Analytical query is an important way to get value from big data in data warehouse. With the growth of data, the same query needs to be executed periodically, which inevitably introduces redundant calculation on historical data. One type of incremental optimization technology reduces redundant calculation by reusing intermediate results of historical data. However it has following problems:1) it isn't transparent for user; 2) choice of historical result storing/reusing position is not intelligent; and 3) optimization gains is limited. This article designs an incremental optimization method, which is guided by the semantic rules. This method focuses on both user transparency and optimization gains, and extends grammar to support incremental description. Historical result storing/reusing location is firstly chosen by operators' operational semantics and output semantics. Positions are then adjusted according to cost model and physical task's division positions. At last, optimized tasks-DAG is generated with the ability to run in a distributed computing framework (such as MapReduce) periodically. This paper implements a prototype, called HiveInc, based on Apache Hive. Experimental results on TPC-H show that, compared to non-optimization, HiveInc can obtain average 2.93 speed-up and highest 5.78 speed-up. Compared to classical optimization techniques, IncMR and DryadInc, speed-up of 1.69 and 1.61 can be obtained respectively.