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Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

In the decentralized blockchain, each node stores the same data copy that costs a lot of repeated storage space to store valuable information. It makes storage efficiency insufficient. Meanwhile, each node needs to exchange the huge messages to create and verify the block. With rapid increasing in the number of communication nodes and communication frequency, it has caused a large network load. In order to further improve the storage efficiency and decrease the network load, a hierarchical proxy model is proposed. Firstly, designing a hierarchical proxy model which accurately divides normal nodes and proxy node groups, and it performs directional division of tasks for different nodes. Secondly, in a small-scale management cluster composed of proxy nodes, cluster failure priority classification can effectively deal with the failure of proxy nodes. Through the hierarchical storage mode, the block header and body data are separated, and it uses global or local method to store. The hierarchical operation mode makes use of idle resources to make transaction verification and block creation run in parallel. On this basis, the proxy model adopts delay correction strategy and concentrated multiple correction strategy to further reduce the communication loss. Through malicious node blacklist, the node with malicious behavior is added to the blacklist, and it prevents the node from further making evil. Finally, the simulation experiments show that the hierarchical proxy model effectively resists malicious behaviors, reduces network load and has great advantages in terms of data storage efficiency.

In order to foster a modern economic system and facilitate high-quality economic development, it is crucial to establish a conducive business environment. Undoubtedly, the evaluation of the business environment for enterprises constitutes a prominent area of research. Nevertheless, ensuring the authenticity and security of the raw data sources provided by participating enterprises poses a challenge, thereby compromising the accuracy of the evaluation. To tackle this issue, an enterprise composite blockchain construction method for business environment is proposed in this paper, which stores the raw data of enterprises by the means of hybrid on-chain and off-chain. Initially, the enhanced hash function SHA256 is introduced to encrypt the raw data of enterprises. The encrypted data is subsequently stored in an off-chain Level DB database, which is based on non-volatile memory. This approach effectively alleviates the burden on communication and storage. Secondly, a composite storage strategy on-chain is adopted: the key values from the Level DB are stored in the DAG-based Conflux public blockchain, while the enterprise state data is stored in the consortium blockchain, so as to provide trusted evidence of business environment evaluation data. Finally, it is demonstrated through a large number of experimental comparisons that the enterprise composite blockchain construction method proposed in this paper exhibits better read and write performance, lower storage efficiency and storage overhead, and outperforms both the before-improved Level DB database and existing blockchain storage models. [ABSTRACT FROM AUTHOR]

Master–slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master–slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master–slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master–slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%. [ABSTRACT FROM AUTHOR]

Node similarity Top-k query has always been a hot topic in the graph field. The existing node similarity Top-k query methods often need to traverse the whole graph, so its iterative calculation process is very inefficient on the large-scale graph. Besides, when the graph changes dynamically, it is difficult to update the query process and results adaptively, leading to the low accuracy of the query results. To solve those problems, in this paper, we propose a node similarity Top-k query method of the large-scale dynamic graph with weak repeated path constraint. In this method, we first divide the similarity relation according to the correlation degree of nodes in the base graph. Then, we put forward the direct similarity and the generic similarity, meanwhile, give their definition and expression, prove the isolation of direct similarity query in the base graph based on the direct similarity relation. And then a quick query method for the direct similarity Top-k attached result set of i step walk is given on the basis of the value of Top-k. When the attached result set of the direct similarity query cannot satisfy the strict Top-k query requirement, by setting weak repeated path multilevel constraint and introducing the idea of the Monte Carlo simulation, we put forward single-edge weaken factor and change the base graph into many single-direction graphs by weak pageRank random walk mechanism, and then realize the cumulative calculation for the generic similarity Top-k complete result set in single-direction graph set. Finally, according to the dynamic characteristics of the graph, based on the principle of local adaptation, we set the trigger update strategy of the base graph and the linkage update strategy of the weak single-direction graph set to ensure the accuracy of the query and minimize the updating maintenance cost. The experiments show that the method in this paper has great advantages in query efficiency, query accuracy, and update efficiency.

The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load. [ABSTRACT FROM AUTHOR]

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted. [ABSTRACT FROM AUTHOR]

Feature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining tasks. Recently, many feature selection strategies have been developed since in most cases it is infeasible to obtain an optimal/reduced feature subset by using exhaustive search. Among these strategies, fuzzy rough set theory has proved to be an ideal candidate for dealing with uncertain information. This article provides a comprehensive review on the fuzzy rough set theory and two fuzzy rough set theory based feature selection methods, that is, fuzzy rough set based feature selection methods and fuzzy rough neural network based feature selection methods. We review the publications related to the fuzzy rough theory and its applications in feature selection. In addition, the challenges in the two types of feature selection methods are also discussed. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

MapReduce and its variants have emerged as viable competitors for big data analysis with a commodity cluster of machines. As an extension of MapReduce, ComMapReduce realizes the lightweight communication mechanisms to enhance the performance of query processing applications for big data. However, different communication strategies of ComMapReduce can substantially affect the executions of query processing applications. Although there is already the research work that can identify the communication strategies of ComMapReduce according to the characteristics of the query processing applications, some drawbacks still exist, such as relative simple model, too much user participation, and relative simple query processing execution. Therefore, an efficient ELM-based two stages query processing optimization model is proposed in this paper, named ELM to ELM (E2E) model. Then, we develop an efficient sample training strategy to train our E2E model. Furthermore, two query processing executions based on the E2E model, respectively, Just-in-Time execution and Queue execution, are presented. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model. [ABSTRACT FROM AUTHOR]

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events ("the coal mine disaster events"). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time. [ABSTRACT FROM AUTHOR]

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads. [ABSTRACT FROM AUTHOR]

With the rapid development of computer and the Internet techniques, the amount of data in all walks of life increases sharply, especially accumulating numerous high-dimensional big data such as the network transactions data, the user reviews data and the multimedia data. High-dimensional big data mixes the typical features of both high-dimensional data and big data, which has also brought new problems and great challenges for processing and optimizing the high-dimensional big data. In this case, the storage structure of high-dimensional big data is a critical factor that can affect the processing performance in a fundamental way. However, due to the huge dimensionality feature of high-dimensional data, the existing data storage techniques, such as row-store and column-store, are not very suitable for high-dimensional and large scale data. Therefore, in this paper, we present an efficient high-dimensional big data storage structure based on US-ELM, H igh-dimensional B ig Data File , named HB-File . Then, we propose a fuzzy cluster algorithm to differentiate the key dimension and non-key dimension of high-dimensional big data based on US-ELM, which can also gain the clusters of key dimension . After that, we propose the execution and API of HB-File based on the open source implementation of MapReduce, Hadoop system. With the intensive experiments, we show the effectiveness of HB-File in satisfying the storage of high-dimensional big data. [ABSTRACT FROM AUTHOR]

Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors' monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption.