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Considering the unique users of scholars' social networks, this paper proposed a collaborative association model of paper and project data based on scholars' social networks. Firstly, the proposed model used the two-step feature selection method to preprocess the data, removed the irrelevant and redundant features and obtained the effective features that affected the association between the paper and the project. Then it would adopt TVSM to calculate the text similarity between the paper and the project, then formed recommendation sets for different papers/projects. Through the social network (SCHOLAT) data for researchers, the model was implemented and applied to SCHOLAT. The online application situation and user feedback show that the model has good accuracy and practicability. Furthermore, it can more fully explore the potential relationship between the paper and the project, provide users with better academic research management services, and propose a novel research method for analyzing the academic big data. [ABSTRACT FROM AUTHOR]

Due to the lack of tailored security and privacy technology in many fields such as Internet of Things, artificial intelligence and big data, the system based on distributed ledger technology (DLT) can well meet their the security requirements as a decentralized data management method. Consequently, systems based on distributed ledger technology emerge in an endless stream. In order to help subsequent researchers understand the field of distributed ledger technology more comprehensively and quickly, this paper reviewed the research results based on distributed ledger technology in recent years. It analyzed the blockchain in depth from the perspectives of data structure, technical principle, consensus mechanism and performance, and compared and analyzed some recent advances in distributed ledger technology in detail based on the current research status, such as Hashgraph, Holochain and Truechain. Finally, this paper summarized the future development prospect of this field. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of big data and the increase of encryption scenarios, it 's difficult to meet the requirements of encryption performance with only software-run encryption methods, and FPGA/ ASIC encryption systems implemented using Verilog/VHDL methods have poor flexibility and difficult maintenance and upgrades problem. In response to the above problems, this paper designed and implemented an AES algorithm encryption system based on heterogeneous reconfigurable computing, including three mainstream modes of AES algorithm ECB, CBC, CTR, each mode realized 128 bit, 192 bit, 256 bit encryption of three key sizes. This paper realized the hardware acceleration of the module based on FPGA, and realized the dynamic switching of different modes and different bit width encryption modules based on the hardware reconfigurable mechanism. By implementing and verifying the system on Intel Stratix 10, the experimental results show that the throughput rates of AES-ECB, AES-CTR and AES-CBC in the system are 116.43 Gbps,60. 34 Gbps, and 4. 32 Gbps, respectively. The ECB mode compared with Intel Xeon E5-2650 V2 CPU and NVIDIA GeForce GTX 1080 GPU achieves 23.18 times and 1.43 times acceleration ratios respectively. Compared with the method just calling the CPU,the overall system has a computational acceleration ratio of 4.72. [ABSTRACT FROM AUTHOR]

Collaboration recommendation is helpful to the development of scientific research and the transformation of technological achievements. However, the gap between scholars ' academic levels seriously affects the establishment of cooperative relations. This paper made recommendations from three perspectives :the academic level gap between scholars, the topological distance in the collaborative network, and research interests. Firstly, this paper defined three networks, namely scholar-scholar network, scholar-topic network and scholar-level label network, and merged them into a graph of topic-scholar-level label, and set weight to the edges in the graph. Then it turned collaboration recommendation task into a link prediction task. Finally, it employed the biased restart random walk algorithm to calculate the probability of visits among scholars, and recommended the candidate scholars with high visit probability to target scholars. Experiments on three datasets show that the proposed model can improve the precision rate, recall rate and F1 index by 5.4%,2.7% and 3.8% . In addition, the academic levels of target scholars and recommended scholars are more closely matched . [ABSTRACT FROM AUTHOR]

Aiming at the problems of parallel support vector machine (SVM) algorithm in big data environment such as redundant data sensitivity, difficulty in parameter selection, and low parallelization efficiency, this paper proposed a parallel SVM algorithm using Relief and bacterial foraging optimization (BFO) algorithm based on MapReduce (RBFO-PSVM). Firstly, the algorithm designed a feature weight calculation strategy (MI-Relief), which used mutual information to improve the weight calculation function of Relief algorithm to eliminate redundant features in the data set and effectively reduce redundant data to support parallelism. Secondly, this paper proposed a hybrid BFO algorithm based on MapReduce (MR-HBFO), which selected the optimal parameters of SVM in parallel, and solved the problem of difficult selection of SVM parameters. Finally, it proposed the kernel clustering strategy (KCS) to reduce the size of the data set involved in parallel training, and proposed a cross-fusion cascaded parallel SVM (CFCPSVM) to improve the cascade SVM (CSVM) feedback mechanism. It trained SVM by combining with the Map Reduce programming framework, and this improved the parallelization efficiency of parallel SVM. Experiments show that the RBFO-PSVM algorithm has a better classification effect on large data sets and is more suitable for large data environments. [ABSTRACT FROM AUTHOR]

In order to solve the problem of extracting temporal, spatial and channel features, this paper proposed an ADGCN for skeleton action recognition. At first, it modeled the skeleton data and fed the movement of joints, bones and joints and bones information into the multi-stream framework of a single stream. And then it transferred the input data to the directed graph convolution network to extract the dependencies between joints and bones, after that, used the space-temporal channel attention network（STCN） to strengthen the key joints in each layer of network temporal, space and channels of information. The last, it calculated the accuracy of action recognition by the weighted average of the information of four streams, output action prediction results. This model trained and verified in two large data sets NTU-RGB+D and Kinectics-Skeleton. Compared with baseline DGNN（directed graph neural network）, the accuracy of this paper on two cross subsets CS and CV is improved by 2.43% and 1.2%, on NTU-RGB+D dataset. On Kinectics-skeleton dataset, the accuracy of top1 and top5 is increased by 0.7% and 0.9%, respectively. The proposed ADGCN can effectively enhance the performance of skeleton action recognition, and the effect is improved on both large data sets. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of indigent scalability, poor parameter optimization ability and low efficiency of parallelization in big data density-based clustering algorithm, this paper developed a density-based clustering algorithm MR-GDBIGS based on groups and improved gravitational search. Firstly, it proposed a GSP to divide data and speed up neighborhood search. Secondly, the algorithm designed an IGSA which used the PUF. In addition, it could dynamically select the optimal parameters of local clustering based on IGSA algorithm, which could improve the clustering effect of local clustering. Finally, based on cover-tree and MapReduce, this paper developed a MR-CTMC to get the result of clustering algorithm more quickly, which improved the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MRGDBIGS algorithm has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

In the big data environment, the Can-tree based on incremental association rule algorithm has problems such as too much space occupation of the tree structure, the efficiency of frequent pattern mining is poor, and the parallelization performance of MapReduce cluster is insufficient. Aiming at these problems, this paper proposed the MR-PARIRM. Firstly, it designed a RS-SIM to merge similar items in the dataset, and constructed Can-tree based on the merged data, thereby reducing the space occupation of the tree structure. Secondly, this paper proposed an MPS to prune and merge the propagation paths in the tree structure, thereby compressing the frequent pattern search space to speed up frequent item mining. Finally, MRP ARIRM used the DSS to dynamically schedule the computing tasks in the heterogeneous MapReduce cluster, thereby implementing the load balance and effectively improving the parallel computing capabilities of the cluster. The final experimental simulation results show that MR-PARIRM has relatively better performance in the big data environment and is suitable for parallel processing of large-scale data. [ABSTRACT FROM AUTHOR]

With the development of big data, computational power and deep learning, the object detection algorithm based on deep learning becomes the mainstream algorithm in this field, and its performance is far superior to the traditional algorithms. In order to review the development of algorithms, track the latest research results, and promote the research in the field of object detection, this paper summarized the representative object detection algorithms from two dimensions: two-stage /one-stage and anchor-based anchor-free, and realized the rectangular box or approximate rectangular box annotation under these two different classification standards. This paper discussed the process, characteristic, evolution and performance of the algorithms, and made the classification and analysis on the important improved versions. Finally, this paper summarized the advantages and disadvantages, limitations and applicable scenarios the algorithms, and looked forward to the development trend in the field of object detection, and put forward several important research directions in the future. [ABSTRACT FROM AUTHOR]

Aiming at the problems of sensitivity of initial center, high communication overhead of nodes and low efficiency of cluster in big data clustering algorithm based on partitioning, this paper proposed a partitioning-based clustering algorithm using grid density and locality sensitive hash function based on MapReduce, named PBGDLSH-MR. Firstly, based on the initial data set, it proposed the GDS(grid density strategy) to get the initial clustering center, which avoided the sensitivity of initial center caused by random selection of initial cluster center. Secondly, it proposed the DP-LSH ( data partitioning based on locality sensitive hash functions) to map more closely related data objects into the same subdataset and get data partitions on the map. Meanwhile, it designed a formula SI( similarity improvement) to evaluate the data partitioning results, reduced the communication overhead between nodes. In addition, this paper designed an AGS (adaptive grouping strategy) to handle data skew in data partitions, which improved the cluster efficiency. Finally, based on MapReduce, it mined the cluster centers in parallel to gene rate the final clustering results. The experimental results show that the PBGDLSH-MR has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

Among all the clustering problems, the K-means problem is probably the most well-known one. How to obtain a theoretically guaranteed solution of K-means efficiently for the big data can be a key problem. This paper surveyed the progress of this problem. Firstly, this paper defined the K-means problem and introduced relevant backgrounds. Secondly, it introduced separately and described in details the techniques for theoretical guarantee and speed up. Finally, it summarized the main results and forecasted the future directions of K-means algorithms on big data. [ABSTRACT FROM AUTHOR]

In the big data environment, the random forest algorithm suffers from excessive redundancy and irrelevant features, the insufficient spatial information content of feature subspace, and low parallelization efficiency. To resolve these issues, this paper presented PRFGRSAE. Firstly, this algorithm proposed a DRNGRSAE, which filtered redundant and irrelevant features of the feature set and extracted features by stacked auto-encoders to reduce the number of redundant and irrelevant features effectively. Secondly, it proposed a SSLF that combined Latin hypercube sampling and normalized correlation degree, which formed feature subspaces with high spatial expression by performing multi-layer division sampling on the feature set, and ensured the feature subspace information content. Finally, it proposed a reducer allocation strategy DSVLA combining with variable action learning automata, which allocated each cluster to reducers for processing evenly and improved the parallelization efficiency effectively. Experimental results show that compared with IMRF, KSMRF, and GAPRF algorithms, the speedup ratio and accuracy of the PRFGRSAE algorithm are significantly improved. Therefore, the algorithm can obtain higher accuracy and parallel efficiency when applied to process large data, especially for data sets with more features. [ABSTRACT FROM AUTHOR]

With the rise of cloud computing, big data and the Internet of Things, network traffic grows at an explosive rate, while the growth rate of network bandwidth cannot match it. In the foreseeable future, the contradiction between increasing network traffic and slow growing network bandwidth will exist for a long time. How to change the current extensive network management means, achieve refined network management, and solve the above problems is the question need to face. This paper presented a state-based view model, described the formal concept of the global state view, and also expanded the extension of the concept of global state view, proposed the concept of state view stack, and expanded the state view of each layer of the defined network. It used the hierarchical Petri net to model and analyze its features, and introduced the global state view construction algorithm in detail. It analyzed the port collision application and constructed the state view of the application. At the same time, this paper took load balancing function plus global state view application as an example to illustrate state-based network management could improve resource utilization. [ABSTRACT FROM AUTHOR]

Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

In differential privacy histogram publication the privacy budget involves the intensity of noise adding, which has a direct impact on the availability of data publication. Therefore, how to allocate and optimize the privacy budget is a challenge to the histogram publishing algorithm. This paper proposed a histogram publishing algorithm based on adaptive privacy budget allocation strategy, called APB. In order to balance the noise error and reconstruction errorefficiently and get the relations hip between the privacy budget allocation weight and the group size and number of group, APB established an optimization model of privacy budget allocation weight by analyzing the noise introduced before and after the grouping step. In order to boost the accuracy of the released histogram, this paper employed optimization model and greedy grouping to propose an adaptive privacy budget allocation strategy. Experimental results show that the proposed method of histogram publication based on adaptive privacy budget allocation strategy is more available than similar algorithms. [ABSTRACT FROM AUTHOR]

This paper studied the problem of secure subset which most of the existing protocols that solve such problems can only keep the elements of one set private to. Under the semi-honest model, this paper constructed a secure subset protocol by using Bloom filter and Goldwasser-Micah homomorphic encryption algorithm, and proved the security of the protocol by using common simulation examples in secure multi-party computing. It used the Bloom filter to map a data set with a large number or large number field elements into a smaller data set, improved the efficiency and range of the protocol, at the same time, it used the Goldwasser-Micali homomorphic encryption algorithm to ensure the security of the protocol. Most of the relevant researches were based on the difficult problems such that secondary residuals, it is impossible to resist quantum attacks, and the secure subset computation which can resist quantum attacks is a further research direction. [ABSTRACT FROM AUTHOR]

In the large data environment, there are many problems in the parallel deep convolution neural network（DCNN） algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2 col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2 col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2 col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets. [ABSTRACT FROM AUTHOR]

In order to solve the problems such as sparse data, cold start and lack of diversity of recommendation results in traditional collaborative filtering recommendation algorithms, this paper proposed a collaborative filtering recommendation algorithm that integrated social networking with key users . Based on the score matrix of user projects, the proposed algorithm integrated user social networks to de rive social trust matrix, integrated key user information to obtain key user scoring matrix. And then it used these three matrix data distributions to predict user's target project with different weights score. At the same time, aiming at the massive data problem, this paper used the Spark distributed cluster to realize the parallelization of the algorithm. The experimental results show that the proposed algorithm can effectively alleviate the data sparse problem and improve the data processing speed and recommendation accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, with the rapid development of Internet of things and big data, micro-embedded systems had been widely used in takeaway and sharing industries such as shared bikes, transportation, express delivery and smart home. Existing fault diagnosis research mainly focuses on traditional embedded systems. Therefore, relatively few fault diagnosis documents for micro-embedded systems. To solve the above problems and combine the characteristics of the system (limited computing capacity and storage space) , this paper proposed a class of fault diagnosis method classification methods suitable for miniature embedded systems, divided into two categories, namely local fault diagnosis methods and remote fault diagnosis method. Then this paper described the core idea of the fault diagnosis method for micro-embedded systems, summarized the advantages and disadvantages of each method used in micro-embedded systems, and the problems that should be noted. Finally, this paper discussed the problems that need to be solved in the fault diagnosis of miniature embedded systems. [ABSTRACT FROM AUTHOR]

In order to solve the low efficiency and practical application of predicting the potential phone replacement user, this paper designed and built a phone replacement prediction system based on big data platform. This system firstly captured signaling data from multiple network interface and collected external data. Through the parse platform, the data from network interface would be distributed, decoded, synthesized and correlated, and the external data would be processed by ETL tools, and then storing processed data into IIDFS. Further, this paper established a phone replacement prediction model, which based on logistic regression, using spark components in the big data platform and output the potential phone replacement users. Finally, this paper chose part of the western city ’ s user data for system testing. The result shows that the prediction accuracy of the phone replacement prediction system is up to 71%. It can preferably recognize potential phone replacement users, and provide reliable support for the precise marketing of operators and mobile phone manufacturers. [ABSTRACT FROM AUTHOR]

With the advancement of medical technology and big data era, how to protect the privacy of sensitive information in patients ' medical records has become a current research focus issue. In order to protect the privacy of medical big data in the publishing process, this paper proposed an AUR-Tree differential privacy data publishing algorithm. The algorithm used the attribute utility value ranking method to measure the influence degree of the quasi-identity attributes on the sensitive attributes, which was as the measurement basis for iterative segmentation. Then it ad opted top-down iterative segmentation classification tree technology based on generalization, and reasonably allocated the privacy budget through the class arithmetic method to realize the privacy protection in the medical data release process. The experimental results show that the algorithm retains the value of subsequent data mining and greatly improves the security, effectiveness and usability of the data. [ABSTRACT FROM AUTHOR]

With the rapid development of highway,the emergence of increased congestion,monitoring difficulty and other issues are increasingly seriously. Firstly based on the urban traffic performance congestion index of Beijing,this paper proposed a method to evaluate the status of highways through utilizing traffic performance index. Then based on the big data platform of Storm,this paper implemented data process,data analysis and key algorithms of index background calculation. Finally it published the real-time traffic performance index in the form of mobile phone APPs and official accounts of Wechat. The experimental results show that highway traffic performance index not only can reflect the overall status of highways,but also can reflect the real-time congestion situation of specific highways or specific highways range,and can real-time monitor the congestion situation of highway road network,which provides a reference for people and plays a positive role to ease road congestion. [ABSTRACT FROM AUTHOR]

Aiming at solving problems like the difficulties in building and maintaining of the traditional spatio-temporal index，and the inefficiency of real-time query，this paper first proposed a new building method of spatio-temporal index based on HBase. By using HBase as the spatio-temporal features index structure of monitoring video big data，using Z-filling curve to fulfill the dimension reduction and storage of spatio-temporal features，and using the associated relations and the dependency rule between tim e，space and attributive characters to arrange the rowkey index key，these disadvantages like the difficulties of traditional spatio-temporal index's building and maintaining would be overcome efficiently. In addition，aiming at solving the traditional space-time index low query efficiency issues in real time，this paper further proposed spatio-temporal association query algorithm based on the 1 curve，which could calculate 1 value ranges and establish space to subset partion，and then the candidate data sets would be obtained by using the spatio-temporal features got from the first step to have indexed searching， and next the related query would be realized by pegging the HBase index table. Experimental results and analysis show that， compared with the conventional R-tree indexing algorithm， the spatio-temporal index building method based on HBase proposed above has higher insertion index efficiency and the association Z curve space-time query-based algorithm proposed can quickly and efficiently deal with spatio-temporal correlated queries. [ABSTRACT FROM AUTHOR]

The shuffling algorithm based on folding technique has ideal data scrambling effect and can meet the prerequisite of big data sampling. In order to prove that the internal rules of the scrambled data set are not destroyed after sampling, this paper analyzed the association rules of the data before and after sampling by data mining, and compared the support and confidence of the association rules as well as the frequency of the transactions, and found that the association rules of the data after the collapsed shuffle algorithm were stable before and after sampling. And by comparing with the time efficiency of the existing algorithm and the overall sampling error, it is further concluded that big data sampling is effective. Which means that the overall situation of the data can be inferred from the sampled sample. [ABSTRACT FROM AUTHOR]

Structural variation detection is an important task of genome big data analysis. Inversion is a common structural variation and is often associated with a variety of serious diseases. Therefore, obtaining the accurate position of inversion is an important task. This paper proposed a method for detecting inversions variation based on long reads and realignment. This method firstly extracted the clipped sequence of each long read, and aligned them against the genome reference sequence . Based on the original alignment information and the realignment information, this method determined the inversion signals. Finally, it clustered the inversion signals to determine the location of the inversions. The performance of this method and the other two common inversion variation detection methods were compared on two datasets. The experimental results show that this method can obtain more satisfactory detection results. [ABSTRACT FROM AUTHOR]

As the era of big data coming, it has been a trend that distributed file systems supports Hadoop to access big data. 'Ibis paper designed and implemented a p.NFS framework for accessing big data with Hadoop. The framework added a p.NFS shim layer module between Hadoop and pNFS, and then implemented the HDKS APIs. In addition, it added write cache and the node data layout aware mechanism to optimize the performance of the system. 'Ibis paper used Hadoop benchmarks to test the designed pNFS framework. The results show that the framework increases the performance of write by 45% , and the performance of read by at least of 97%. So the proposed framework can support accessing Hadoop big data effectively. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To find out the present situation, problems and future development of volunteered geographic information (VGI), this paper studied it carefully. Firstly, this paper introduced the concept of volunteered geographic information and the research background. Then, through the current related articles, this paper summarized and analyzed the research status and the urgent problems of volunteered geographic information. In addition, according to the focuses of VGI, this paper divided current researches into three aspects, which were data acquisition of VGI, data processing and data applications. Besides, this paper still grouping explored the research status, achievements and the insufficient of VGI. Finally, combined with the big data research, this paper prospected the future research directions of volunteered geographic information. The result of the research shows that VGI has become an important branch of GIS, but the theory and methods of VGI data processing and application are still needed to be improved. [ABSTRACT FROM AUTHOR]

In the era of big data, the data sources are complex, the data qualities are problematic, and some data is not accurate, missing or involves error. Incorrect data has seriously affected the quality of data mining, resulting in a significant impact on decision making. There are many repair methods about the missing data. In them conditional function dependency is an effective one and many research findings on how to find conditional function dependencies have been found. This paper presented a method of constructing conditional function dependencies based on association rules. The association rules were gotten by data mining and they had some hidden features and could not be found by usual ways. Thus the conditional function dependency based on association rules has a certain value of application. The paper gave a construction method and experiments prove its effectiveness. [ABSTRACT FROM AUTHOR]

Aiming at the problems of unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization in big data density-based clustering algorithm, this paper proposed a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce, named MR-DBIFOA. Firstly, based on KD-Tree, it designed a division strategy to divide the cell of grid adaptively. Secondly, this method proposed an improve fruit fly optimization algorithm which used KLSS and CFF. Then,based on IFOA algorithm, it dynamically selected the optimal parameters of local clustering, which could improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, it proposed a density-based clustering algorithm using IFOA to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce,it proposed a clusters merging algorithm (MR-QRMEC) to get the result of clustering algorithm more quickly, which improved the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

Aiming at the problems of too many redundant parameters, slow convergence speed and low parallel efficiency of parallel DCNN algorithm in big data environment, this paper proposed a parallel deep convolutional neural network optimization algorithm named PDCNNO. Firstly, the algorithm designed PFM strategy, pretraining network, and obtained the compressed net work, which effectively reduced redundant parameters and reduced the time and space complexity of DCNN training. Secondly, it designed a CGMSE to obtain local classification results, which realized rapid convergence of conjugate gradient method and improved the convergence speed of the network. Finally, in the reduce phase, it proposed a LBRLA strategy to obtain the global classification results, which realized the fast and uniform grouping of data and improved the acceleration ratio of the parallel system, Experiments show that the algorithm not only reduces the time and space complexity of DCNN training in the big data environment, but also improves the parallelization performance of the parallel system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of excessive time,space complexity and unbalanced load for each node based on the parallel frequent itemset mining algorithm MRPrePost,this paper proposed an optimization parallel frequent itemset mining algorithm based on MapReduce,named PFIMD.Firstly,this algorithm adopted a data structure called DiffNodeset,which effectively avoided the defect that the N-list cardinality got very large in the MRPrePost algorithm.Secondly,in order to reduce the time complexity of this algorithm,it designed the T-wcs to avoid the invalid calculation in the procession of two DiffNodesets connection.Finally,considering the impact of cluster load on the efficiency of parallel algorithm,it proposed the LBSBDG,which decreased the size of PPC-Tree on each computing node and reduced the amount of time required to traverse the PPC-Tree by evenly grouping each item in the F-list.The experimental results show that the modified algorithm has better performance on mining frequent itemset in a big data environment. [ABSTRACT FROM AUTHOR]

The number of low-frequency sampling traffic trajectory data is growing explosively in big data era. Map matching techniques play an essential role in taxi pick-up hot spot and route recommendation, which can help accurately and efficiently matching low-frequency floating car data from complex road networks. In view of the aforementioned discussion, this paper proposed an improved algorithm based on curve fitting. This algorithm filled missing trajectory data and road network data by interpolation and mean methods, employed Geohash technology to store and search road network data and trajectory data, took into full consideration the factors of the speed of vehicles and road speed limit, used the backward vector of trajectory point and road segment vector to analyze the candidate road segments, and designed a comprehensive evaluation function to obtain the optimal matching results. The experimental results show that the proposed curve-fitting algorithm has a higher matching accuracy and faster time efficiency when compared with the traditional vertical projection and curve -fitting algorithms. [ABSTRACT FROM AUTHOR]

Today's society is in the era of big data. There are more and more network data in reality, which has complex structure and large scale. Analyzing their structure effectively plays an important role for understanding and applying their provided information. The network structure discovery algorithm based on hybrid model can mine the multi type clustering structure in the network, but it can not deal with large-scale network effectively. Based on Graph X graph computing model, this paper proposed a large scale network structure exploring algorithm LNSES (large scale network structure exploring algorithm on Spark ) to improve the efficiency of the algorithm from two aspects of storage space and running time . In order to decreasing the large memory consumption for the network adjacency matrix, the LNSES algorithm stored attribute values of edge, node and node in a distributed manner. Edge partitions recorded edges, which were used as index for parameter transferring between nodes. For improving the efficiency of the structure exploring algorithm, a zipper ope ration between edge partition and node partition generated an index structure. At the stage of updating parameter, nodes found corresponding edges based on the index structure, and updated parameters in parallel. Experiments on large-scale real and artificial networks illustrate that LNSES is better than the similar network structure exploring algorithms in terms of running time and accuracy, and can mine and analyze the structure of large-scale network. [ABSTRACT FROM AUTHOR]

Current clustering methods study data clustering problems only from one angle, it is insufficient to considerate clustering chaotic big data of Internet of Things based on cloud pattern with low clustering quality. To achieve agile, intelligent and stable clustering on big data of Internet of Things, with studying general cloud pattern description models on events of Internet of Things, general cloud pattern analysis models on chaotic correlation features of events of Internet of Things, extracting algorithms on chaotic correlation features of events of Internet of Things based on cloud pattern, correlation mining of big data of Internet of Things based on cloud pattern chaotic correlation features, improved decompositing singular value algorithms, grid coupling clustering algorithms, K-means algorithms, decision tree learning methods, methods of analysis principal components, stratification merging methods and distribution probability function, this paper designed an agile, intelligent and stable clustering algorithm on big data of Internet of Things based on chaotic correlation features of events. Finally, it carried out validating experiments, and compared performance of this proposed algorithm with traditional algorithms. Experimental results show this algorithm has shorter clustering time, less error and higher agility, has better intelligence, dynamic evolution, stability than those of traditional algorithms. Therefore, this proposed algorithm achieves effective clustering on big data of events of Internet of Things with chaotic correlation features based on cloud patterns, has higher utility. [ABSTRACT FROM AUTHOR]

In order to quickly and accurately identify real attack events in massive, multi-source and heterogeneous network threat intrusion logs, and discover attackers, and construct their characteristic portraits, this paper proposed a method to construct attacker portraits based on BDSAL. It defined a paradigm model of security events according to CAPEC, and rapidly normalized the multi-source heterogeneous log into a paradigm-based security event combined with the implementation of large data flow message queue. By extracting and expanding the features of security events, it generated event feature maps. According to the characteristics of space-time and attack patterns, it used the community discovery algorithm to cluster the feature maps and find the attackers. Finally, it verifies the feasibility and validity of the proposed method by the real attack and defense data of the laboratory. [ABSTRACT FROM AUTHOR]

As a new platform in big data stream computing, Apache Heron ignores the difference in communication modes between task instances and the unbalance of processing load among nodes, which leads to the decline system performance. To address the problem, this paper designed the model of node resource limitation, the model of communication overhead optimization and the model of data stream relationships among instances, as the foundation to propose the TUR-Heron. The strategy was composed of the node resource limitation algorithm and the instance reallocation algorithm. By judging the criteria for instance reallocation and executing instance reallocation algorithm, this strategy transformed the inter-node data streams into intra-node data streams and minimized the communication overhead of the system. The experimental results show that under the three sets of benchmarks, TUR-Heron reduces the communication overhead between nodes and the response latency of the system compared with the default scheduling strategy, and improves the balance of resource utilization of computing nodes. [ABSTRACT FROM AUTHOR]

Aiming at the problem of extensibility of big data analysis on massively parallel distributed systems and software platforms, this paper proposed a big data mining technique based on parameter-free CLUBS algorithm around centroids. The technique worked in a completely unsupervised manner, splitting clusters based on minimum quadratic distance criteria to separate data from noise, identified blocks containing only outliers by intermediate refinement and generated complete clusters for the remaining blocks, it designed a parallelized version of CLUBS to enable fast and efficient clustering of big data. Experiments show that the CLUBS parallel algorithm is not affected by data dimension and noise, and is better than the existing algorithms in terms of scalability and execution time. [ABSTRACT FROM AUTHOR]

In today' s big data era, big data processing frameworks such as MapReduce often appear slow and inefficient when processing data, specially related to graphs. Therefore, it is necessary to explore an efficient algorithm to handle this type of clique counting problem. Since the predecessor literatures have thoroughly explored the 3-clique counting, the extended version of the problem( the 4-clique counting problem) improves its position gradually. Under the guidance of a heuristic idea, this paper proposed a probability sampling algorithm based on neighboring edge sampling to solve the extended problem. With the usage of Chernoff inequality, the algorithm only needed a certain number of samplers as the performance guarantee of relative error under the approximate condition. Later, the experimental evaluation and comparison shows that the probability sampling algorithm loses a small amount of precision compared with the traditional precision algorithm, but it has great advantages in algorithm running time and space occupation. Finally, it comes to the conclusion that it has great practical value in practical applications. [ABSTRACT FROM AUTHOR]

Under the background of big data, K-means has local optimum solution and slow clustering speed caused by choosing centroid. The paper developed a new clustering optimization and parallel algorithm CK-means based on K-means. For algorithm optimization, Canopy algorithm could determine the number of clusters k and select the initial centroid. For parallelization acceleration, F1ink platform could help to design a parallel acceleration strategy for CK-means. It also could analyze the impact of different parallelism degrees on computing time-consuming. Experiments show that CK-means algorithm has higher accuracy and better performance. The performance ratio in the iris data set is increased by 44. 79%, and the performance ratio in the wine dataset is improved by 32. 03%. The clustering time of CK-means algorithm under different parallelism degrees decreases first and then rises, and the minimum clustering time is related to the size of the data set. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low computational efficiency and low clustering performance of clustering algorithms for big data, this paper proposed a clustering algorithm of big data based on the improved ABC algorithm and MapReduce . This algorithm combined the grey wolf optimizer algorithm and ABC algorithm, and improved the exploration and exploitation of the ABC algorithm simultaneously, it could help to improve the clustering performance effectively. The algorithm utilized the chaotic map and backward learning as the initial strategy of ABC colony to improve the solution quality of search procedure. It realized the clustering algorithm based on MapReduce programming model, and realized the clustering process for big data by minimizing the quadratic sum of inner class distances . Experimental results demonstrate that the proposed algorithm improves the clustering quality of big data, and speedups the clustering procedure. [ABSTRACT FROM AUTHOR]

In order to solve the weakness of existing medial image encryption algorithm based on chaotic system in considering the image' s texture feature, this paper proposed a medical image encryption and decryption method based on chaotic mapping and adaptive partitioning. Firstly, it used the 2D Sine Logistic chaotic system to generate two secure sequences with good chaotic characteristics . Then, it divided the original medical image into fixed size image blocks, and calculated their maximum pixel difference and variance . According to the given threshold, it divided these image blocks into texture smooth blocks and texture complex blocks. Finally, it used the first chaotic sequence with ciphertext feedback mechanism to encrypt the smooth blocks, and encrypt the complex blocks by using the second chaotic sequence with plaintext feedback mechanism. Therefore, it obtained the encrypted image. Because the presented algorithm efficiently considered the texture characteristics of image blocks and optimized the algorithm of chaotic encryption, it improved the encryption and decryption efficiency of medical images. Experimental simulation results show that the presented algorithm has high security and encryption efficiency. Compared with the existent method, it reduces the encryption and decryption time by about 0. 5 times . The presented algorithm is suitable for real-time medical images encryption and decryption with big data size. [ABSTRACT FROM AUTHOR]

Because any MapReduce job requires a series of complex operations such as task scheduling and resource allocation independently, there are a lot of redundant disk I/O and resource duplicate application operations among multiple MapReduce jobs coordinated by the same algorithm, causing inefficient resource utilization in job computing process. Big data mining algorithms are usually divided into several MapReduce Jobs, taking ItemBased algorithm as an example, this paper analyzed the resource efficiency of mining algorithm with multi-MapReduce job collaboration scenario. It proposed an ItemBased algorithm based on Distributed Cache, which used Distributed Cache to cache I/O data between multiple MapReduce Jobs, broke the defect of independence between jobs, and reduced the waiting delay between Map and Reduce tasks. The experimental results show that, DistributedCache can improve the data reading speed of MapReduce jobs. The algorithm reconstructed by Distributed Cache greatly reduces the waiting delay between Map and Reduce tasks, and improves the resource efficiency by more than three times. [ABSTRACT FROM AUTHOR]

In the big data environment, the problem of high time complexity of multi-label K-nearest neighbor( ML-KNN ) is particularly prominent. In addition, ML-KNN does not consider the effect of K nearest neighbors on the final classification results. This paper first clustered the training set, and then found a nearest cluster as new training set for test dataset. Then it calculated nearest neighbor distance weight, by which the effect of k neighbors was described. Finally, an unseen sample could be classified by this new method. Numerical simulation results in different datasets show that the proposed algorithm obtained a better classification result and improved the time complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Most recommendation systems based on the network structure suffer from lack of diversity, so that the paper proposed a recommendation system of big data based on multi-weight projection of bipartite network. Firstly, it abstracted the basic information of datasets, and applied items-users lists as an input to Levenshtein distance algorithm to compute similarity of each property. Then, it computed the number of common neighbors of the nodes in the bipartite network, the degree of common neighbors of the nodes in the bipartite network and degree of each node in bipartite network, and also computed the triple weights of each side of the bipartite network. Lastly, it adopted the enhanced bipartite projection technique to abstract the potential links of the bipartite network to realize the link prediction based on similarity. It realized the experiments based on both of big dataset and small dataset. The results show that the proposed algorithm outperforms different kinds of recommendation systems in terms of accuracy and coverage of recommendation, at the same time, it outperforms the other recommendation system based on network structure. [ABSTRACT FROM AUTHOR]

In order to further improve the speed of the association rules mining frequent sets and optimize the execution performance of the algorithm, this paper proposed an association rule mining algorithm based on improved memory structure. Based on the Spark distributed framework, the proposed algorithm mined frequent sets in parallel. It used the Bloom filter to store the project in the mining process, and simplified the operation of the transaction set and the candidate set, so as to optimize the speed of mining frequent sets and save the computing resources. Compared with the YAFIM and the MR-Apriori algorithm, the proposed algorithm has a significant improvement in the efficiency of mining frequent sets under the condition of occupying less memory. The algorithm can not only improve the mining speed and reduce the memory pressure, but also has good scalability, so that the algorithm can be applied to larger data sets and clusters to optimize the performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem of defects in the accuracy of recommendation and data sparse processing for recommendations made by explicit feedback information. This paper introduced implicit feedback information, designed and implemented a multi-dimensional recommendation algorithm( iMCF) based on implicit feedback. The algorithm included three dimensional information of user, project and implicit feedback. For the first two dimensional information, it used the similarity of cloud model to model. For the information of implicit feedback, it mainly used the probabilistic matrix decomposition model to deal with. Afterwards, it balanced the prediction scores obtained from the three dimensions according to the weights, and obtained the final prediction score and made recommendation. The experimental data shows that the algorithm's performance in terms of recall rate and accuracy is significantly improved compared to other algorithms, and it is suitable for big data environments. [ABSTRACT FROM AUTHOR]