The traditional algorithms are difficult to implement the Top- K subgraph query on large-scale dynamic labeled graph due to high time or space complexity. For this reason, a dynamic Top- K interesting subgraph query method named DISQtop- K was proposed. In this algorithm, a Graph Topology Structure Feature (GTSF) index that include Node Topology Feature (NTF) index and Edge Feature (EF) index was established, which can effectively prune and filter the invalid nodes and edges. Then a multi-factor candidate set filtering strategy was put forward based on GTSF index, which can be used to further prune the query graph candidate sets. Considering that the dynamic changes in the graph may have an impact on the matching results, to ensure the real-time and accuracy of the query results, a new matching-verification method for Top- K interesting subgraph was also given, which has two stages of initial matching and dynamic correction. Experimental results show that compared with RAM and RWM, DISQtop- K method costs shorter time for index creation and occupies less space, which can effectively deal with dynamic Top- K interesting subgraph query on large-scale labeled graph.

The traditional feature selection methods always remove redundant features by using correlation measures, and it is not considered that there is a large amount of noise in a high-dimensional correlation matrix, which seriously affects the feature selection result. In order to solve the problem, a feature selection method based on Random Matrix Theory (RMT) was proposed. Firstly, the singular values of a correlation matrix which met the random matrix prediction were removed, thereby the denoised correlation matrix and the number of selected features were obtained. Then, the singular value decomposition was performed on the denoised correlation matrix, and the correlation between feature and class was obtained by decomposed matrix. Finally, the feature selection was accomplished according to the correlation between feature and class and the redundancy between features. In addition, a feature selection optimization method was proposed, which furtherly optimize the result by comparing the difference between singular value vector and original singular value vector and setting each feature as a random variable in turn. The classification experimental results show that the proposed method can effectively improve the classification accuracy and reduce the training data scale.

In the evolution of dynamic graph topology, in order to quantify the change of the relation between the nodes within a certain time, a concept, namely ubiquitous similarity node, was defined, and the level of ubiquitous similarity with the current node was measured by the frequent degree of interaction with the current node and the uniformity of distribution, and a similarity node query processing algorithm for large dynamic graph based on the snapshots was proposed. The concrete content includes: the snapshot expression of the dynamic evolution of graph, namely evolution dynamic graph; the semantic representation and its formal representation of the nodes' ubiquitous similarity in the dynamic evolution of graph, which was characterized by the frequent degree of interaction and uniformity coefficient of distribution; the matrix representation and processing method of the semantic of the nodes' ubiquitous similarity; the query algorithm for ubiquitous similarity nodes. The experimental results on the synthetic dataset and the real dataset show that the proposed algorithm can deal with the nodes' ubiquitous similarity query on the large dynamic graph, and be implemented in the practical applications.

Currently, the query of transmission lines monitoring system in smart grid is mostly aiming at the global query of Wireless Sensor Network (WSN), which cannot satisfy the flexible and efficient query requirements based on any area. The layout and query characteristics of network were analyzed in detail, and a query algorithm based on mesh structure in large-scale smart grid named MSQuery was proposed. The algorithm aggregated the data of query nodes within different grids to one or more logical query trees, and an optimized path of collecting query result was built by the merging strategy of the logical query tree. Experiments were conducted among MSQuery, RSA which used routing structure for querying and SkySensor which used cluster structure for querying. The simulation results show that MSQuery can quickly return the query results in query window, reduce the communication cost, and save the energy of sensor nodes.

Concerning that the monitoring data in large-scale smart grid are massive, real-time and dynamic, a new data storage approach supporting large-scale smart grid based on data-centric was proposed, which is a hierarchical extension scheme for storing massive dynamic data. Firstly, the extended Hash coding method could adjust the number of storage nodes dynamically to avoid data loss of sudden or frequent events and increase system availability. Then, the multi-threshold leveling method was used to distribute data to multiple storage nodes, which could avoid hotspot storage problem and achieve load balance. Simulation results show that this method is able to satisfy the need of massive data storage, to obtain better load balance, to lower the total energy consumption and to extend the life cycle of the whole network.

With Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA) adopted as the kernel processors, the embedded network conversion gateway of high real-time was designed to realize the conversion between fieldbus and Ethernet. The conversion was finished on the physical layer and the data link layer, and the problem of direct communications between fieldbus and Ethernet was solved. The experimental results indicate that the feasibility and effectiveness of the design satisfy the requirements.

In this paper, concerning the complexity of calculation, maintenance and matching ambiguity, a Deep Web query interface identification approach based on label coding was proposed after studying the current identification approach of query interface thoroughly. This approach coded and grouped labels by the directivity and the irregularity of arrangement of the query interface. The identification approach of simple attributes and composite attributes and the processing approach of isolated texts were proposed, taking each label group as an independent unit to identify the feature information. The texts matching the elements were determined by the constraints on the label subscript, which greatly reduced the number of texts considered in matching an element and avoided the problem of matching ambiguity caused by massive heuristic algorithm, and the presentation of nested information was solved by twice clustering effectively and efficiently.