Abstract: Moving object index is widely used in location-based services. Since people spend large parts of their lives in indoor spaces (e.g. hospitals, shopping malls, subway systems, etc.), effective management of indoor mobile data becomes very important. Existing indoor moving object indices focus on historical data queries, and only one type of queries is supported. In this paper, we propose a novel index, called MQII (multiple queries indoor index), which supports not only history queries and present queries, but also object queries and range queries. MQII is based on graph-based model, and can index two aspects with the object list and bucket list structure, such as the object and spatial-temporal scales. In order to improve the query performance, we present a RFID (radio frequency identification) data preprocessing method to reduce the size of the input data sets for MQII. Furthermore, effective update and query algorithms are developed. Experimental results show that compared with existing indoor moving object indices, the data preprocessing can reduce the amount of data. In addition, the index we proposed not only supports history queries and present queries, but also provides efficient object location queries, trajectory queries and range queries. This method can be used in various indoor spaces such as office buildings, hospitals and hotels.