SPATIO-TEMPORAL PATTERN QUERIES PROCESSING ON MOVING OBJECT DATABASE WITH UNKNOWN DATA HANDLING - Muhammad Suria Nagara, Raihan
Spatio-temporal database is a database that manages data with spatial and temporal semantics to fulfill the need for the history of an object movement. However, to be able to handle a query that include two moving objects, ordinary spatio-temporal operations cannot be used. Therefore, Sakr and Güting (2011) proposed a complex query called STP query. Yogiandra (2019) created a tool that implements STP queries. However, in this implementation, a gap appears in the generated data type which is referred to as unknown data, that has not been handled and is considered non-existent. In this final project, the handling of unknown data is carried out by looking for the crossover interval. There are two stages in the search for crosseover interval, namely the possibility searching stage which determines whether a crossing occurs in the interval and the interval division stage which performs binary division of the interval until the interval is small enough to be considered as a crossover interval. The size of the crossover interval is determined by inputting the maximum crossing distance by the user. The implementation for the distribution method is in the form of lifted function which performs type overload on the lifted function in Yogiandra (2019)'s tool. To implement this function, the UnitCheck function is formed which will look for the crossover interval between two units. The UnitCheck function will be executed for all units inputted into the lifted function and the result will be matched with the function arguments the user entered. Based on the test results using specially generated data containing all cases of possible crossovers on unknown data, this method succeeded in processing unknown data that was not processed by the STP query tool created by Yogiandra (2019).