Radio Frequency Identification (RFID) has been proposed as an efficient, effective and useful technology in the recent years. RFID technology is widely used across many application domains with promising results such as supply chain management, retail, access control, airline luggage management, medical identification, electronic paort and pet identidication. It uses radio frequency waves to read a unique identifier that is attached to an unexpensive tag by RFID reader from a far distance and without a line of sight. Reader receives radio waves from tags and convertes them into transformable data and then save data in computer servers. This technology facilitates and accelerates many applications, but it has proposed a challenge. RFID systems generates enormous amount of data where traditional methods are not capable of handling them and therefore new, novel and efficient techniques for processing are needed. The volume is so enormous that disusing the system comes into consideration. Data mining techniques are used for modeling of relationship and discovering hidden pattern in massive data consider being useful. RFID data is stored in multidimensional format by RFID warehousing which is more suitable for analysis and mining. Before using data mining algorithm, we use warehousing techniques to load and store the data in data warehouse. In this project, we have focused on preprocessing techniques and try to improve these techniques for improving data mining. In this project a new warehousing model is presented. In this new model, we add a new step into old model of warehousing, which is compression step (old model contains data gathering, data cleaning and data transformation and then data warehouse construction). This data structure is compressed to the highest degree possible without missing any data and therefore data warehouse is compressed as well. In this method, we consider a definite and deterministic sequence among different phases in the production line of car engines. Then we combine these phases at different levels. Therefore, low level data have been converted to higher and more meaningful levels. In this model, we save the combination of different levels of stages in the table in database and thus we can decompose the data at every level and return to level 0 again that is a main advantage of our method; that is to say, Key words Radio Frequency Identification, Data Mining, Data warehousing, and Data warehouse. ده
