A performance analysis for UMTS packet switched network based on multivariate KPIs

Ouyang, Ye; Fallah, M. Hosein

doi:10.1109/wts.2010.5479629

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…Performance metrics are utilized by cellular carrier engineering teams to determine the overall regional health of the network. Review of the cellular carrier performance metrics for the 90 days prior to critical events in the region surrounding the vicinity of the subscriber device communications sessions under analysis will aid in determining whether the cellular network was functioning nominally (Ouyang & Falla, 2010 general condition of the network within the geographic region of analysis (Andleeb & Ali, 2015). This review will aid in validating the impact of planned and unplanned maintenance events on the state of network functionality.…”

Section: Analysis Of Cellular Carrier Performance Metricsmentioning

confidence: 99%

Forensic Cell Site Analysis: A Validation & Error Mitigation Methodology

Minor¹

2017

JDFSL

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The E911 Initiative in the mid-1990s established an opportunity to obtain location specific digital evidence of subscriber activity from cellular carriers. Call Detail Records (CDR) containing Cell Site Location Information (CSLI) evidence production was made available from cellular carriers in response to the CALEA, 911 and ECPA acts. In the late 1990s, cellular carriers began to produce evidence for investigative and litigation purposes. CDR/CSLI evidence has become an important evidentiary focus in the courtroom. This research project resulted in the creation of a method of validating cellular carrier records accuracy and mitigating errors in forensic cell site analyst conclusions. The process establishes a scientific foundation critical to satisfying key Daubert requirements. The United States Patent and Trademark Office (USPTO) awarded a patent for this methodology.

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Section: Analysis Of Cellular Carrier Performance Metricsmentioning

confidence: 99%

Forensic Cell Site Analysis: A Validation & Error Mitigation Methodology

Minor¹

2017

JDFSL

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“…For example, a value can be considered normal in a certain context (in a peak period) but abnormal in another context (in off-peak periods), and the differentiation can only be made clear when each value has a time stamp associated with it. An overview of outlier detection for temporal data can be found in [17], which comprises ensemble methods (e.g., [18,19]), time-series models (e.g., with ARIMA or GARCH models in [20]), and correlation analysis [21,22].…”

Section: Research Backgroundmentioning

confidence: 99%

Latent Clustering Models for Outlier Identification in Telecom Data

Ouyang

Huet²,

Shim

et al. 2016

Mobile Information Systems

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Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA) and time-dependent Gaussian Mixture Models (time-GMM). These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information). We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.

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“…Погрешности вычис-ления собственных значений и собственных векторов приведут к неверному выбору наилучшего решения, по-лучаемого методом анализа иерархий. Другими слова-ми, из-за ошибочного задания (измерения, вычисления) исходных данных возникает риск выбора неверного или не самого эффективного набора КПЭ [9].…”

Section: локализация собственных значений при наличии возмущений элемunclassified

“…Как показано в [9], при использовании метода анализа иерархий для поиска наилучшего решения из конечного множества возможных решений, можно применять нелинейную свертку критериев. Однако в [11] утверждается, что при аддитивном виде целевой функции линейная свертка обеспечивает получение «лучшего» решения.…”

Section: таблицаunclassified