology is mostly used to identify the theme of documents, classify documents with the same theme into predefined themes, or find the theme set corresponding to the target document in numerous theme sets. However, document clustering classifies documents with high similarity into one class, and there is no predefined topic as prior knowledge [17]. Big data analysis and mining is an important technology to transform massive, complex, high-speed, low-density big data into knowledge or mode of human production and life services. Through the big data analysis and processing systemic to reflect the operation mode of big data, etc., through the establishment of data tables, we can more intuitively see the trend of data calculation as shown in Figure 2.Figure 2
Data map of big data message calculation.The text is supplemented as follows: the download rate of traditional data is about 50% slower than that of big data.


### 3.3. Big Data Store Systemic

Big data is growing at an alarming rate. The store mode of big data not only affects the efficiency of data analysis and processing but also affects the cost of data store. Therefore, it is necessary to study high-efficiency and low-cost data store methods. Cloud store technology is a good choice. Cloud store focuses on providing users with online store services based on the Internet. The cumbersome underlying technologies such as store capacity, store device type, data store location, data availability, reliability, and security need not be considered at all and can be obtained directly from the provider.In the process of providing storage services, according to the different service contents of users, cloud store users can be divided into store users and cloud computing users according to their different service contents. Store users only need simple and fast data store services provided by cloud service providers, while cloud computing users first store data in the cloud to prepare for computing services [18]. By establishing a cloud store model, we can see the technology of cloud store more intuitively. According to the store calculation, the data diagram of message sparsity and execution time is shown in Figure 3.Figure 3
Calculation and comparison of the message with different sparsity.No matter how sparse the information is, the processing time of big data is about 50% faster than that of traditional data.The data shows that the probability of traditional data being transmitted for a long time is 78.5%, while the probability of big data being transmitted for a long time is 35.4%, which greatly reduces the probability of long time (Figure4).Figure 4
Comparison of the execution time of different users.Massive data is the object of big data analysis. Because of the diversity of its generation methods, it involves the efficient collection and seamless integration of multisource data such as different message systemics and different application software and sensor networks. For the development of big data systemic, the systemic must be usable and easy to use and have the ability of human-computer interaction. At the same time, the computing efficiency and throughput of the systemic must be high. In order to adapt to the characteristics of big data changing with time, the systemic also needs to have the ability of evolution. All these requirements have brought great challenges to the design and development of big data processing systemic. There is time management in the store systemic, and the time interval between each subtask is distributed independently and randomly, such as(1)A∗S=∫0∞estatdt,(2)B∗S=∫0∞estbtdt,(3)C∗S=∫0∞estctdt.A subtask is served by a cloud node, and we define the time interval from the service start time to the service end time as a service time.


### 3.4. Challenges in Data Analysis Model and Processing

In the field of data analysis systemic, database, model base, and knowledge base are three interrelated but different concepts. The model base subsystem includes model base and model base management system. As the name implies, the model database contains many available models, which can be divided into simple models and complex models, single models, and combined models [19]. From the practical application of big data analysis, the amount of data is large and complex, and the application of complex and combined models may not meet the requirements of data analysis in a short time. In the process of data mining, IBM data mining accelerator provides a series of stream processing language operators to sort and record real-time data, and these operators will predict model markup language. Each database forms a dynamic circular relationship in the data results as shown in Figure 5.Figure 5
Simplification of the relationship.Constantly optimizing the model of data analysis, through the cooperation of model base and model base management systemic, can expand the analysis and processing ability of data analysis model to the emerging big data. To manage and retrieve multimedia data, it is necessary to label different multimedia data. There are two kinds of multimedia labeling: manual labeling and automatic labeling. In the early multimedia labeling, the mixed labeling method combining manual labeling and computer automatic labeling was mainly adopted. Content-based method depends on the selection of content similarity measure, which is greatly influenced by content analysis results. In the data analysis, the cloud computing center formula such as formula (4) is used:
(4)vmtal=∑i=1Mmi.In the formula model, there are heterogeneousMs’s; among which, the largest heterogeneous PM can be created on the Mi.


## 3.1. Connotation and Characteristics of Big Data Analysis

What is “big data”? This issue has always been the focus of debate in the industry. Scholars and experts engaged in big data research have their own opinions on the definition of big data. Big data analysis is the product of rethinking data science and exploring new models in the data-intensive environment. Big data is a concept opposite to traditional data sets. The first thing that has to be said about the difference between traditional data is the amount of data, which is the key factor to distinguish big data from traditional data [14, 15]. Traditional data sets generally only reach GB, while big data has reached PB, EB, or even ZB. Big data analysis is a process that takes information and data as basic resources and research objects and effectively organizes, manages, analyzes, and mines information and data, so as to provide related services for users. Compare data with traditional data, as shown in Table 1.Table 1
Comparison between big data and traditional data.
Traditional dataBig dataAmount of dataKB, MB, and GBGB, PB, EB, and so onTransmission speedEvery hour, every day, etc.Minutes, seconds, etc.TypeDesignabilitySemistructured or unstructuredSourceSingle source, centralized distributionDiverse sourcesIntegratedSimpleDifficultySaveRMHDFrom the above table, we can know that it is quite difficult to label and store big data, and the labeling task of big data cannot be completed in most cases, so the future of big data development is limitless.


## 3.2. Analysis and Processing of Big Data

With the rapid development of message technology, data acquisition becomes easy and fast, and the amount of data expands rapidly in a short time. The store, query, and index of data are all facing unprecedented challenges [16]. The foundation of big data lies in data, which is characterized by huge data volume, various data types, low data value density, and fast processing speed. How to deal with super-large-scale network data, mobile data, radiofrequency acquisition data, and social computing data has become a key problem to be solved in scientific research and industry, and it is also the core problem to be solved in big data. The task of big data analysis is to classify the data redundantly, remove the rough and extract the fine, mine valuable message and knowledge from the data, and turn big data into small data through quantitative analysis. In the data, both traditional data and big data have packet loss rates. Based on this, a data table is made to compare the packet loss rates as shown in Figure 1.Figure 1
Comparison of data packet loss rate.According to the above table, the packet loss rate of traditional data is as high as 70.9%, while that of big data is only 34.5%.Any kind of message structure is related to each other according to certain rules. Analyzing and revealing the laws and rules of message correlation (i.e., correlation) is the basis for effective organization, retrieval, analysis, and mining of message, knowledge, and intelligence. Text classification technology is mostly used to identify the theme of documents, classify documents with the same theme into predefined themes, or find the theme set corresponding to the target document in numerous theme sets. However, document clustering classifies documents with high similarity into one class, and there is no predefined topic as prior knowledge [17]. Big data analysis and mining is an important technology to transform massive, complex, high-speed, low-density big data into knowledge or mode of human production and life services. Through the big data analysis and processing systemic to reflect the operation mode of big data, etc., through the establishment of data tables, we can more intuitively see the trend of data calculation as shown in Figure 2.Figure 2
Data map of big data message calculation.The text is supplemented as follows: the download rate of traditional data is about 50% slower than that of big data.


## 3.3. Big Data Store Systemic

Big data is growing at an alarming rate. The store mode of big data not only affects the efficiency of data analysis and processing but also affects the cost of data store. Therefore, it is necessary to study high-efficiency and low-cost data store methods. Cloud store technology is a good choice. Cloud store focuses on providing users with online store services based on the Internet. The cumbersome underlying technologies such as store capacity, store device type, data store location, data availability, reliability, and security need not be considered at all and can be obtained directly from the provider.In the process of providing storage services, according to the different service contents of users, cloud store users can be divided into store users and cloud computing users according to their different service contents. Store users only need simple and fast data store services provided by cloud service providers, while cloud computing users first store data in the cloud to prepare for computing services [18]. By establishing a cloud store model, we can see the technology of cloud store more intuitively. According to the store calculation, the data diagram of message sparsity and execution time is shown in Figure 3.Figure 3
Calculation and comparison of the message with different sparsity.No matter how sparse the information is, the processing time of big data is about 50% faster than that of traditional data.The data shows that the probability of traditional data being transmitted for a long time is 78.5%, while the probability of big data being transmitted for a long time is 35.4%, which greatly reduces the probability of long time (Figure4).Figure 4
Comparison of the execution time of different users.Massive data is the object of big data analysis. Because of the diversity of its generation methods, it involves the efficient collection and seamless integration of multisource data such as different message systemics and different application software and sensor networks. For the development of big data systemic, the systemic must be usable and easy to use and have the ability of human-computer interaction. At the same time, the computing efficiency and throughput of the systemic must be high. In order to adapt to the characteristics of big data changing with time, the systemic also needs to have the ability of evolution. All these requirements have brought great challenges to the design and development of big data processing systemic. There is time management in the store systemic, and the time interval between each subtask is distributed independently and randomly, such as(1)A∗S=∫0∞estatdt,(2)B∗S=∫0∞estbtdt,(3)C∗S=∫0∞estctdt.A subtask is served by a cloud node, and we define the time interval from the service start time to the service end time as a service time.


## 3.4. Challenges in Data Analysis Model and Processing

In the field of data analysis systemic, database, model base, and knowledge base are three interrelated but different concepts. The model base subsystem includes model base and model base management system. As the name implies, the model database contains many available models, which can be divided into simple models and complex models, single models, and combined models [19]. From the practical application of big data analysis, the amount of data is large and complex, and the application of complex and combined models may not meet the requirements of data analysis in a short time. In the process of data mining, IBM data mining accelerator provides a series of stream processing language operators to sort and record real-time data, and these operators will predict model markup language. Each database forms a dynamic circular relationship in the data results as shown in Figure 5.Figure 5
Simplification of the relationship.Constantly optimizing the model of data analysis, through the cooperation of model base and model base management systemic, can expand the analysis and processing ability of data analysis model to the emerging big data. To manage and retrieve multimedia data, it is necessary to label different multimedia data. There are two kinds of multimedia labeling: manual labeling and automatic labeling. In the early multimedia labeling, the mixed labeling method combining manual labeling and computer automatic labeling was mainly adopted. Content-based method depends on the selection of content similarity measure, which is greatly influenced by content analysis results. In the data analysis, the cloud computing center formula such as formula (4) is used:
(4)vmtal=∑i=1Mmi.In the formula model, there are heterogeneousMs’s; among which, the largest heterogeneous PM can be created on the Mi.


## 4. Intelligent Make Policy Support Systemic

### 4.1. IIDS Knowledge Systemic

In the research of DSS, influenced by the structure of multidatabase systemic, data, models, methods, and knowledge are always distinguished, and each database and its management systemic are established, respectively. According to the different make policy tools adopted, a variety of make policy support systemics such as model-based systemic, data-based systemic, and communication-based systemic have appeared successively. Different ways of dealing with knowledge constitute different types of make policy support systemics. In the traditional DSS, the computer execution process is carried out in a predefined process and way. By applying mathematical models and various quantitative make policy-making methods, the auxiliary make policy-making message can be obtained by operating the data. This structure of IDSS takes the knowledge base and its management systemic as the core, and the key technologies are knowledge representation and processing and self-learning ability. The advantage of this structure is that it is convenient for the maintenance of knowledge base and the exchange of data and information, and it can really realize the dominant idea of IDSS’s complementary advantages.The make policy-making ability of DSS is related to knowledge systemic. Actually, any intelligent system can change its behavior by changing its knowledge structure. The self-learning ability of the system is the basis of the adaptability of the system. The self-learning ability of the systemic is the basis of the adaptability of the systemic. According to the different learning ability of IDSS, the knowledge systemic can be divided into static knowledge systemic and dynamic knowledge systemic. In a knowledge-based systemic, knowledge is always organized in some way and contains certain data structures, so that the inference engine can be operated by search or pattern matching technology. IDSS can be regarded as the organic combination of DSS and ES, which leads to the connection between knowledge base subsystemic and other units of the systemic and the combination of qualitative reasoning of knowledge and quantitative calculation of model. The deep knowledge of simulated domain problems reflects the internal relationship between the phenomena described by premises and conclusions in reasoning rules and improves the systemic’s ability to explain and acquire knowledge. Active make policy-making is an important feature of IDSS. By establishing human cognitive model, active make policy support systemic can provide make policy-makers with a different choice at different problem solving stages, thus forming different problem solving paths.Many systemic services are based on the same cloud computing center, and no matter what systemic computing methods are, they are all the same.Therefore, each numerical value is calculated according to the following formula, such as(5)di=1−dm−1×R,whereR represents each pm service point, and all service points obey the general distribution.Then based on the above definition, a formula of average service rate can be obtained, such as(6)μ=μa=μb=∑i=1Mμijvm.


### 4.2. Research Model of Business Management Intelligent Make Policy Systemic

Since the emergence of intelligent make policy support systemic, due to the great potential of expert systemic technology in the field of management make policy-making, researchers at home and abroad have done a lot of research and integrated the tools and methods of computer science and AI with people’s make policy-making process, and intelligent make policy support systemics with various structures and functions have emerged. In the intelligent make policy support systemic, knowledge management involves reasoning knowledge, description knowledge, and process knowledge, thus supporting the problem solving process. However, the intelligent systemic implementation method cannot guarantee that the developed systemic is an intelligent make policy support systemic. According to the core contents of IDSS research, such as knowledge processing systemic, intelligent model of systemic, systemic architecture, and knowledge processing technology, we can know that this is a discussion on the method and progress of IDSS research based on the evolution of knowledge-based systemic. From the perspective of knowledge, learning, and evolution ability of the systemic, the intelligence of the systemic is gradually deepening.The introduction of data warehouse and web-based data mining technology is one of the key features of Internet-based IDSS, which is different from general IDSS. Its goal is to realize make policy support on wide area network. Complex make policy-making needs to collect feedback message from the systemic for prediction and sometimes requires remote consultation between make policy-makers. The powerful original message base and knowledge base, intelligent knowledge mining, make safe and efficient real-time control and make policy-making, so that IDSS based on the Internet can truly realize long-distance multi-party collaboration in wide-area cluster make policy-making. The traditional make policy support systemic provides the corresponding data and models, and the users choose the corresponding methods and models independently. The make policy-making process is completely controlled by the users, and the systemic only completes the auxiliary computing function. The development, construction, and utilization of intelligent make policy support systemic have important theoretical significance and practical value for enhancing the ability of knowledge development and utilization, improving the intelligence level of make policy-making and improving the application effect of the systemic.ADSS (adaptive make policy systemic) mainly depends on people’s prior knowledge, the systemic running environment is static, and the domain knowledge and reasoning knowledge needed for make policy-making are known in advance. Adaptive make policy support systemic has logic-based reasoning. Besides traditional process calculation and other reasoning forms, it also adopts inductive reasoning to realize dynamic knowledge systemic. Make policy expert systemic is a make policy support systemic based on expert technology. Make policy expert systemic can automatically give make policy conclusions according to make policy conditions, which can be regarded as a special make policy support mode. Make policy-making expert systemic adopts deductive reasoning and deduces the conclusion by using the existing knowledge, and the correctness of the deduction process can be guaranteed. The problem of this systemic is that it needs a complete axiomatic systemic as the basis of reasoning. Integrated make policy support systemic is the development of adaptive make policy support systemic and make policy expert systemic. In fact, this kind of systemic is a more advanced expert systemic, which is similar to human experts in function. It can not only give make policy-making suggestions directly but also analyze the make policy-making process and basis according to the needs of make policy-makers. Make policy-making process has always been the research focus of make policy-making and behavior theory, which is used by many researchers to distinguish make policy support systemic from other message systemics. Conventional make policy-making theory is obviously more inclined to think that people’s thinking always adopts a structured make policy-making process, but in complex environment, especially when the message and knowledge needed for make policy-making are not sufficient, it is limited by people’s cognitive and perceptual abilities. The understanding of make policy-making is the foundation of establishing intelligent make policy support systemic.


### 4.3. Make Policy Support Systemic

Make policy support systemic is a new branch of make policy science. It can provide a make policy-making environment for users to analyze problems, construct models, and simulate the make policy-making process and effects through man-machine interaction. Users can directly participate in the make policy-making process, thus exerting their subjective initiative. Therefore, the systemic itself has adaptability to environmental changes [20]. IDSS uses machine reasoning method to realize make policy support function, while the knowledge of human experts is always limited, and the knowledge that can be expressed by symbolic logic and used for reasoning is even more limited. Many human experts’ knowledge is not already available from the beginning. The core of IDSS is knowledge and knowledge processing. The knowledge used in make policy-making is always related to specific application fields. Different fields have different characteristics in knowledge representation and processing, and different intelligent make policy-making methods have their own characteristics and application scope. The synthesis of methods has become an important way to improve the make policy-making ability of the systemic [21]. The message processing flow reflects the work process and flow as shown in Figure 6.Figure 6
Message processing flow.Through the establishment of its model, the systemic level can be further improved. The systemic can run in various operating systemic environments of various mainframes, minicomputers, and microcomputers, and the data management adopts a general relational database management systemic to establish a friendly man-machine interface. At the same time, it can provide a set of evaluation methods for make policy-makers’ reference, make a reasonable explanation of the behavior in the process of systemic reasoning and the knowledge of the systemic itself, increase the transparency of the systemic, and make the systemic easy to be accepted by users.


## 4.1. IIDS Knowledge Systemic

In the research of DSS, influenced by the structure of multidatabase systemic, data, models, methods, and knowledge are always distinguished, and each database and its management systemic are established, respectively. According to the different make policy tools adopted, a variety of make policy support systemics such as model-based systemic, data-based systemic, and communication-based systemic have appeared successively. Different ways of dealing with knowledge constitute different types of make policy support systemics. In the traditional DSS, the computer execution process is carried out in a predefined process and way. By applying mathematical models and various quantitative make policy-making methods, the auxiliary make policy-making message can be obtained by operating the data. This structure of IDSS takes the knowledge base and its management systemic as the core, and the key technologies are knowledge representation and processing and self-learning ability. The advantage of this structure is that it is convenient for the maintenance of knowledge base and the exchange of data and information, and it can really realize the dominant idea of IDSS’s complementary advantages.The make policy-making ability of DSS is related to knowledge systemic. Actually, any intelligent system can change its behavior by changing its knowledge structure. The self-learning ability of the system is the basis of the adaptability of the system. The self-learning ability of the systemic is the basis of the adaptability of the systemic. According to the different learning ability of IDSS, the knowledge systemic can be divided into static knowledge systemic and dynamic knowledge systemic. In a knowledge-based systemic, knowledge is always organized in some way and contains certain data structures, so that the inference engine can be operated by search or pattern matching technology. IDSS can be regarded as the organic combination of DSS and ES, which leads to the connection between knowledge base subsystemic and other units of the systemic and the combination of qualitative reasoning of knowledge and quantitative calculation of model. The deep knowledge of simulated domain problems reflects the internal relationship between the phenomena described by premises and conclusions in reasoning rules and improves the systemic’s ability to explain and acquire knowledge. Active make policy-making is an important feature of IDSS. By establishing human cognitive model, active make policy support systemic can provide make policy-makers with a different choice at different problem solving stages, thus forming different problem solving paths.Many systemic services are based on the same cloud computing center, and no matter what systemic computing methods are, they are all the same.Therefore, each numerical value is calculated according to the following formula, such as(5)di=1−dm−1×R,whereR represents each pm service point, and all service points obey the general distribution.Then based on the above definition, a formula of average service rate can be obtained, such as(6)μ=μa=μb=∑i=1Mμijvm.


## 4.2. Research Model of Business Management Intelligent Make Policy Systemic

Since the emergence of intelligent make policy support systemic, due to the great potential of expert systemic technology in the field of management make policy-making, researchers at home and abroad have done a lot of research and integrated the tools and methods of computer science and AI with people’s make policy-making process, and intelligent make policy support systemics with various structures and functions have emerged. In the intelligent make policy support systemic, knowledge management involves reasoning knowledge, description knowledge, and process knowledge, thus supporting the problem solving process. However, the intelligent systemic implementation method cannot guarantee that the developed systemic is an intelligent make policy support systemic. According to the core contents of IDSS research, such as knowledge processing systemic, intelligent model of systemic, systemic architecture, and knowledge processing technology, we can know that this is a discussion on the method and progress of IDSS research based on the evolution of knowledge-based systemic. From the perspective of knowledge, learning, and evolution ability of the systemic, the intelligence of the systemic is gradually deepening.The introduction of data warehouse and web-based data mining technology is one of the key features of Internet-based IDSS, which is different from general IDSS. Its goal is to realize make policy support on wide area network. Complex make policy-making needs to collect feedback message from the systemic for prediction and sometimes requires remote consultation between make policy-makers. The powerful original message base and knowledge base, intelligent knowledge mining, make safe and efficient real-time control and make policy-making, so that IDSS based on the Internet can truly realize long-distance multi-party collaboration in wide-area cluster make policy-making. The traditional make policy support systemic provides the corresponding data and models, and the users choose the corresponding methods and models independently. The make policy-making process is completely controlled by the users, and the systemic only completes the auxiliary computing function. The development, construction, and utilization of intelligent make policy support systemic have important theoretical significance and practical value for enhancing the ability of knowledge development and utilization, improving the intelligence level of make policy-making and improving the application effect of the systemic.ADSS (adaptive make policy systemic) mainly depends on people’s prior knowledge, the systemic running environment is static, and the domain knowledge and reasoning knowledge needed for make policy-making are known in advance. Adaptive make policy support systemic has logic-based reasoning. Besides traditional process calculation and other reasoning forms, it also adopts inductive reasoning to realize dynamic knowledge systemic. Make policy expert systemic is a make policy support systemic based on expert technology. Make policy expert systemic can automatically give make policy conclusions according to make policy conditions, which can be regarded as a special make policy support mode. Make policy-making expert systemic adopts deductive reasoning and deduces the conclusion by using the existing knowledge, and the correctness of the deduction process can be guaranteed. The problem of this systemic is that it needs a complete axiomatic systemic as the basis of reasoning. Integrated make policy support systemic is the development of adaptive make policy support systemic and make policy expert systemic. In fact, this kind of systemic is a more advanced expert systemic, which is similar to human experts in function. It can not only give make policy-making suggestions directly but also analyze the make policy-making process and basis according to the needs of make policy-makers. Make policy-making process has always been the research focus of make policy-making and behavior theory, which is used by many researchers to distinguish make policy support systemic from other message systemics. Conventional make policy-making theory is obviously more inclined to think that people’s thinking always adopts a structured make policy-making process, but in complex environment, especially when the message and knowledge needed for make policy-making are not sufficient, it is limited by people’s cognitive and perceptual abilities. The understanding of make policy-making is the foundation of establishing intelligent make policy support systemic.


## 4.3. Make Policy Support Systemic

Make policy support systemic is a new branch of make policy science. It can provide a make policy-making environment for users to analyze problems, construct models, and simulate the make policy-making process and effects through man-machine interaction. Users can directly participate in the make policy-making process, thus exerting their subjective initiative. Therefore, the systemic itself has adaptability to environmental changes [20]. IDSS uses machine reasoning method to realize make policy support function, while the knowledge of human experts is always limited, and the knowledge that can be expressed by symbolic logic and used for reasoning is even more limited. Many human experts’ knowledge is not already available from the beginning. The core of IDSS is knowledge and knowledge processing. The knowledge used in make policy-making is always related to specific application fields. Different fields have different characteristics in knowledge representation and processing, and different intelligent make policy-making methods have their own characteristics and application scope. The synthesis of methods has become an important way to improve the make policy-making ability of the systemic [21]. The message processing flow reflects the work process and flow as shown in Figure 6.Figure 6
Message processing flow.Through the establishment of its model, the systemic level can be further improved. The systemic can run in various operating systemic environments of various mainframes, minicomputers, and microcomputers, and the data management adopts a general relational database management systemic to establish a friendly man-machine interface. At the same time, it can provide a set of evaluation methods for make policy-makers’ reference, make a reasonable explanation of the behavior in the process of systemic reasoning and the knowledge of the systemic itself, increase the transparency of the systemic, and make the systemic easy to be accepted by users.


## 5. The Situation, Problems, and Evaluation of Business Management

With the wide application of various social media, the scope of many enterprises is becoming wider and wider, which makes the amount of text data increase dramatically. As the most common form of data store, text is neither completely unstructured nor completely structured, resulting in incomplete statistics of data, which will make the data missing and make the statistics incomplete, thus making the enterprise lose some money. Under the background of big data, the characteristics of large amount, high dimension, and sparseness of text data add difficulties to the unsupervised learning task of clustering. Many existing text clustering algorithms cannot meet the needs of practical applications in terms of accuracy and real time. With the rapid development of big data, the store problem is the first important problem in the process of big data analysis and processing. The impact of store problem will indirectly affect the management of the whole enterprise, which can be said to be a fatal problem. In the processing and integration of big data, heterogeneity, spatiotemporal characteristics, etc. are also important issues. Improper data processing may lead to data loss, data confusion, and other issues. The management of an enterprise also determines its sales efficiency. Some two kinds of enterprises adopt different methods, one is traditional data, and the other is big data. By establishing a data table, the difference of turnover between the two enterprises can be directly reflected, a method which is more suitable for the enterprise, as shown in Figure7.Figure 7
Comparison of turnover of business management methods.In this era of rapid development, big data is an important track for future development. Unprecedented big data resources are both opportunities and challenges. Up to now, the work of big data analysis and mining is still very limited, so it is necessary to overcome big data.Domain of the above challenges still needs to carry out a lot of work. As a new field of theory and practice, big data thought is a concentrated expression and comprehensive reflection of social ideas, user needs, and the development level of technical means. Big data thought will inevitably have many influences on message analysis. In the big data environment, the technical systemic, process, and method of message analysis are changing. It is expected that in the era of cloud computing and big data, data analysis will be fully integrated into the development of enterprise products.The service and value of data analysis need to be mined in a deeper level, and the results of data analysis can really be used to assist management make policy-making.


## 6. Conclusion

Through the research of big data, we can see that the development of big data is essential in the future. It plays a very important role in the future. The value of data analysis needs to be further explored, and the results of data analysis can really help management make policies. After research, there is a natural connection between big data analysis and intelligence analysis, both of which have similarities and some differences. By summarizing the commonalities between big data analysis and intelligence analysis, we can better understand the opportunities that big data brings to intelligence analysis and strengthen intelligence analysis by the east wind of big data. Through the role of intelligent make policy-making systemic in business management, it can be found that make policy-making systemic plays an important role in business management. In make policy support systemic, the data used for marketing make policy includes two components, namely, static database and dynamic database. The future development of make policy systemic will also show an upward trend. Starting from the connotation and characteristics of b+ig data, this paper briefly introduces the technical systemic of big data analysis and processing. The combination of make policy support systemic with AI technology and Internet greatly improves the flexibility and scalability of traditional make policy support systemic. Combining advanced technologies such as data mining technology, knowledge discovery technology, fuzzy processing technology, and hypertext remote communication technology, the database systemic in the make policy support systemic will automatically update these changed message every day.



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*Source: 3919664-2022-08-05.xml*