关键词: 数据划分, 联机事务处理（OLTP）, 增量数据

Abstract: Nowadays, more and more applications have to be deployed in a distributed environment in order to handle huge volume of data, which need to use data partitioning to optimize the performance of parallel OLTP (on-line transaction processing) systems via carefully dividing the original data and newly appended data into different data nodes. This paper presents a novel data partitioning strategy for allocating both static and appended data, called TDPS (table dependency partitioning strategy). This strategy firstly partitions the initial data based on table dependency. When there are new data arriving, it will assign each data fragment to the partition most close to it. This paper conducts a series of experiments over TPC-C datasets and transactions. According to the results, the proposed strategy can effectively improve the system performance compared with previous methods.

Key words: data partitioning, on-line transaction processing (OLTP), appended data