Web Intelligence - Volume 18, issue 2

Authors: Hou, Zhixiang | Shen, Chong

Article Type: Editorial

DOI: 10.3233/WEB-200431

Citation: Web Intelligence, vol. 18, no. 2, pp. 97-99, 2020

Authors: Jia, Zhicheng

Article Type: Research Article

Abstract: In order to overcome the problem of poor recovery and stability of traditional big data fault-tolerant technology, a fault-tolerant technology of big data cluster in distributed flow processing system is proposed. Using the target protocol to build the cache data fault-tolerant mechanism of the distributed flow processing system and the disk data fault-tolerant mechanism, so as to build the internal data fault-tolerant mechanism of the system. By using spark application framework, a fault-tolerant model of big data cluster is built to realize the fault-tolerant of big data cluster in distributed flow processing system. The experimental results show that compared with …the traditional methods, the recovery rate of big data cluster fault-tolerant technology proposed in this paper is higher, the highest recovery efficiency is 99.83%, the stability is stronger, and it is more suitable for big data fault-tolerant processing of distributed flow processing system. Show more

Keywords: Distributed flow processing system, big data, cluster fault-tolerant technology, disk data fault-tolerant mechanism

DOI: 10.3233/WEB-200432

Citation: Web Intelligence, vol. 18, no. 2, pp. 101-110, 2020

Authors: Wang, Hao

Article Type: Research Article

Abstract: In order to overcome the shortcomings of the traditional method of integrating historical data of library e-book borrowing, such as high delay and low integration throughput, this paper proposes a method of integrating historical data of library e-book borrowing based on big data technology. Based on big data technology, this method builds an integrated platform for library e-book borrowing, which consists of data layer, business layer, interface layer and application layer. Through the data layer, a series of borrowing historical data generated by the borrowing integration platform are collected, and data integration module is used to clean and filter the …collected borrowing historical data of e-books. The data integration layer solves the local distortion and realizes the integrated processing of borrowed historical data. Experimental results show that the proposed method has the advantages of short time delay and low integration error rate, and the highest integration throughput can reach 97%. Show more

Keywords: Big data, e-book, borrowing, historical data, integration

DOI: 10.3233/WEB-200433

Citation: Web Intelligence, vol. 18, no. 2, pp. 111-120, 2020

Authors: Sun, Hui

Article Type: Research Article

Abstract: In order to overcome the problems of inaccurate recommendation results and high response delay existing in traditional recommendation methods, this paper proposes an interest reading recommendation method based on big data technology for intelligent library. Build the recommendation platform of intelligent library, design the cataloging subsystem, book category setting subsystem, new book storage subsystem and new journal storage subsystem. Through big data processing technology, users’ reading interest in each subsystem is clustered and fully mined. Based on the results of reading interest mining, the user interest model is established according to the semantic topic and the updating scheme of user …interest model is designed. Combined with the user’s score, we recommend the reading target which accords with the user’s reading interest to the user. The experimental results show that compared with the traditional recommendation method, the proposed method can achieve high-precision reading recommendation with low response delay, and the minimum delay is only 0.019. Show more

Keywords: Big data technology, intelligent library, interest reading recommendation, data mining

DOI: 10.3233/WEB-200434

Citation: Web Intelligence, vol. 18, no. 2, pp. 121-131, 2020

Authors: Xu, Dong

Article Type: Research Article

Abstract: In order to overcome the low efficiency of traditional data mining algorithms without considering weighted association rules, this paper proposes a distributed data flow mining algorithm based on matrix weighted association rules. According to the way of separating metadata and data flow, garbage data processing in data flow is realized. By using sliding window and data summary structure to optimize PCA algorithm, the main component decision matrix is formed in the window, and the dimension of data in sliding window is reduced by using the decision matrix. The matrix weighted association rules are used to mine the distributed data. After …dimensionality reduction, the transactions in the database are clustered according to the time distribution. The weighted analysis is carried out for each aggregation to obtain the weighted frequent item set with time and output the mining results. The experimental results show that the proposed algorithm has high efficiency and the highest accuracy of 98.9%. Show more

Keywords: Matrix weighted association rules, data flow, mining, data dimension reduction

DOI: 10.3233/WEB-200435

Citation: Web Intelligence, vol. 18, no. 2, pp. 133-143, 2020

Authors: Jiang, Xiao-Ling | Zhang, Hui

Article Type: Research Article

Abstract: Traditional methods have some problems such as large memory occupation and slow mining speed, so an intelligent mining acceleration algorithm based on particle swarm optimization is proposed. Based on the analysis of the communication data, the features of the communication data are selected by the acceleration strategy, and multiple feature subsets of the communication data are obtained repeatedly by using the remaining attributes. Particle swarm optimization (pso) algorithm is used to select the optimal feature subset, and average classification error is used as fitness function to complete intelligent mining of communication data. The experimental results show that the memory usage …of this algorithm is between 62 and 71 GB in the experimental process, which is small and the average running time is better than the traditional algorithm. The results show that the algorithm has lower memory consumption and faster mining speed. Show more

Keywords: Cloud computing, communication data, mining, particle swarm optimization

DOI: 10.3233/WEB-200436

Citation: Web Intelligence, vol. 18, no. 2, pp. 145-153, 2020

Authors: Liu, Ruijing | Luo, Xiaoting

Article Type: Research Article

Abstract: In order to overcome the existing abnormal big data intelligent detection method, the problem of low detection accuracy and poor convergence is not carried out without abnormal big data classification. A new Bayesian classification based heterogeneous network anomaly big data intelligent detection is proposed in this paper. method. Design an abnormal big data intelligent detection architecture, use TcpDump collection tool to collect and process heterogeneous network traffic data, and build the relationship between bottleneck traffic and abnormal big data based on the processed data, through Fourier transform The method obtains the data frequency information and uses the Bayesian network classification …method to realize the intelligent detection of abnormal big data in heterogeneous networks. The experimental results show that compared with the traditional method, the proposed method greatly improves the detection accuracy, convergence and anti-interference, and fully demonstrates that the proposed method has better detection effect. Show more

Keywords: Bayesian classification, heterogeneous network, abnormal big data, intelligent detection

DOI: 10.3233/WEB-200437

Citation: Web Intelligence, vol. 18, no. 2, pp. 155-165, 2020

Authors: Li, Ying

Article Type: Research Article

Abstract: In order to overcome the inaccuracy recognition results of the current network redundancy data, a new method based on attributes and relations for evaluating the redundancy of massive heterogeneous data in the Internet of Things is proposed. This method constructs a global database system and preliminarily obtains redundant data. Using angle variance, anomaly recognition and detection of Internet of Things data are carried out. A weighted network based on attribute distance is constructed, in which attributes and relations are used to calculate comprehensive redundancy, and heterogeneous data redundancy evaluation of the Internet of Things is realized. The experimental results show …that the redundancy calculation accuracy and recall rate of the results of this method are both above 95%, which is obviously higher than other methods. It proves that the proposed method has high accuracy and strong anomaly recognition performance, and the integrated database of the Internet of Things is security. Show more

Keywords: Internet of Things, heterogeneous data, redundancy, evaluation

DOI: 10.3233/WEB-200438

Citation: Web Intelligence, vol. 18, no. 2, pp. 167-177, 2020