Focusing on the high execution cost of star-join between the fact table and multiple dimension tables in On-line Analytical Processing (OLAP), a star-join optimization technique was proposed for advanced multicore CPU (Central Processing Unit) and GPU (Graphics Processing Unit). Firstly, the vector index based vectorized star-join algorithm on CPU and GPU platforms was proposed for the intermediate materialization cost problem in star-join in multicore CPU and GPU platforms. Secondly, the star-join operation based on vector granularity was presented according to the vector division for CPU cache size and GPU shared memory size, so as to optimize the vector index materialization cost in star-join. Finally, the compressed vector index based star-join algorithm was proposed to compress the fixed-length vector index to the variable-length binary vector index, so as to improve the storage access efficiency of the vector index in cache under low selection rate. Experimental results show that the vectorized star-join algorithm achieves more than 40% performance improvement compared to the traditional row-wise or column-wise star-join algorithms on multicore CPU platform, and the vectorized star-join algorithm achieves more than 15% performance improvement compared to the conventional star-join algorithms on GPU platform; in the comparison with the mainstream main-memory databases and GPU databases, the optimized star-join algorithm achieves 130% performance improvement compared to the optimal main-memory database Hyper, and achieves 80% performance improvement compared to the optimal GPU database OmniSci. It can be seen that the vector index based star-join optimization technique effectively improves the multiple table join performance, and compared with the traditional optimization techniques, the vector index based vectorized processing improves the data storage access efficiency in small cache, and the compressed vector further improves the vector index access efficiency in cache.

The robustness of a trading model means that the model's profitability curve is less volatile and does not fluctuate significantly. To solve the problem of robustness of an algorithmic trading model based on Support Vector Regression (SVR), several strategies to derive a unified trading model and a portfolio diversification method were proposed. Firstly, the algorithm trade model based on SVR was introduced. Then, based on the commonly used indicators, a number of derived indicators were constructed for short term forecasting of stock prices. The typical patterns of recent price movements, overbought/oversold market conditions, and divergence of market conditions were characterized by these indicators. These indicators were normalized and used to train the trading model so that the model can be generalized to different stocks. Finally, a portfolio diversification method was designed. In the portfolio, the correlation between various stocks, sometimes leads to great investment losses; because the price of the stock with strong correlation changes in the same direction. If the trading model doesn't predict the price trend correctly, then stop loss will be triggered, and these stocks will cause loss in a mutual accelerated manner. Stocks were clustered into different categories according to the similarity, and a diversified portfolio was formed by selecting a number of stocks from different clustered categories. The similarity of stocks, was defined as the similarity of the recent profit curves on different stocks by trading models.Experiments were carried out on the data of 900 stocks for 10 years. The experimental results show that the transaction model can obtain excess profit rate over time deposit, and the annualized profit rate is 8.06%. The maximum drawdown of the trading model was reduced from 13.23% to 5.32%, and the Sharp ratio increased from 81.23% to 88.79%. The volatility of the profit margin curve of the trading model decreased, which means that the robustness of the trading model was improved.