Kanthaka: Big Data Caller Detail Record (CDR) Analyzer for Near Real Time Telecom Promotions

Jayawardhana, P.; Kumara, A. D. B.; Perera, Dulanjana M.; Paranawithana, A.

doi:10.1109/isms.2013.40

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…At present, there are a variety of big data analysis techniques [3], [4]. Among them, Hadoop is an open-source software framework for distributed storage and processing for huge data sets on computer clusters built on cheap hardware [5].…”

Section: A Existing Problemsmentioning

confidence: 99%

A Hive-Based Retrieval Optimization Scheme for Long-Term Storage of Massive Call Detail Records

2020

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With the dramatic rise of mobile internet users and the administrative requirements of longterm data retention, telecom providers are facing increasingly challenging storage and retrieval issues of call detail records (CDRs). The existing storage system can only achieve the requirement of online query and offline analysis of the CDRs. However, to the best of our knowledge, few studies have focused on the topic of CDRs retrieval optimization with long-term storage. In order to improve the retrieval speed while ensuring a high compression ratio, in this paper we propose a novel hash storage scheme, termed dual-column bucketing (DCB), based on the Hive platform by making use of its Bucketing nature. Compared to the conventional scheme, the proposed DCB scheme can improve the performance both for CDRs compression and query. Second, similar storage scenarios such as storage of SMS, email and extended detail records (XDRs) are included in the optimization scope of the DCB. Experiments on real-world CDRs show that in contrast to the conventional scheme, the proposed DCB scheme can save the storage space by approximately 40%, reduces the amount of disk read to 2%, and improve the retrieval speed of known phone number queries by up to seven times. INDEX TERMS Bucketing, call detail records, hash storage, long-term storage.

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Section: A Existing Problemsmentioning

confidence: 99%

A Hive-Based Retrieval Optimization Scheme for Long-Term Storage of Massive Call Detail Records

2020

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“…In [12], authors discussed about the system which selects eligible users for predefined promotion and then analyzing the Caller Detail Records, while rapidly escalating subscriber base. In our paper, we are analyzing customers on various data sets and then targeting the high value and potential churn segment of the customers.…”

Section: Related Workmentioning

confidence: 99%

Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics

Singh¹,

Singh²

2017

IJEME

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Since the more importance is played on customer's behavior in today's business market, telecom companies are not only focusing on customer's profitability to increase their market share but also on their potential churn customers who could terminate the relation with the company in near future. Big data promises to promote growth and increase efficiency and profitability across the entire telecom value chain. This paper presents a framework for targeting high value customers and potential churn customers. Firstly, customers are segmented on basis of RFM (Recency-Frequency-Monetary) analysis and finally customers in each segment are targeted by various offers on basis of their similar characteristics.

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“…This work demonstrates benefits of applying stream computing to time-window based operation. Jayawardhana et al [4] proposed a CDR analyzer for near real time telecom promotions. This work adopts Cassandra [23], a NoSQL database with high write throughput, as data store.…”

Section: Related Workmentioning

confidence: 99%

Building a CDR analytics platform for real-time services

Shih

Huang

Lin

et al. 2014

The 16th Asia-Pacific Network Operations and Management Symposium

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Kanthaka: Big Data Caller Detail Record (CDR) Analyzer for Near Real Time Telecom Promotions

Cited by 7 publications

References 8 publications

A Hive-Based Retrieval Optimization Scheme for Long-Term Storage of Massive Call Detail Records

A Hive-Based Retrieval Optimization Scheme for Long-Term Storage of Massive Call Detail Records

Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics

Building a CDR analytics platform for real-time services

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