Performance analysis of the past, present and future indexing methods for spatio-temporal data

In spatio-temporal data, PPF (past, present and future) is used as one of the main indexing methods. Indexing methods are developed to effectively process the user query in many real-time and moving object management applications. Spatio-temporal based indexing methods are used to predict data in fleet management, traffic prediction, Radio frequency identification and sensor networks. Indexing methods are focused on four different directions: indexing past data, indexing present data, indexing future data and indexing past, present and future data (PPF). The past data is used for investigation and present data is used finding the current location of the moving objects and data, future data used to predict next data in spatio-temporal environments. This work presents a performance analysis of various PPF supported indexing methods. The analysis parameters of PPF are time complexity, supporting queries, integrated indexing methods, updating cost and query cost. The time complexity is based disk access, updating cost, access types, access times, insertion time, deletion time, and space overhead. Finally, this paper presents a comparison of various indexing methods with its parameters.