Social network data has a certain degree of aggregation, namely the similar users are more prone to the same behavior. According to the conventional horizontal database shard method, a large amount of time and connection loss were consumed in order to access a plurality of databases in turn when performing the information query of these events. In order to solve this problem, the database shard strategy based on clustering analysis was proposed. Through clustering the characteristic scalars of social network subjects, the main body with the high aggregation was divided into one or as possible libraries to improve the query efficiency of the events, and to give consideration to load balancing, large data migration and other issues. The experimental results show that for the mainstream social networking events, the performance improvement of the proposed strategy is up to 23.4% at most, and local optimal load balance and zero data migration are realized. In general, the database shard strategy based on clustering analysis of social network, has a considerable advantage on improving query efficiency, balance load balancing and large data migration feasibility over the traditional conventional horizontal database shard method of cutting library.

To achieve the optimization of integrated flexible process planning and job shop scheduling, taking the flexibility of manufacturing process and order and manufacturing machine of the workpieces into account, for minimizing the maximum completion time of the product processing task, an artificial bee colony algorithm based on crossover and mutation was proposed. Aiming at the discrete characteristics of integrated flexible process and job shop scheduling, the process route was coded in sequence, and the job scheduling was based on the working procedure. To improve the performance of the algorithm, by means of crossover and mutation operation of process population and scheduling population, the employed foragers and onlookers bees seeked local optimality, and the scouts seeked global optimality. On this basis, the necessity of the integration research and the effectiveness of the improved algorithm were verified by two test cases.