Due to the high connectivity and great convenience, many E-commerce application systems have a high transaction volume. Consequently, the system state changes rapidly and it is likely that customers issue transactions based on out-of-date state information. Thus, the potential of transaction abortion increases greatly. To address this problem, we proposed an M3-transaction model. An M3-transaction is a generalized transaction where users can issue their preferences in a request by specifying multiple criteria and optional data resources simultaneously within one transaction. In this paper, we introduce the transaction grouping and group evaluation techniques. We consider evaluating a group of M3-transactions arrived to the system within a short duration together. The system makes optimal decisions in allocating data to transactions to achieve better customer satisfaction and lower transaction failure rate. We apply the fuzzy constraint satisfaction approach for decision-making. We also conduct experimental studies to evaluate the performance of our approach. The results show that the M3-transaction with group evaluation is more resilient to failure and yields much better performance than the traditional transaction model.