# The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

Authors: Yangfan Li
Journal: Advances in Multimedia
 (2022)

---

## Abstract

With the surge in the amount of data in the internal and external environment, the collection, analysis, processing, and storage of the increasing data sources and data volume, as well as the problems of big data management, are the current situation and dilemmas of data management faced by enterprises today. Cognitive science and big data technology can provide good auxiliary support for enterprise management decision-making. This study takes the business information management of cold chain logistics enterprises as an example, aiming at the characteristics of business intelligence data in real applications, based on cognitive science and big data technology, from low-cost and high-performance storage, security management, and big data analysis. This paper is mainly through the research of big data processing theory and key technologies. Based on analyzing the logistics industry’s data access rules and characteristics, this study proposes a hot data prediction model for multilayer hybrid storage systems. It is verified that the prediction model has good accuracy, robustness, and universality. For the application scenario of multitenant distributed data access, a data transparent security management model is proposed. Simulation experiments show that this method can realize data security management when the performance loss is controlled within an acceptable range. Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.


---

## Body

## 1. Introduction

Informatization has become a significant technology to promote the development of commercial modernization [1, 2]. Massive information transactions and production data are generated through various informatization systems in the enterprise [3]. At present, the important production data in the enterprise information system can only be stored for one month. If it is to be stored effectively for a long time, it requires expensive storage costs. In addition, to deal with massive commercial data information, extracting useful data is very important for the production management of enterprise managers [4, 5]. However, traditional data management and data mining technologies are unable to store and analyze large amounts of data efficiently [6, 7]. Therefore, although companies have valuable data resources, they cannot be effectively utilized them [8, 9]. In the process of business information management, the effective storage and use of big data can effectively reduce the cost of enterprise information technology operations [10, 11]. In addition, managers can conduct an in-depth understanding of data, provide quantified decisions and technical support for enterprise management, improve production and sales efficiency, and ultimately improve economic efficiency.With the in-depth integration and integration of the domestic e-commerce and logistics industries, the latest technology in the information field is gradually combined in the research of logistics informatization [12, 13]. A large number of crossresearches on domestic logistics information technology and cloud computing technology are carried out [14, 15]. The development of logistics enterprises depends on the development of e-commerce and promotes the development of e-commerce [16, 17]. On the basis of the original industrial system, logistics, commercial flow, and information flow, combined with the agency and distribution of goods, have developed a socialized logistics and distribution system [18, 19]. Due to the explosive increase of business information data, there is an urgent need to use big data processing technology in modern business information management to discover potentially exploitable information, which can not only provide data support for enterprise management when making decisions but also provide better data services for users of the enterprise [20, 21]. Big data technology has been used by many researchers in business information management [22, 23]. Machine learning can help management handle commercial data to make judgments and decisions on key issues in operations, optimize logistics information systems, optimize services, save space, and control inventory [24, 25]. In the logistics industry, the Internet of Things technology provides it with comprehensive information collection capabilities [26, 27]. RFID, GPS, infrared induction, sensors, and other technologies are used to collect commercial information anytime and anywhere to form a full range of spatiotemporal monitoring data. These massive amounts of data need to be processed urgently, and solving this problem requires the help of big data technology [28]. A complete cloud computing architecture is formed in the analysis system, which can help business managers to grasp the business situation more clearly, provide scientific data for the development trend of the enterprise, and provide decision suggestions for managers.In business development, key technologies such as cloud computing and big data analytics can be used to better manage modern business information [29, 30]. However, researchers working in computer science in this multidisciplinary field of study lack knowledge about business confidence management. Therefore, the specific application of cognitive science and big data technology in business information management is difficult to achieve [31]. The research foundation of information technology in business information management is relatively weak, resulting in the slow development of existing cloud computing and big data analysis technologies in business information management [32, 33]. In the development process of modern logistics, the fourth-party logistics mode appears, to realize the sharing and unification of information flow, capital flow, and logistics of various logistics enterprises and users [34]. The fourth-party logistics information platform provides basic information processing and data processing services for various logistics enterprises and realizes the safe sharing of information [35]. Since the shared information flow can run through the existing logistics and capital flow, the shared economic model can easily develop the shared transportation mode of logistics on the current information platform [36]. Each logistics enterprise constructs an independent transportation network, and the loading rate and unloading rates of vehicles are heavily dependent on the supply of goods. However, a large number of vehicles travel on the same route during transportation, resulting in a huge waste of transportation resources. Due to the lack of an information platform for logistics data sharing, the current research on logistics sharing transportation is still in its infancy. However, due to different application scenarios, the existing theoretical research results of transportation route optimization and transportation sharing optimization in the field of intelligent transportation are difficult to be directly applied in logistics shared transportation, and the logistics industry needs to process large-scale data such as trajectories and packages in real time. In transportation scheduling, parallel computing solutions suitable for big data analysis and processing need to be considered, to meet users’ needs for computing performance.This study takes logistics information management as an example to illustrate the application of cognitive science and big data technology in business information management. Massive of transaction data, management data, and surveillance video data will be generated in the information management system of commercial logistics enterprises, and the analysis and real-time processing technology of big data will be studied. The research topic belongs to the field of big data processing and application, and the research results can provide theoretical and technical guidance for the informatization construction of local trade and logistics cities. The research content includes three contents: the first is the storage of commercial data, the next is the deal with commercial data, and the last one is the analysis and application of commercial data. In this thesis, we study and deeply analyze data storage, computation, and clues. The distributed storage of big data is the foundation, and the computation is the support for data analysis. The ultimate goal is to analyze the data and realize the application of big data in the decision-making of commercial logistics enterprises. This paper focuses on the design and implementation of the logistics big data processing basic system, as well as the application of big data analysis and calculation. Based on analyzing the laws and characteristics of data access in the logistics industry, a hot-spot data prediction model for multilayer hybrid storage systems is proposed. Aiming at the application scenario of multitenant distributed data access, a data-transparent security management model is proposed. On the big data analysis platform, two parallel algorithms of collaborative filtering and label optimization of logistics distribution services are implemented, which solves the performance bottleneck of recommendation service computing.


## 2. Data Security in Storage

At present, the commonly used data storage method is multilevel hybrid distributed storage, which is a complex storage system, and the multitenant storage outsourcing model has become the main application scenario. However, business survey results show that data security has gradually become a prominent obstacle to the development of distributed storage systems and cloud storage applications. Multilevel hybrid distributed storage is a complex storage system, and the multitenant storage outsourcing model has become the main application scenario. However, business survey results show that data security has gradually become a prominent obstacle to the development of distributed storage systems and cloud storage applications. At present, only 20% of users are willing to put private data in the cloud or distributed storage system, and 50% of users only want to store data backup and disaster recovery data in the cloud. Therefore, establishing an effective data security model for multilevel hybrid distributed storage systems has become a new challenge. There are two fundamental problems in a multilevel hybrid distributed storage system: (1) the optimistic assumption of the trusted domain of distributed storage systems leads to ignoring attacks and threats from within the storage system; (2) the storage management mechanism of the multilevel hybrid storage system is more complex, such as copy mechanism, load balancing, data synchronization, and data migration. Complex data management requires a corresponding data security mechanism to work together. Aiming at these two fundamental problems, this section focuses on the data security model of multilevel distributed hybrid storage. Design the data security model from the perspective of a computer system to realize the effective integration of data security control and storage management in the hybrid storage system. The cooperative working mechanism of control and data storage plane is studied, and an authentication scheme of multilevel key control is proposed.


### 2.1. Key Technologies of the Data Security Model

Data security in HMSS is guaranteed by multiple levels of keys. This scheme not only enhances data security but also reduces the communication cost during key usage and maintenance. The multilayer key management and distribution usage scheme is shown in Figure1. Data security in HMSS is guaranteed by multilevel keys. Data privacy controls address two key issues: (1) structural design of data files and (2) key management in a distributed key environment. For the structural design of the data file, the data file is logically divided into two parts in the data storage node: the metadata file and the data file. The metadata file (mod-file) stores attribute related to data security and storage management, such as access information, root hash linked list, data file popularity, and storage node location. The data file (d-file) stores the ciphertext data of the file. Secondly, the key management of HMSS adopts a hierarchical management scheme, which not only enhances data security but also reduces the communication cost in the process of key use and maintenance.Figure 1
HMSS data logical structure and key management.Data security model HMSS is to realize the effective integration of the storage management plane and the data security control plane; it is necessary to realize the cooperative management and data synchronization of the two planes. Collaborative management is implemented using event-triggered data synchronization. To realize collaborative management, two main problems need to be solved: the basic data structure and collaborative events to realize data synchronization in a distributed environment. The HMSS data synchronization mechanism is shown in Figure2.Figure 2
Schematic diagram of HMSS collaboration and data synchronization.


### 2.2. Testing and Analysis

This section tests the performance consumed by HMSS data security management, regardless of the network latency and system performance consumed by the client reading and writing data. By writing a test script to simulate virtual users to initiate data access requests, in the test script, starting from 100 virtual users, the initial step is 50 virtual users, and when more than 1000 virtual users are adjusted, the virtual user adjustment step is increased to 100 users. The experimental benchmark authentication model is a single authentication server working mode, where an authentication server deployed in the LAN is responsible for the authentication and data encryption management of 30 storage nodes. All virtual users have to initiate data authentication and encryption requests to this authentication server. The experimental observation points are the performance metrics of the first-level authentication server AS0: CPU utilization, memory utilization, network utilization, and disk utilization. As shown in Figures3–6, the horizontal coordinates indicate the number of virtual users and the vertical coordinates indicate the utilization of various hardware metrics.Figure 3
Comparison of CPU utilization between HMSS and benchmark authentication mode.Figure 4
Comparison of memory utilization between HMSS and benchmark authentication mode.Figure 5
Comparison of network bandwidth utilization between HMSS and benchmark authentication mode.Figure 6
Disk utilization comparison between HMSS and benchmark certification mode.To improve the adaptability to complex storage management systems, this section first realizes the decoupling of storage management and data security management from the logical level, forming two independent spaces for storage management and data security. In the storage management layer, the scheme of data storage is allowed to be adjusted without affecting the design of data security. Experiments show that the method proposed in this study has better performance for file reading, and the performance loss caused by data security is within the acceptable range for users.


## 2.1. Key Technologies of the Data Security Model

Data security in HMSS is guaranteed by multiple levels of keys. This scheme not only enhances data security but also reduces the communication cost during key usage and maintenance. The multilayer key management and distribution usage scheme is shown in Figure1. Data security in HMSS is guaranteed by multilevel keys. Data privacy controls address two key issues: (1) structural design of data files and (2) key management in a distributed key environment. For the structural design of the data file, the data file is logically divided into two parts in the data storage node: the metadata file and the data file. The metadata file (mod-file) stores attribute related to data security and storage management, such as access information, root hash linked list, data file popularity, and storage node location. The data file (d-file) stores the ciphertext data of the file. Secondly, the key management of HMSS adopts a hierarchical management scheme, which not only enhances data security but also reduces the communication cost in the process of key use and maintenance.Figure 1
HMSS data logical structure and key management.Data security model HMSS is to realize the effective integration of the storage management plane and the data security control plane; it is necessary to realize the cooperative management and data synchronization of the two planes. Collaborative management is implemented using event-triggered data synchronization. To realize collaborative management, two main problems need to be solved: the basic data structure and collaborative events to realize data synchronization in a distributed environment. The HMSS data synchronization mechanism is shown in Figure2.Figure 2
Schematic diagram of HMSS collaboration and data synchronization.


## 2.2. Testing and Analysis

This section tests the performance consumed by HMSS data security management, regardless of the network latency and system performance consumed by the client reading and writing data. By writing a test script to simulate virtual users to initiate data access requests, in the test script, starting from 100 virtual users, the initial step is 50 virtual users, and when more than 1000 virtual users are adjusted, the virtual user adjustment step is increased to 100 users. The experimental benchmark authentication model is a single authentication server working mode, where an authentication server deployed in the LAN is responsible for the authentication and data encryption management of 30 storage nodes. All virtual users have to initiate data authentication and encryption requests to this authentication server. The experimental observation points are the performance metrics of the first-level authentication server AS0: CPU utilization, memory utilization, network utilization, and disk utilization. As shown in Figures3–6, the horizontal coordinates indicate the number of virtual users and the vertical coordinates indicate the utilization of various hardware metrics.Figure 3
Comparison of CPU utilization between HMSS and benchmark authentication mode.Figure 4
Comparison of memory utilization between HMSS and benchmark authentication mode.Figure 5
Comparison of network bandwidth utilization between HMSS and benchmark authentication mode.Figure 6
Disk utilization comparison between HMSS and benchmark certification mode.To improve the adaptability to complex storage management systems, this section first realizes the decoupling of storage management and data security management from the logical level, forming two independent spaces for storage management and data security. In the storage management layer, the scheme of data storage is allowed to be adjusted without affecting the design of data security. Experiments show that the method proposed in this study has better performance for file reading, and the performance loss caused by data security is within the acceptable range for users.


## 3. Recommended Models and Algorithms for Mass Logistics Data Distribution

This section is aimed at studying the recommendation model applicable to logistics distribution and the implementation of distributed parallel algorithms in business information management. The advantages of the algorithm are as follows: (1) the algorithm can collect data quickly in real time, although the data in the logistics operation environment is exceptionally complex. (2) The sparsity of high-dimensional data in this algorithm is more obvious in the context of big data. (3) In the fusion of multisource data, the mainstream data types of this algorithm are unstructured and semistructured stream data which are conducive to storage and processing. Data in the logistics industry is very complex, leading to serious problems such as noisy and redundant data. To address the above-mentioned problems, it is necessary to study the recommendation algorithm for logistics distribution services. In addition, due to data accumulation, algorithms suitable for distributed parallel processing are developed to meet the user’s demand for processing massive data. This research can improve the user experience, increase the utilization of data resources in the logistics industry, and bring economic benefits to the business management of enterprises.


### 3.1. Logistics Distribution Recommendation Model

In the condition of massive business data, the high-dimensional sparsity of collaborative recommender systems is more obvious, and because the data types become more complex, the problem of redundant and noisy data is more serious. The processing of real-time streaming data also puts forward new requirements on the computational performance of recommender systems. To better deal with high-dimensional sparse data, reduce the sensitivity of redundant and noisy data, and reduce the computational complexity of the algorithm. In this section, the matrix decomposition method is used to design a logistics distribution recommendation model, and the recommendation is transformed into a matrix decomposition problem, and the sparse user rating matrix is mapped to a specific set of users and items, which can effectively reduce the sensitivity of high-dimensional data. On the other hand, the recommendation system can collect more contextual data in the big data analysis environment. This section focuses on the optimization of recommended labels and uses the optimized labels to build a more accurate logistics distribution recommendation model.First, we construct a network graph about the previous relationship between the three parts of user-resource-tag and then use a tag ranking algorithm to obtain the popularity of tags. At the same time, considering the characteristics of tags decaying over time, the tags whose popularity is ranked in the front are filtered out, and the garbage or redundant tags whose popularity is lower than a certain threshold are deleted. First, three network graphs are established with users, recommended resources, and labels as network nodes. There are directions and weights between nodes in the three-part network graph. The three-part network graph is formally expressed asG=V,E,W, where U=u1,u2,⋯,um represents the user set; R=r1,r2,⋯,rn represents the set of resources to be recommended; T=t1,t2,⋯,tl represents the set of tags; V is the set of all users, resources, and tag nodes; V=u1,u2,⋯,um,r1,r2,⋯,rn,t1,t2,⋯,tl; E is the set of directed edges; E=euv,ers,etp,eur,eut,ert; euv denote the user edge from u to user v; ers denote the edge from resource r to resource s; etp denote the edge from label t to label p; eur denote the edge from user u to resource r; eut denote the edge from user u to label t; ert denote the resource, r denote the edge to label t; and W denote the set of edge weights.The six methods for calculating the weights of directed edges between nodes in a tripartite graph network are as follows:(1)
Weights between user and user nodes. Set user nodesu1 and u2; if u1 and u2 use the same label or label the same resource, then there is a bidirectional edge between u1 and u2, and its weight is(1)wu1,u2=12Tu1∩Tu2Tu1∪Tu2+Ru1∩Ru2Ru1∪Ru2Tu1 and Tu2 represent the set of labels marked by u1 and u2, respectively; Ru1 and Ru2 represent the set of resources to be recommended marked by u1 and u2, respectively.
(2)
Weights between resource and resource nodes. Assuming resource nodesr1 and r2, the same user or the same label has marked r1 and r2; then, there is a bidirectional edge between r1 and r2, and its weight is(2)wr1,r2=12Tr1∩Tr2Tr1∪Tr2+Rr1∩Rr2Rr1∪Rr2Tr1 and Tr2 represent the set of labels labeled r1 and r2, respectively; Ur1 and Ur2 represent the set of users labeled r1 and r2, respectively.
(3)
Weights between tag and tag nodes. Assuming label nodest1 and t2, if the same user or the same resource has labeled t1 and t2, then there is a bidirectional edge between t1 and t2, and its weight is(3)wt1,t2=12Tt1∩Tt2Tt1∪Tt2+Rt1∩Rt2Rt1∪Rt2Ut1 and Ut2 represent user sets marked by t1 and t2, respectively, and Rt1 and Rt2 represent resource sets marked by t1 and t2, respectively.
(4)
Weights between user and resource nodes. If userui labels resource rj, there is a directed edge from ui to rj, and the weight is set to 1(5)
Weights between user and tag nodes. If userui uses label tj, there is a directed edge from ui to tj; then, the weight is(4)wui,tj=Frequi,tj∑t∈TuiFrequi,tT [25] represents the set of tags used by ui, and Frequi,tj represents the number of times the user ui has used the tag tj.
(6)
Weights between resource and tag nodes. Iftj labels resource ri, a directed edge exists from ri to tj with the following weights:(5)wri,tj=Freqri,tj∑t∈TriFreqri,tTri represents the set of labels that label ri, and Freqri,tj represents the number of times tj is used to label ri.The tag optimization strategy is based on three assumptions: (1) high-quality users who use high-quality tags to tag resources have higher value; (2) users who use high-quality tags to tag high-value resources also have higher quality; and (3) the labels used by high-quality users to label high-value resources are of better quality; the above assumptions constitute a mutually reinforcing learning relationship. This mutually reinforcing learning relationship is introduced based on the search engine’s scoring algorithm (Hyperlink-Induced Topic Search, HITS) to obtain the popularity of users, resources, and recommended tags. Sorting according to the popularity of the tags, the tags with low popularity are determined as spam tags and deleted.The HITS algorithm that introduces mutual reinforcement learning is as follows: Any nodegkauth,hub in the network graph G is represented by two attribute values, and the initial position of each node is set to (1,1). At the beginning of the algorithm, all nodes in the network graph G are input, and the following updates are performed for each node gk:
(6)authgk=∑i=1andi≠knhubgi×wgk,gi,hubgk=∑i=1andi≠knauthgi×wgk,gi,where the sum of nodes in G is denoted by n and wgk,gi represents the weight of nodes gi to gk. In each node iterative update, the attribute values of each node should be renormalized to prevent numerical overflow. After K iterations converge, return the auth values of all label nodes. To reduce data size, delete all tags with auth value that is lower than ε. In the recommendation process of social network tags, there is another problem that needs to be solved: the recommended tags reduce the recommendation effect over time. In order to comprehensively consider the characteristic that the quality of labels decreases over time, a temporal attention function is introduced here to realize the decay of labels over time:
(7)fticur=1/1+bticur−ti.In Equation (7), fticur is the label time attention function, parameter b controls the decay speed, and ticur and ti are the current time and the time when the label was created, respectively.


### 3.2. Logistics Distribution Recommendation Algorithm

Based on user preferences and resource characteristics, this section recommends a business information management model characterized by tag vectors. Although these tags are widely used and popular with the public, these tags lack personalization characteristics. For example, users mark “big data” tags in network resources, and such popular tags cannot reflect the characteristic of the recommended resources. Hence, judging and limiting the weight of these labels are necessary.First, a preference function for user tags needs to be constructed. Based on how often users use tags, the preference function can be determined, that is, when a user uses a tag frequently, which means the user prefers this tag. The function for user tags is(8)pui,tj=tfui,tj⋅idfutj,where tfui,tj is the number of resources marked by the user ui, as shown in the following equation:
(9)tfui,tj=wui,tj,idfutj=logMmj,where idfutj represents the ratio between the number of users and the number of tag tj. Therefore, ui can be defined as
(10)pui,t=pui,t1,pui,t2⋯pui,tj.Second, establish a resource tag preference function. The tag preference function that defines the resource is as follows:(11)pri,tj=tfri,tj⋅idfrtj,wheretfri,tjrepresents the number of users who haverilabel resource with labeltj, which is
(12)tfri,tj=wri,tj,idfrtj=logNnj,where idfrtj represents the ratio of the number of resources to the number of resources labeled with tag ti. So the resource ri can be expressed by
(13)pri,t=pri,t1,pri,t2⋯pri,tjThen, the user’s score for the resource can be calculated by(14)Preu,r=∑t∈Tu∩Trpu,t×pr,t.The recommendation model needs to input various vector data of users and resources, and then, it will automatically output the information of recommended resources. The basic principle is a matrix-based recommendation model that decomposes the user’s preference matrix for a resource into a low-dimensional feature matrix, a process shown in the following equation:(15)Rm×n=Dm×FVn×FT,where Dm×F represents the user feature matrix, Vn×F represents the resource feature matrix, and F denotes the amount of latent features. From this, the feature matrix of user and the feature matrix of resource can be applied to estimate the user ui’s score for the resource rj:
(16)ri,j=∑f=1Fdif×vjfT.The function of objective loss is(17)E=∑i∈Uandj∈Rri,j−∑f=1Fdif×vjfT2+βdi2+vj2.The recursive formula of the iterative process is as follows:(18)dif⟵dif+α×ei,j×vjf−βdif,vjf⟵vjf+α×ei,j×dif−βvjf,where α is the learning rate and ei,j is the deviation between the score known and the value predicted. The above process is repeated n times unless the objective function E is less than the threshold predefined, and the final feature vectors D and V were netted.


## 3.1. Logistics Distribution Recommendation Model

In the condition of massive business data, the high-dimensional sparsity of collaborative recommender systems is more obvious, and because the data types become more complex, the problem of redundant and noisy data is more serious. The processing of real-time streaming data also puts forward new requirements on the computational performance of recommender systems. To better deal with high-dimensional sparse data, reduce the sensitivity of redundant and noisy data, and reduce the computational complexity of the algorithm. In this section, the matrix decomposition method is used to design a logistics distribution recommendation model, and the recommendation is transformed into a matrix decomposition problem, and the sparse user rating matrix is mapped to a specific set of users and items, which can effectively reduce the sensitivity of high-dimensional data. On the other hand, the recommendation system can collect more contextual data in the big data analysis environment. This section focuses on the optimization of recommended labels and uses the optimized labels to build a more accurate logistics distribution recommendation model.First, we construct a network graph about the previous relationship between the three parts of user-resource-tag and then use a tag ranking algorithm to obtain the popularity of tags. At the same time, considering the characteristics of tags decaying over time, the tags whose popularity is ranked in the front are filtered out, and the garbage or redundant tags whose popularity is lower than a certain threshold are deleted. First, three network graphs are established with users, recommended resources, and labels as network nodes. There are directions and weights between nodes in the three-part network graph. The three-part network graph is formally expressed asG=V,E,W, where U=u1,u2,⋯,um represents the user set; R=r1,r2,⋯,rn represents the set of resources to be recommended; T=t1,t2,⋯,tl represents the set of tags; V is the set of all users, resources, and tag nodes; V=u1,u2,⋯,um,r1,r2,⋯,rn,t1,t2,⋯,tl; E is the set of directed edges; E=euv,ers,etp,eur,eut,ert; euv denote the user edge from u to user v; ers denote the edge from resource r to resource s; etp denote the edge from label t to label p; eur denote the edge from user u to resource r; eut denote the edge from user u to label t; ert denote the resource, r denote the edge to label t; and W denote the set of edge weights.The six methods for calculating the weights of directed edges between nodes in a tripartite graph network are as follows:(1)
Weights between user and user nodes. Set user nodesu1 and u2; if u1 and u2 use the same label or label the same resource, then there is a bidirectional edge between u1 and u2, and its weight is(1)wu1,u2=12Tu1∩Tu2Tu1∪Tu2+Ru1∩Ru2Ru1∪Ru2Tu1 and Tu2 represent the set of labels marked by u1 and u2, respectively; Ru1 and Ru2 represent the set of resources to be recommended marked by u1 and u2, respectively.
(2)
Weights between resource and resource nodes. Assuming resource nodesr1 and r2, the same user or the same label has marked r1 and r2; then, there is a bidirectional edge between r1 and r2, and its weight is(2)wr1,r2=12Tr1∩Tr2Tr1∪Tr2+Rr1∩Rr2Rr1∪Rr2Tr1 and Tr2 represent the set of labels labeled r1 and r2, respectively; Ur1 and Ur2 represent the set of users labeled r1 and r2, respectively.
(3)
Weights between tag and tag nodes. Assuming label nodest1 and t2, if the same user or the same resource has labeled t1 and t2, then there is a bidirectional edge between t1 and t2, and its weight is(3)wt1,t2=12Tt1∩Tt2Tt1∪Tt2+Rt1∩Rt2Rt1∪Rt2Ut1 and Ut2 represent user sets marked by t1 and t2, respectively, and Rt1 and Rt2 represent resource sets marked by t1 and t2, respectively.
(4)
Weights between user and resource nodes. If userui labels resource rj, there is a directed edge from ui to rj, and the weight is set to 1(5)
Weights between user and tag nodes. If userui uses label tj, there is a directed edge from ui to tj; then, the weight is(4)wui,tj=Frequi,tj∑t∈TuiFrequi,tT [25] represents the set of tags used by ui, and Frequi,tj represents the number of times the user ui has used the tag tj.
(6)
Weights between resource and tag nodes. Iftj labels resource ri, a directed edge exists from ri to tj with the following weights:(5)wri,tj=Freqri,tj∑t∈TriFreqri,tTri represents the set of labels that label ri, and Freqri,tj represents the number of times tj is used to label ri.The tag optimization strategy is based on three assumptions: (1) high-quality users who use high-quality tags to tag resources have higher value; (2) users who use high-quality tags to tag high-value resources also have higher quality; and (3) the labels used by high-quality users to label high-value resources are of better quality; the above assumptions constitute a mutually reinforcing learning relationship. This mutually reinforcing learning relationship is introduced based on the search engine’s scoring algorithm (Hyperlink-Induced Topic Search, HITS) to obtain the popularity of users, resources, and recommended tags. Sorting according to the popularity of the tags, the tags with low popularity are determined as spam tags and deleted.The HITS algorithm that introduces mutual reinforcement learning is as follows: Any nodegkauth,hub in the network graph G is represented by two attribute values, and the initial position of each node is set to (1,1). At the beginning of the algorithm, all nodes in the network graph G are input, and the following updates are performed for each node gk:
(6)authgk=∑i=1andi≠knhubgi×wgk,gi,hubgk=∑i=1andi≠knauthgi×wgk,gi,where the sum of nodes in G is denoted by n and wgk,gi represents the weight of nodes gi to gk. In each node iterative update, the attribute values of each node should be renormalized to prevent numerical overflow. After K iterations converge, return the auth values of all label nodes. To reduce data size, delete all tags with auth value that is lower than ε. In the recommendation process of social network tags, there is another problem that needs to be solved: the recommended tags reduce the recommendation effect over time. In order to comprehensively consider the characteristic that the quality of labels decreases over time, a temporal attention function is introduced here to realize the decay of labels over time:
(7)fticur=1/1+bticur−ti.In Equation (7), fticur is the label time attention function, parameter b controls the decay speed, and ticur and ti are the current time and the time when the label was created, respectively.


## 3.2. Logistics Distribution Recommendation Algorithm

Based on user preferences and resource characteristics, this section recommends a business information management model characterized by tag vectors. Although these tags are widely used and popular with the public, these tags lack personalization characteristics. For example, users mark “big data” tags in network resources, and such popular tags cannot reflect the characteristic of the recommended resources. Hence, judging and limiting the weight of these labels are necessary.First, a preference function for user tags needs to be constructed. Based on how often users use tags, the preference function can be determined, that is, when a user uses a tag frequently, which means the user prefers this tag. The function for user tags is(8)pui,tj=tfui,tj⋅idfutj,where tfui,tj is the number of resources marked by the user ui, as shown in the following equation:
(9)tfui,tj=wui,tj,idfutj=logMmj,where idfutj represents the ratio between the number of users and the number of tag tj. Therefore, ui can be defined as
(10)pui,t=pui,t1,pui,t2⋯pui,tj.Second, establish a resource tag preference function. The tag preference function that defines the resource is as follows:(11)pri,tj=tfri,tj⋅idfrtj,wheretfri,tjrepresents the number of users who haverilabel resource with labeltj, which is
(12)tfri,tj=wri,tj,idfrtj=logNnj,where idfrtj represents the ratio of the number of resources to the number of resources labeled with tag ti. So the resource ri can be expressed by
(13)pri,t=pri,t1,pri,t2⋯pri,tjThen, the user’s score for the resource can be calculated by(14)Preu,r=∑t∈Tu∩Trpu,t×pr,t.The recommendation model needs to input various vector data of users and resources, and then, it will automatically output the information of recommended resources. The basic principle is a matrix-based recommendation model that decomposes the user’s preference matrix for a resource into a low-dimensional feature matrix, a process shown in the following equation:(15)Rm×n=Dm×FVn×FT,where Dm×F represents the user feature matrix, Vn×F represents the resource feature matrix, and F denotes the amount of latent features. From this, the feature matrix of user and the feature matrix of resource can be applied to estimate the user ui’s score for the resource rj:
(16)ri,j=∑f=1Fdif×vjfT.The function of objective loss is(17)E=∑i∈Uandj∈Rri,j−∑f=1Fdif×vjfT2+βdi2+vj2.The recursive formula of the iterative process is as follows:(18)dif⟵dif+α×ei,j×vjf−βdif,vjf⟵vjf+α×ei,j×dif−βvjf,where α is the learning rate and ei,j is the deviation between the score known and the value predicted. The above process is repeated n times unless the objective function E is less than the threshold predefined, and the final feature vectors D and V were netted.


## 4. Experiment Verification and Improvement

### 4.1. Simulation Experiment Verification

The experimental data set is the data set on Delicious, with a total of 10,000 users, 9,007 resources, and 4,438 tags. Since the labels used by users in the data set have timestamps, the records of each user in the data set are sorted by time in the experiment, the first 20% of the records are used as test data, and the last 80% of records are used as training data. In the experiment, the mean absolute error (MAE) is used as the evaluation metric to evaluate the degree of deviation between the resource score predicted by the recommendation algorithm and the user’s actual resource score. The calculation formula is as follows:(19)MAE=∑u∈U∑i∈Rtestupreu,i−rui∑u∈URtestu.For the value of this parameter, an experimental method is used to explore the optimal value. As shown in Figure7, the abscissa is the popularity threshold, and the ordinate is the average error of recommendation. The prediction error of different parameter values is verified by continuously increasing the value of the popular threshold.Figure 7
The effect of tag popularity thresholdε on MAE.As shown in Figure8, the user-resource matrix [35], user-label matrix (UT), and resource-label matrix (RT) in the original data set are compared with the sparsity of the optimized label matrix (the matrix sparsity is the null value in the matrix. The ratio of the number of elements to the total number of elements in the matrix). The comparison found that after label optimization, the sparsity of the three matrices was reduced, especially the user-label matrix and the resource-label matrix. This shows that label optimization can play a role in solving data sparsity from another perspective.Figure 8
Sparsity comparison of matrices.Figure9 lists the influence of the learning rate parameter α in various recommendation algorithms on the recommendation accuracy. When the value of α is small, the MAE of the four algorithms changes quickly and is relatively low because the gradient descent method will quickly fall into the local optimal value in the iteration. Gradually increasing, the algorithm OTTA-SVD always maintains a better effect on different α values, and compared with tag-SVD, it can be seen that the recommendation accuracy is improved after optimizing the tags.Figure 9
Effect of learning rateα on MAE.Figure10 lists the effect of regularization coefficient β on recommendation accuracy in various algorithms. With the increase of β, the MAE values of the four recommendation algorithms all show a trend of first decreasing and then increasing, and OTTA-SVD has a better recommendation effect than the other three algorithms.Figure 10
Influence of regularization coefficientβ on MAE.Figure11 lists the influence of the number of latent features F on the recommendation accuracy in various recommendation algorithms. Simulation experiments show that the recommendation effect of the OTTA-SVD algorithm is the best.Figure 11
Influence of the number of latent featuresF on MAE.In conclusion, the OTTA-SVD algorithm can not only optimize the label data but also reduce the data sparsity and the overfitting problem of the recommendation model, and the results on the real data set show that OTTA-SVD has better recommendation accuracy. The research of this chapter can improve the efficiency of distribution in logistics enterprises. In addition, collecting customer feedback information can improve the service quality of the distribution company, enhance the brand effect of the company, and accelerate the development of the company.


### 4.2. Method Improvements

To promote the computing performance of the recommendation model, this section studies the implementation of the parallel algorithm based on the existing matrix factorization recommendation algorithm and improves the performance by implementing the parallel algorithm of the memory iterative computing mode in the distributed cluster. The system consists of three parts: data collection, data storage, calculation, and data analysis, as shown in Figure12.Figure 12
Logistics big data real-time processing system.To further improve the efficiency, the parallel transplantation of OTTA-SVD to the Spark system is carried out to improve the performance of the recommendation algorithm in logistics big data applications. First, use elastic data sets to realize parallel segmentation of massive data, and then, use parallel operation functions in Spark to describe complex parallel operation logic. Encapsulate different user data and user’s scoring data on logistics services into elastic data set RDD, and then, implement parallel operations such as Map, Filter, and Union on different RDDs.Figure13 provides the performance of the Hadoop and Spark versions of the recommendation algorithms ICF and OTTA-SVD on various data scales. The abscissa is the number of users, reflecting the user size in the recommendation algorithm; the ordinate is the calculation time of the recommendation task. Since the calculation time is shorter, the calculation performance is better. It can be seen that the Spark parallel algorithm has obvious advantages.Figure 13
Comparison of different parallel performances of recommendation algorithms in different scale data sets.
(a)
Two parallel versions of ICF(b)
Two parallel versions of OTTA-SVDTo verify the effect of scalability on speedup in the Spark cluster, select the Spark version of the two recommended algorithms, OTTA-SVD and ICF, and use 20,000 and 40,000 user data nodes, respectively, to implement the experiment and calculate the parallel speedup ratio of 4, 8, 16, and 32 nodes to analyze the cluster scalability. The result is shown in Figure14.Figure 14
Performance comparison of two Spark parallel algorithms on different node clusters.
(a)
20000 user node data set(b)
40000 user node data setIn the process of operation, logistics information platform generates huge amount of business data. The logistics distribution recommendation service is obtained based on the calculation of massive data, and the core algorithm recommended by this study is used to help enterprises quickly select the distribution solution suitable for user needs. This section analyzes the advantages and disadvantages of logistics service recommendation models and parallel distributed algorithms and discusses the related technologies for business data analysis, including big data frameworks, key data storage systems, and Spark and Hadoop. It is found that Hadoop and Spark big data computing frameworks are good for analyzing massive amounts of data. However, the recommendation algorithm involves a large number of iterative operations, and the way the intermediate data are stored affects the performance of the parallel algorithm. Spark parallel processing algorithms achieve better parallel acceleration performance at large data set sizes, which is about 10 times more efficient than Hadoop. In addition, the recommendation algorithm is very sensitive to the basic configuration parameters of data nodes. Coping with different data sizes requires constant experimentation to determine the configuration parameters. This is a shortcoming of the current study and will be investigated in the future.


## 4.1. Simulation Experiment Verification

The experimental data set is the data set on Delicious, with a total of 10,000 users, 9,007 resources, and 4,438 tags. Since the labels used by users in the data set have timestamps, the records of each user in the data set are sorted by time in the experiment, the first 20% of the records are used as test data, and the last 80% of records are used as training data. In the experiment, the mean absolute error (MAE) is used as the evaluation metric to evaluate the degree of deviation between the resource score predicted by the recommendation algorithm and the user’s actual resource score. The calculation formula is as follows:(19)MAE=∑u∈U∑i∈Rtestupreu,i−rui∑u∈URtestu.For the value of this parameter, an experimental method is used to explore the optimal value. As shown in Figure7, the abscissa is the popularity threshold, and the ordinate is the average error of recommendation. The prediction error of different parameter values is verified by continuously increasing the value of the popular threshold.Figure 7
The effect of tag popularity thresholdε on MAE.As shown in Figure8, the user-resource matrix [35], user-label matrix (UT), and resource-label matrix (RT) in the original data set are compared with the sparsity of the optimized label matrix (the matrix sparsity is the null value in the matrix. The ratio of the number of elements to the total number of elements in the matrix). The comparison found that after label optimization, the sparsity of the three matrices was reduced, especially the user-label matrix and the resource-label matrix. This shows that label optimization can play a role in solving data sparsity from another perspective.Figure 8
Sparsity comparison of matrices.Figure9 lists the influence of the learning rate parameter α in various recommendation algorithms on the recommendation accuracy. When the value of α is small, the MAE of the four algorithms changes quickly and is relatively low because the gradient descent method will quickly fall into the local optimal value in the iteration. Gradually increasing, the algorithm OTTA-SVD always maintains a better effect on different α values, and compared with tag-SVD, it can be seen that the recommendation accuracy is improved after optimizing the tags.Figure 9
Effect of learning rateα on MAE.Figure10 lists the effect of regularization coefficient β on recommendation accuracy in various algorithms. With the increase of β, the MAE values of the four recommendation algorithms all show a trend of first decreasing and then increasing, and OTTA-SVD has a better recommendation effect than the other three algorithms.Figure 10
Influence of regularization coefficientβ on MAE.Figure11 lists the influence of the number of latent features F on the recommendation accuracy in various recommendation algorithms. Simulation experiments show that the recommendation effect of the OTTA-SVD algorithm is the best.Figure 11
Influence of the number of latent featuresF on MAE.In conclusion, the OTTA-SVD algorithm can not only optimize the label data but also reduce the data sparsity and the overfitting problem of the recommendation model, and the results on the real data set show that OTTA-SVD has better recommendation accuracy. The research of this chapter can improve the efficiency of distribution in logistics enterprises. In addition, collecting customer feedback information can improve the service quality of the distribution company, enhance the brand effect of the company, and accelerate the development of the company.


## 4.2. Method Improvements

To promote the computing performance of the recommendation model, this section studies the implementation of the parallel algorithm based on the existing matrix factorization recommendation algorithm and improves the performance by implementing the parallel algorithm of the memory iterative computing mode in the distributed cluster. The system consists of three parts: data collection, data storage, calculation, and data analysis, as shown in Figure12.Figure 12
Logistics big data real-time processing system.To further improve the efficiency, the parallel transplantation of OTTA-SVD to the Spark system is carried out to improve the performance of the recommendation algorithm in logistics big data applications. First, use elastic data sets to realize parallel segmentation of massive data, and then, use parallel operation functions in Spark to describe complex parallel operation logic. Encapsulate different user data and user’s scoring data on logistics services into elastic data set RDD, and then, implement parallel operations such as Map, Filter, and Union on different RDDs.Figure13 provides the performance of the Hadoop and Spark versions of the recommendation algorithms ICF and OTTA-SVD on various data scales. The abscissa is the number of users, reflecting the user size in the recommendation algorithm; the ordinate is the calculation time of the recommendation task. Since the calculation time is shorter, the calculation performance is better. It can be seen that the Spark parallel algorithm has obvious advantages.Figure 13
Comparison of different parallel performances of recommendation algorithms in different scale data sets.
(a)
Two parallel versions of ICF(b)
Two parallel versions of OTTA-SVDTo verify the effect of scalability on speedup in the Spark cluster, select the Spark version of the two recommended algorithms, OTTA-SVD and ICF, and use 20,000 and 40,000 user data nodes, respectively, to implement the experiment and calculate the parallel speedup ratio of 4, 8, 16, and 32 nodes to analyze the cluster scalability. The result is shown in Figure14.Figure 14
Performance comparison of two Spark parallel algorithms on different node clusters.
(a)
20000 user node data set(b)
40000 user node data setIn the process of operation, logistics information platform generates huge amount of business data. The logistics distribution recommendation service is obtained based on the calculation of massive data, and the core algorithm recommended by this study is used to help enterprises quickly select the distribution solution suitable for user needs. This section analyzes the advantages and disadvantages of logistics service recommendation models and parallel distributed algorithms and discusses the related technologies for business data analysis, including big data frameworks, key data storage systems, and Spark and Hadoop. It is found that Hadoop and Spark big data computing frameworks are good for analyzing massive amounts of data. However, the recommendation algorithm involves a large number of iterative operations, and the way the intermediate data are stored affects the performance of the parallel algorithm. Spark parallel processing algorithms achieve better parallel acceleration performance at large data set sizes, which is about 10 times more efficient than Hadoop. In addition, the recommendation algorithm is very sensitive to the basic configuration parameters of data nodes. Coping with different data sizes requires constant experimentation to determine the configuration parameters. This is a shortcoming of the current study and will be investigated in the future.


## 5. Conclusion

With the development of commercial business, the calculation and analysis of big data have become a popular application. In the application field of business information management, corresponding theoretical models and algorithms are urgently needed to deal with the increasing data information. Combined with actual logistics projects, this paper studies three aspects of big data storage, calculation, and analysis. To optimize the storage performance and data security of logistics big data, theoretical models and key technologies are studied. Based on data storage, effective theoretical models, strategies, and key algorithms are proposed for the logistics distribution calculation and shared transportation problems. To further improve the application value of the logistics data analysis algorithm, a distributed parallel algorithm is implemented based on the original algorithm, the performance of the algorithm is deeply analyzed, and the key issues such as parameters are studied and adjusted. The above research can not only be applied to the data centre construction of large and medium-sized logistics enterprises but can also realize low-cost storage of logistics trajectory big data information and video surveillance data and guide logistics distribution and transportation schedule.Firstly, based on the multilevel hybrid storage system with SSD as the cache layer, the optimization scheme to improve data access performance is studied. Then, from the perspective of data security, the multilevel hybrid storage model is studied, and a data security storage model suitable for a multilevel hybrid storage system is proposed. Finally, to optimize the distribution service in the logistics industry, based on the traditional matrix decomposition recommendation idea, a logistics distribution recommendation model is proposed. After optimization, it is incorporated into social networks and has a recommendation effect. The research results have achieved a good simulation recommendation effect. Based on this research achievement, the big data analysis research was carried out, the big data distributed computing platform based on the open-source software Spark was established, the typical recommendation algorithm was transplanted into the big data environment, and the key technical problems were solved. The parallel implementation scheme of the big data recommendation algorithm has certain universality and can be applied to various typical machine learning and data analysis algorithms. The business management system of this study can not only be used in logistics and transportation but can also provide a reference for other enterprises’ business information management.



---

*Source: 2761661-2022-11-17.xml*