Multidimensional Analysis and Mining of Call Detail Records Using Pattern Cube Algorithm
In the emerging and keenly competitive telecommunications market, it is imperative for mobile telecom operators to carry out regular analysis of their massive stored call logs to maintain and manage their numerous subscribers. Such sequential stream data analysis requires an efficient data mining algorithm and techniques bearing in mind the challenges posed by its massive size. Many data mining applications have been adapted for similar purposes. However, there has not been much emphasis on an in-depth mining of call detail records (CDR) as a multidimensional sequential stream data with its attending storage overhead. This paper proposes a novel algorithm for the multidimensional analysis of call records. Pattern Cube Algorithm (PCA) was implemented with a computer program and established empirically that: a massive CDR can be meaningfully summarized into a handy record as data mart with a gain of about 90% reduction in size and the possibility of processing a huge data from any server irrespective of the size of the target data. A quantitative exploration of the various gains in IT resources is conducted through an extensive experimental study on a sample of CDR adapted from MTN Communications Nigeria Limited.