SoLoMo analytics for telco Big Data monetization

被引：7

作者：

Cao, H. ^{[1
]}

Dong, W. S. ^{[2
]}

Liu, L. S. ^{[2
]}

Ma, C. Y. ^{[2
]}

Qian, W. H. ^{[2
]}

Shi, J. W. ^{[2
]}

Tian, C. H. ^{[2
]}

Wang, Y. ^{[1
]}

Konopnicki, D. ^{[3
]}

Shmueli-Scheuer, M. ^{[3
]}

Cohen, D. ^{[3
]}

Modani, N. ^{[4
]}

Lamba, H. ^{[5
]}

Dwivedi, A. ^{[5
]}

Nanavati, A. A. ^{[4
]}

Kumar, M. ^{[6
]}

机构：

[1] IBM Res China, Shanghai Lab, Shanghai 201203, Peoples R China

[2] IBM Res China, Beijing 100193, Peoples R China

[3] IBM Res Haifa, IBM Res Div, IL-31905 Haifa, Israel

[4] IBM Res India, New Delhi 70, India

[5] IBM Res India, Social Network Analyt Grp, New Delhi 70, India

[6] IBM Singapore Pte Ltd, IBM Telecom Ind Sales & Distribut, Singapore 486048, Singapore

来源：

IBM JOURNAL OF RESEARCH AND DEVELOPMENT | 2014年 / 58卷 / 5-6期

关键词：

COMMUNITY STRUCTURE;

D O I：

10.1147/JRD.2014.2336177

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The mobile Internet brought tremendous opportunities for businesses to capitalize on the vast amount of SoLoMo (social-location-mobile) data for delivering high-quality and personalized customer services. In this paper, we describe algorithms and technologies for discovering actionable customer insights using the combined power of social network, location pattern mining, and mobile usage analysis. We illustrate our implementation using Big Data platforms including IBM InfoSphere (R) BigInsights, IBM InfoSphere Streams, and IBM Netezza (R) Data Warehouse, while addressing various Big Data-related challenges, such as context generation of unstructured data and high-performance analytics for both data at rest and data in motion. The presented system combines location, social interactions, and user behavior data to find like-minded communities. The system leverages Big Data capabilities to attempt to scale to support the subscriber base of large telecoms in an efficient manner.

引用

页数：13

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