As an important query type, interval query is widely used in social networks, information retrieval and database domain. Many kinds of optimization techniques have sprung up to support effective interval query. Although existing methods are efficient to handle single query, they all suffer from performance problem when the concurrent query loads exceed the processing capacity of the server such that more than 70% queries couldn't receive the results in the expected time. To solve this problem, this paper presents a method named SESIQ (shared execution strategy for interval queries). SESIQ batches interval queries, analyzes common operations among a group of interval queries and reduces duplicate data access to lower the cost of disk I/O and network transmission. The paper theoretically studies and analyzes SESIQ, and demonstrates the feasibility by large number of experiments based on two types of real datasets. Results show that SESIQ improves the performance of interval query by several ten folds.