With the rapid development of knowledge graph and its wide usage in various vertical domains, the requirements for efficient processing of Resource Description Framework (RDF) data has increasingly become a new topic in the field of modern big data management. RDF is a data model proposed by W3C to describe knowledge graph entities and inter-entity relationships. In order to effectively cope with the storage and query of the large-scale RDF data, many scholars consider managing RDF data in a distributed environment. The key problem faced by the distributed storage of RDF data is data partitioning, and the performance of Simple Protocol and RDF Query Language (SPARQL) queries is largely determined by the results of partitioning. From the perspective of data partitioning, two types:graph structure-based RDF data partitioning methods and semantics-based RDF data partitioning methods, were mainly focused on and described in depth. The former include multi-granularity hierarchical partitioning, template partitioning and clustering partitioning, and are suitable for the wide semantic categories scenes of general domain query, while the latter include hash partitioning, vertical partitioning and pattern partitioning, and are more suitable for the environments of the relatively fixed semantic categories of vertical domain query. In addition, several typical partitioning methods were compared and analyzed to provide enlightenment for the future research on RDF data partitioning methods. Finally, the future research directions of RDF data partitioning methods were summarized.

Hbase can only use key value query, it is not suitable for multidimensional query of mobile objects in road network, which leads to inefficiency in storing index and query. In order to solve this problem, an efficient HBase indexing framework for Road network Moving objects (RM-HBase) was designed and implemented on the basis of HBase storage structure. Firstly, the upper Hmaster and lower HregionServer of the primary HBase index structure were improved to solve the hot distribution problem of distributed cluster data and improve the query efficiency of spatial data. Secondly, the road network moving object index - Road Network tree (RN-tree) was proposed to solve the problem of "dead space" in space division and improve the query efficiency of road sections in the space at the same time. Then, based on the above improvements of HBase index, the query algorithms for spatio-temporal range query, spatial-temporal K Nearest Neighbor (KNN) query and moving object trajectory query were designed respectively. Finally, the Spatial-TEmporal HBase IndeX (STEHIX) framework based on HBase distributed database was selected as the contrast object, the performance of RM-HBase was respectively analyzed from two aspects of the performance of index framework and the efficiency of query algorithm. The experimental results show that, the proposed RM-HBase is superior to the STEHIX framework in both the performance of data equilibrium distribution and the query performance of spatio-temporal query algorithm, and it is helpful to promote the efficiency of spatial-temporal index for the moving object data in mass road network.

A fast multi-objective hybrid evolutionary algorithm was proposed for solving bi-criteria Flow shop Scheduling Problem (FSP) with the objectives of minimizing makespan and total flow time. The sampling strategy of the Vector Evaluated Genetic Algorithm (VEGA) and a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function were integrated with the proposed algorithm. The new sampling strategy made up the shortage of the sampling strategy of VEGA. VEGA was good at searching the edge region of the Pareto front, but it neglected the central area of the Pareto front, while the new sampling strategy preferred the center region of the Pareto front. The fusion of these two mechanisms ensured that the hybrid algorithm can converge to the Pareto front quickly and smoothly. Moreover, the algorithm efficiency was improved greatly without calculating the distance. Simulation experiments on Taillard benchmark sets show that, compared with Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), the fast multi-objective hybrid evolutionary algorithm is improved in the performance of convergence and distribution, and the efficiency of the algorithm has been improved. The proposed algorithm can be better at solving the bi-criteria flow shop scheduling problem.