Parallel Techniques for Large Data Analysis in the New Version of a Futures Trading Evaluation Service

A futures trading evaluation system is used to help investors analyze their trading history and find out the root cause of profit and loss, so that investors can learn from their past and make better decisions in the future. To analyze trading history of investors, the system processes a large volume of transaction data to calculate key performance indicators (KPI) as well as time series behavior patterns, and concludes some recommendations with the help of an expert knowledge base. This work is based on our early work of parallel techniques for large data analysis for futures trading evaluation service. In our early work, we have used the query rewriting technique to avoid joining between fact table and dimension table for OLAP aggregation queries, and used a data driven shared scanning of data method to compute KPIs for one customer. However, the query rewriting technique cannot eliminate joining for queries which aggregate on an intermediate level of the hierarchy of a dimensional table, so we propose a segmented bit encoding of dimensional table method which can eliminate the joining operation when the query aggregates on any level of the hierarchy of any dimensional table. Furthermore, our previous method perform badly when concurrency is high, so we propose an inter customer data scan sharing scheme to improve system performance in highly concurrent situations. We present our new experimental results.