Deep Network Analyzer (DNA): A Big Data Analytics Platform for Cellular Networks

Yang, Kai; Liu, Ruilin; Sun, Yanjia; Yang, Jin; Chen, Xin

doi:10.1109/jiot.2016.2624761

Cited by 40 publications

(18 citation statements)

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“…As a potential solution for AD with big data, we here present a simple yet effective AD method using big data analytics [46]. Compared with the existing AD methods, we detect abnormal values of key quality indicator (KQI) so as to monitor performance regression of the communication network, which often indicates poor user experience.…”

Section: Anomaly Detection Methods Using Big Data Analyticsmentioning

confidence: 99%

Anomaly detection for cellular networks using big data analytics

Zhao

Zhang

et al. 2019

IET Communications

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Section: Anomaly Detection Methods Using Big Data Analyticsmentioning

confidence: 99%

Anomaly detection for cellular networks using big data analytics

Zhao

Zhang

et al. 2019

IET Communications

Self Cite

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“…Amdoc's Deep Network Analyzer provides predictive maintenance and proactive network deployment for cellular networks. The authors in [114] presented a similar approach. Log analytics can be used for a variety of purposes.…”

Section: Big Data Analytics In the Industrymentioning

confidence: 99%

Big data analytics for wireless and wired network design: A survey

et al. 2018

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a b s t r a c tCurrently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions.In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks' control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial effort s toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

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“…In [5], the traffic volume was predicated before making service degradability to alleviate the communication pressure if the network has a heavy load. A statistical machine learning approach was employed to identify the anomalies within the incoming dataset collected via various probes in the network [6]. Many event-driven IoT applications define the triggering event based on the functions of observations, such as the weighted linear combination [7].…”

Section: Introductionmentioning

confidence: 99%