For energy consumption and severe environmental problems brought by big data, building an energy-efficient green database system has become a key requirement and an important challenge. To solve the problem that traditional database systems mainly focus on performance, and are lack of energy consumption perception and optimization, an energy consumption perception model based on database workload was proposed and applied to the database system based on Solid-State Drive (SSD). Firstly, the consumption of major system resources (CPU, SSD) during database workload execution was quantified as time overhead and power consumption overhead. Based on basic I/O type of SSD database workload, a time cost model and a power consumption overhead model were built, and an energy consumption perception model with uniform resource unit was implemented. Then, multi-variable linear regression mathematical tools were used to solve the model, and in the exclusive environment and competitive environment, the energy estimation accuracy of the model for different I/O types of database workload was verified. Finally, the experimental results were analyzed and the factors that affect the model accuracy were discussed. The experimental results show that the model accuracy is relatively high. Under ideal conditions that DBMS monopolized system resources, the average error is 5.15% and the absolute error is no more than 9.8%. Although the accuracy in competitive environment is reduced, the average error is less than 12.21%.The model can effectively build an energy-aware green database system.

Under MapReduce computing scenarios, complex data mining algorithms typically require multiple MapReduce jobs' collaboration process to compete the task. However, serious redundant disk read and write and repeat resource request operations among multiple MapReduce jobs seriously degrade the performance of the algorithm under MapReduce. To improve the computational efficiency of ItemBased recommendation algorithm, firstly, the performance issues of the ItemBased collaborative filtering algorithm under MapReduce platform were analyzed. Secondly, the execution efficiency of the algorithm was improved by taking advantage of Spark's performance superiority on iterative computation and memory computing, and the ItemBased collaborative filtering algorithm under Spark platform was implemented. The experimental results show that, when the size of the cluster nodes is 10 and 20, the running time of the algorithm in Spark is only 25.6% and 30.8% of that in MapReduce. The algorithm's overall computing efficiency of Spark platform improves more than 3 times compared with that of MapReduce platform.

Concerning the problem of data sparseness, cold start and low user experience of recommendation system, a video recommendation algorithm based on clustering and hierarchical model was proposed to improve the performance of recommendation system and user experience. Focusing on the user, similar users were obtained by analyzing Affiliation Propagation (AP) cluster, then historical data of online video of similar users was collected and a recommendation set of videos was geberated. Secondly, the user preference degree of a video was calculated and mapped into the tag weight of the video. Finally, a recommendation list of videos was generated by using analytic hierarchy model to calculate the ranking of user preference with videos. The experimental results on MovieLens Latest Dataset and YouTube video review text dataset show that the proposed algorithm has good performance in terms of Root-Mean-Square Error (RMSE) and the recommendation accuracy.

In order to build energy-saving green database, a database model of dynamic power consumption based on the smallest unit of Structured Query Language (SQL) resource (Central Processing Unit (CPU), disk) consumption. The proposed model profiled the dynamic power consumption and mapped the main hardwares (CPU, disk) resource consumption to power consumption. Key parameters of the model were fitted by adopting the method of multiple linear regression to estimate the dynamic system power in real-time and build the unit-unified model of dynamic power consumption. The experimental results show that, compared with the model based on the total number of tuples, the total number of CPU instructions can better reflect the CPU power consumption. The average relative error of the constitutive model is less than 6% and the absolute error of the constitutive model is less than 9% while the DataBase Management System (DBMS) monopolizes system resources in the static environment. The proposed dynamic power consumption model is more suitable for building energy-saving green database.