A proposed architecture for generic and scalable CDR analytics platform utilizing big data technology

Telecom Call Details Record (CDR) data-set is considered a rich source of valuable information that will bring new big revenues to Communication Service providers (CSP) as well as it will empower many out-telco services such as transportation, education, health programs, and business analysis in resource management and planning, decision making, and processes optimization. However, extracting these valuable information from raw CDRs with the classical SQL and BI systems is very costly and has poor performance measures. This is due to the big volume of CDR data-set, the high and growing data rate and the large number of fields it contains. Many CDR analytics systems were built using Big Data technology, to overcome the scalability problem of the centralized computing, but the heterogeneity usage of CDR analytics have not been considered; they were built for specific and predetermined use cases. This paper presents a proposed platform architecture for real, near-real time and batch CDR analysis to provide analytics for heterogeneous applications, through designing a high generic and scalable platform. This paper illustrates the platform design consideration along with how the proposed architecture was built. Moreover, it gives a brief functional description and implementation suggestions for each component in the architecture

Big data analysis solutions using mapReduce framework

Recently, data that generated from variety of sources with massive volumes, high rates, and different data structure, data with these characteristics is called Big Data. Big Data processing and analyzing is a challenge for the current systems because they were designed without Big Data requirements in mind and most of them were built on centralized architecture, which is not suitable for Big Data processing because it results on high processing cost and low processing performance and quality. MapReduce framework was built as a parallel distributed programming model to process such large-scale datasets effectively and efficiently. This paper presents six successful Big Data software analysis solutions implemented on MapReduce framework, describing their datasets structures and how they were implemented, so that it can guide and help other researchers in their own Big Data solutions

CDR analysis using big data technology

Call Detail Records (CDR) is a valuable source of information; it opens new opportunities for telecom industry and maximize its revenues as well as it helps the community to raise its standard of living in many different ways. However, we need to analyze CDRs in order to extract its big value. But CDRs has a huge volume, variety of data and high data rate, while current telecom systems are designed without these issues in mind. CDRs can be seen as Big Data source, and hence, it is applicable to use Big Data technologies (storage, processing and analysis) in CDR analytics. There are considerable research efforts to address the CDR analysis challenges. This paper presents the use of Big Data technology in CDR analysis by giving some CDR analytics based application examples, highlighting their architecture, the utilized Big Data tools and techniques, and the CDR use case scenarios