A dynamic workload description method and a dynamic workload matching algorithm were proposed to improve the accuracy of workload matching in automatic parameter tuning systems, such as OtterTune, with static workload description. First, a dynamic workload description method was proposed to describe the workload changes more accurately. Then, for the problems such as irregular sequences in workload matching and that the Euclidean distance algorithm is no longer applicable, a dynamic workload matching algorithm using data alignment was proposed based on the Dynamic Time Warping (DTW) algorithm. Finally, the proposed methods were applied to OtterTune to form a dynamic workload-based tuning tool D-OtterTune (Dynamic workload based OtterTune), and several experiments were conducted on it. Experimental results showed that, compared with the original method OtterTune, with the stable improvement in the workload matching accuracy for automatic parameter tuning brought by the dynamic workload matching method, D-OtterTune had the accuracy increased by 3%. It can be seen that D-OtterTune can have a significant impact on the overall business performance in data-intensive applications.

Motivated by the poor performance of existing domestic pedigree systems on data sharing, scalability and editing efficiency, an online pedigree editing system was proposed based on Browser/Server (B/S) architecture and graph database. First, the proposed system took advantage of B/S architecture to support online collaborative entering, so as to promote data entering efficiency. Second, the system used database to store pedigrees for better management and retrieval, and promoted the data sharing. Third, the system greatly improved the efficiency of data processing, because it was managed by graph database and pedigrees are graphs in nature. Finally, the system is empirically proven to be effective through systematical experiments with real pedigree data, LIU's pedigree data, which contained over 200000 people. Specifically, the proposed system based on graph database Neo4j is 50% better than that based on relation database PostgreSQL on storage space; and the query responding time of the system based on Neo4j is respectively 20%, 80%, 16% and 15% of that based on PostgreSQL for descendant query, ancestor query, relative query and descendant gender query. According to the experimental results, a conclusion can be achieved that the system can be used to process massive pedigree data efficiently and support online collaborative entering.