1. A method for parallel mining of temporal relations in a large event file, comprising: aligning an event file based on customer identification (ID) and event time at which each event has occurred; generating a set of large event types satisfying a preset support or more from the event file; converting the event file into a large event sequence including the large event type set; summarizing the large event sequence and then creating a time interval data file; generating candidate temporal relations from the time interval data file, and deriving frequent temporal relations satisfying a preset support or more from the candidate temporal relations; and generating a temporal relation rule from the derived frequent temporal relations, wherein converting the event file into the large event sequence comprises: extracting event records belonging to the large event types by processing the event file in parallel using a Map function; converting event times at which the events have occurred into sequence numbers; and generating the large event sequence by combining large events type sets, event times of which are converted into sequence numbers, in parallel using a Reduced function.
1. A method for parallel mining of temporal relations in a large event file, comprising: aligning an event file based on customer identification (ID) and event time at which each event has occurred; generating a set of large event types satisfying a preset support or more from the event file; converting the event file into a large event sequence including the large event type set; summarizing the large event sequence and then creating a time interval data file; generating candidate temporal relations from the time interval data file, and deriving frequent temporal relations satisfying a preset support or more from the candidate temporal relations; and generating a temporal relation rule from the derived frequent temporal relations, wherein converting the event file into the large event sequence comprises: extracting event records belonging to the large event types by processing the event file in parallel using a Map function; converting event times at which the events have occurred into sequence numbers; and generating the large event sequence by combining large events type sets, event times of which are converted into sequence numbers, in parallel using a Reduced function. 10. The method of claim 1 , wherein deriving the frequent temporal relations is configured to obtain sets of candidate temporal relations in parallel from the time interval data file using a Map function and a temporal relation table, and calculating supports of the candidate temporal relations in parallel using a Reduce function.