Telecommunication Data Research Articles

Построение графа знаний по телекоммуникационным данным

The paper presents a method for building a knowledge graph from telecommunication data using proprietary and ref-erence models commonly employed in the telecommunications domain. Reference models are based on those included in the framework developed by the TM Forum consortium. The proposed approach involves building a knowledge graph for proprietary models through automated processing of autotest log files and billing system database tables. The relevance of knowledge graphs stems from their structured and semantic nature, as well as their potential for applying machine learning algorithms to generate recommendations for optimizing telecommunication processes and systems. A method based on a multi-step reasoning approach is proposed for creating interpretable recommendations by predict-ing and restoring missing links in a proprietary knowledge graph. This approach treats multi-step reasoning as a ques-tion-answering task using natural language processing techniques. The implementation of the proposed solution, lever-aging a transformer-based neural network architecture, yielded interpretable results while maintaining metric values comparable to existing methods.

Intensified customer churn Prediction: Connectivity with weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation

In the telecommunication industry, forecasting customer turnover in telecom networks is crucial for maintaining customers and increasing profits. The proposed model explores the importance of predicting churn, emphasizing its financial impact and strategic significance for telecommunications companies. Manual methods for predicting churn are restricted due to their dependence on basic models and human opinion, leading to less than ideal precision and effectiveness. Artificial Intelligence (AI)-based methods have arisen as promising solutions in light of these limitations. Nevertheless, existing AI methods frequently encounter issues like over-fitting, restricted interpretability, and suboptimal generalization. The presented system introduces a novel technique, named “MBP-WMLP” abbreviated as Multipath Back Propagation with Weighted MLP, to improve prediction accuracy in telecommunication networks. The respective approach utilizes a specialized multi-layer perceptron (MLP) structure with weights, designed for managing the intricacies of telecommunications data. Moreover, an improved multipath back-propagation method is presented to improve the optimization of model training and convergence. The projected model combines weighted MLP and enhanced multipath back-propagation to achieve better prediction accuracy, increased interpretability, and improved generalization capabilities. Furthermore, the proposed technique will be applied for authentic Telco customer churn datasets containing sanitized customer activity features and churn labels indicating subscription cancellations for creating predictive models. The projected approach is intended to show potential for improving customer loyalty tactics and boosting profits for telecom companies in a more competitive market environment.

Intensified Customer Churn Prediction: Connectivity with Weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation

In the telecommunication industry, forecasting customer turnover in telecom networks is crucial for maintaining customers and increasing profits. The proposed model explores the importance of predicting churn, emphasizing its financial impact and strategic significance for telecommunications companies. Manual methods for predicting churn are restricted due to their dependence on basic models and human opinion, leading to less than ideal precision and effectiveness. Artificial Intelligence (AI)-based methods have arisen as promising solutions in light of these limitations. Nevertheless, existing AI methods frequently encounter issues like over-fitting, restricted interpretability, and suboptimal generalization. The presented system introduces a novel technique, named "MBP-WMLP” abbreviated as Multipath Back Propagation with Weighted MLP, to improve prediction accuracy in telecommunication networks. The respective approach utilizes a specialized multi-layer perceptron (MLP) structure with weights, designed for managing the intricacies of telecommunications data. Moreover, an improved multipath back-propagation method is presented to improve the optimization of model training and convergence. The projected model combines weighted MLP and enhanced multipath back-propagation to achieve better prediction accuracy, increased interpretability, and improved generalization capabilities. Furthermore, the proposed technique will be applied to authentic Telco customer churn datasets containing sanitized customer activity features and churn labels indicating subscription cancellations for creating predictive models. The projected approach is intended to shows potential for improving customer loyalty tactics and boosting profits for telecom companies in a more competitive market environment.

Gumbel–Logistic Unit Distribution with Application in Telecommunications Data Modeling

The manuscript deals with a new unit distribution that depends on two positive parameters. The distribution itself was obtained from the Gumbel distribution, i.e., by its transformation, using generalized logistic mapping, into a unit interval. In this way, the so-called Gumbel-logistic unit (abbr. GLU) distribution is obtained, and its key properties, such as cumulative distribution function, modality, hazard and quantile function, moment-based characteristics, Bayesian inferences and entropy, have been investigated in detail. Among others, it is shown that the GLU distribution, unlike the Gumbel one which is always positively asymmetric, can take both asymmetric forms. An estimation of the parameters of the GLU distribution, based on its quantiles, is also performed, together with asymptotic properties of the estimates thus obtained and their numerical simulation. Finally, the GLU distribution has been applied in modeling the empirical distributions of some real-world data related to telecommunications.

SUBSCRIPTION FRAUD DETECTION USING AUTOENCODER IN CASE OF ETHIOTELECOM

Nowadays, telecom services are becoming an essential communication and business facilitators. However, the development of telecom services motivates fraudsters for illegal use. Hence, telecom fraud becomes a serious challenge in telecommunication sector by losing telecom revenue. It also results in poor quality of services for their customers. Telecom data, SMS and voice call are not free from security issues. Despite SMS, and USSD services becomes good options over installable mobile based or web based applications due to lesser cost and real time support, the methods provide fixed amount requirements which is not dynamic way to address the need of these services subscribers. The other challenge of these services is that; the services are not safe from frauds as such, especially in GSM switches. Subscription fraud is one type of fraud in today’s telecom business which is a common telecom fraud. The need of fraudsters is to make money illegally or getting telecom services with the intention of not to pay for the service they used. The prime objective of this study is to build a predictive model using deep auto-encoder to detect subscription fraud in case of Ethiotelecom. The performance of fraud detection model is 98.95% validation accuracy on ethiotelecom call detail record dataset using deep neural network autoencoder and CNN-LSTM autoencder algorithms at threshold of 0.0103 and 0173 respectively.

Data retention in the light of copyright infringements: Protecting the status quo or seeking a third way? (Case C-470/21 La Quadrature du Net II)

In La Quadrature du Net II, the EU Court of Justice addressed – once again – the issue of the application of national retention legislation providing for the generalized retention of telecommunications data. This time, unlike in the previous cases, the problem was examined not in relation to measures used in the areas of combating serious crime or pursuing national security objectives, but in relation to counteracting online copyright infringements. Hence the case, decided by the CJEU sitting as the Full Court, concerned an important and topical issue, namely the possibility of countering mass copyright infringements in a situation where, according to a strict reading of the previous case law, the permissibility of imposing an obligation on telecommunications operators to collect information on users’ IP addresses was questioned. The LQDN II judgment – commented on by some as diverging from previous case law – is an important and necessary complement to the Court's earlier interpretation and, at the same time, a guideline on how to design retention laws to be applied not only in the area of countering copyright infringements, but also in other important areas, such as the fight against hate speech.

The Court of Justice in Staatsanwaltschaft Berlin v. M.N. (EncroChat): From cross-border, data-driven police investigations to evidence admissibility

The ruling of the Grand Chamber of the Court of Justice of the EU (CJEU) in Case C-670/22 Staatsanwaltschaft Berlin v. M.N. addresses the realities of data-driven police investigations in the light of the Directive 2014/41/EU on the European Investigation Order (EIO Directive). In particular, it examines the cross-border, digital investigation of the encrypted communication network EncroChat and the use and sharing of intercepted telecommunication data in criminal proceedings. In doing so, the CJEU offers clarifications as to the interpretation of the term ‘issuing authority’ under the EIO Directive and the proportionality requirements for issuing an EIO, taking into account its prior jurisprudence on the implications of access to traffic and location data for the private life of the persons concerned. More importantly, this CJEU ruling takes a stand on admissibility of intercepted data as evidence in criminal proceedings, considering the technical complexity of contemporary investigation measures and the secrecy of said police methods. Overall, although this judgment seems to hold no great surprises regarding the interpretation of the EIO Directive's provisions, it underscores the important role of the national legislator and national courts in striking the right balance between rearing the rewards of data-driven policing and safeguarding defence rights.

Resource demands in telco data centers

Telecommunication (telco) cloud services have emerged as crucial components in the modern digital landscape, offering extensive capabilities for data management, connectivity, and service provision. However, research on telco clouds lacks comprehensive data on the characteristics of production workloads, which is fundamental for designing effective resource management systems, such as workload schedulers and power management mechanisms. To this end, this paper addresses a substantial gap in telco cloud research by creating a comprehensive dataset that encapsulates crucial information regarding the pattern demands of applications within telco data centers. In addition, the proposed dataset contributes to the field by enabling strategic network configuration, optimizing data center sizing, facilitating proactive decision-making for data center operations, but its applicability extends beyond these cases. These examples illustrate the practical value of the dataset in enhancing efficiency, reducing operational costs, and ensuring optimal performance within telecommunication data centers.

Change-Point Detection in Functional First-Order Auto-Regressive Models

A sample of continuous random functions with auto-regressive structures and possible change-point of the means are considered. We present test statistics for the change-point based on a functional of partial sums. To study their asymptotic behavior, we prove functional limit theorems for polygonal line processes in the space of continuous functions. For some situations, we use a block bootstrap procedure to construct the critical region and provide applications. We also study the finite sample behavior via simulations. Eventually, we apply the statistics to a telecommunications data sample.

Improving Minority Class Recall through a Novel Cluster-Based Oversampling Technique

In this study, we propose an approach to address the pressing issue of false negative errors by enhancing minority class recall within imbalanced data sets commonly encountered in machine learning applications. Through the utilization of a cluster-based oversampling technique in conjunction with an information entropy evaluation, our approach effectively targets areas of ambiguity inherent in the data set. An extensive evaluation across a diverse range of real-world data sets characterized by inter-cluster complexity demonstrates the superior performance of our method compared to that of existing oversampling techniques. Particularly noteworthy is its significant improvement within the Delinquency Telecom data set, where it achieves a remarkable increase of up to 30.54 percent in minority class recall compared to the original data set. This notable reduction in false negative errors underscores the importance of our methodology in accurately identifying and classifying instances from underrepresented classes, thereby enhancing model performance in imbalanced data scenarios.

Detection of Emergent Anomalous Structure in Functional Data

Motivated by an example arising from digital networks, we propose a novel approach for detecting the emergence of anomalies in functional data. In contrast to classical functional data approaches, which detect anomalies in completely observed curves, the proposed approach seeks to identify anomalies sequentially as each point on the curve is received. The new method, the Functional Anomaly Sequential Test (FAST), captures the common profile of the curves using Principal Differential Analysis and uses a form of CUSUM test to monitor a new functional observation as it emerges. Various theoretical properties of the procedure are derived. The performance of FAST is then assessed on both simulated and telecommunications data.

Mobile network traffic analysis based on probability-informed machine learning approach

The paper proposes an approach to the joint use of statistical and machine learning (ML) models to solve the problems of the precise reconstruction of historical events, real-time detection of ongoing incidents, and the prediction of future quality of service-related occurrences for prospective development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced. First, the preliminary clustering based on the finite normal mixtures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability-informed ML approach in the field of telecommunications networks. Using the real-world datasets from a Portuguese mobile operator as well as public cellular traffic data, the article compares this approach with methods such as random forests, support vector machine regression, gradient boosting and LSTM. Vector autoregression, informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchmark. We demonstrate that DGMM-based regression is 6.82–22.8 times faster than LSTM for the dataset. Moreover, DGMM-based regression can achieve better results for the most important traffic characteristics (average and total traffic, the number of users). For metrics MAPE and RMSE, it surpasses the results of statistical methods up to 46.7% (RMSE) and 91.5% (MAPE) (median increases are 28.0% and 80.1%, respectively), as well as for ML methods up to 13.0% (RMSE) and 35.7% (MAPE) (median increases are 0.39% and 2.5%, respectively). Thus, the use of a probability-informed approach for telecommunication data seems optimal for the computational speed and accuracy trade-off. Also, we introduce a novel statistical hypothesis testing method based on GG distribution for detecting suspected anomalies in traffic.

Data mining models in telecom churn prediction

Customer attrition has become one of the biggest problems facing the telecommunications industry due to its fast growth. According to telecom studies, acquiring new customers is more expensive than keeping current ones. Telecom companies may use the information gleaned from telecom data to understand the causes behind customer churn and take steps to maintain their current clientele. This study explores the popular data mining methods for spotting trends in loss of clients. Principal component analysis is used in the survey to reduce the dimension of the attributes. Some conclusions about the relationship between costumer usage data their churn can be summarized through the exploratory data analysis. And three prediction techniques (Logistic Regression, SVM Regression, Random Forest Regression) are applied in the customer churn prediction. This paper compares the accuracy and performance of these models. The result shows, among these models, SVM regression has the highest accuracy. It has an accuracy of 0.92 and a precision of 0.48.

TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling

To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. Our extensive analysis validates its potential for transportation. Focusing on vehicle-related GCT flow prediction, we propose a graph neural network that integrates multivariate, temporal, and spatial facets for improved accuracy. Experiments reveal our model's superiority over baselines, especially in long-term predictions. We also highlight the potential for GCT flow integration into transportation systems.

Telecom data analytics: Informed decision-making: A review across Africa and the USA

Telecom data analytics has emerged as a pivotal tool for transforming raw data into actionable insights, empowering telecom operators to make informed decisions and enhance the overall efficiency of their networks. This abstract provides an overview of the comprehensive review that explores the landscape of telecom data analytics in both Africa and the USA. The review delves into the diverse strategies, challenges, and opportunities associated with telecom data analytics in these regions. It examines how advanced analytics techniques, including machine learning and artificial intelligence, are being leveraged to extract valuable insights from vast datasets. The comparative analysis highlights contextual differences in regulatory environments, infrastructure development, and technological landscapes that influence the adoption and implementation of telecom data analytics. In Africa, where the telecom landscape is dynamic and diverse, the review explores how data analytics is playing a crucial role in addressing connectivity challenges, optimizing network performance, and expanding telecommunications services. It also considers the impact of regulatory frameworks and investment climates on the deployment of data analytics solutions. In the USA, a mature telecom market with high technological adoption, the review investigates how data analytics is shaping decision-making processes, improving customer experiences, and contributing to the development of innovative services. It delves into the regulatory landscape, market dynamics, and the role of data analytics in maintaining a competitive edge. Throughout the review, the focus is on identifying best practices, lessons learned, and cross-regional insights that can inform the future trajectory of telecom data analytics. The abstract encapsulates the broader themes of the review, offering a glimpse into the critical role played by data analytics in shaping the telecom industry across Africa and the USA.

Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data

Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom’s retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciates the uncertainty of the data and provides insights into churn modeling. The goal of the study was to evaluate the applicability of the Mamdani and Sugeno models for building a churn model based on a limited but real-world dataset enriched with feature engineering. The additional goal was to find features most usable for churn modeling. Four metrics—accuracy, recall, precision, and F1-score—were used to estimate the performance of the models. The developed fuzzy rule-based systems show that to generalize possible churn identification factors with fuzzy rules, it is advisable to begin with features such as the change in the total amount of the invoice in the last period before the churning compared to the previous one, the total amount of the invoice in the period preceding the churning, the total amount of subscription in two months before the churning, the time of cooperation with the operator, and the number of calls out of the last quarter before leaving.

Fraud Detection and Prevention in Telecommunication Data and Voice Networks

Fraud Detection and Prevention in Telecommunication Data and Voice Networks

Studying Customer Behavior in the Libyan Telecoms Market using Data Visualization

In general, the main benefit of data visualization is that it provides a better understanding of complex data and enables faster decision-making.This study on call detail records (CDRs) and SMS activity data visualization was conducted to fully understand the customer behavior patterns and culture in the Libyan telecom market.A big telecom data (261 GB) was collected for CDRs between 28 March and 22 April 2020. This includes 19 working days, three Fridays and four Saturdays. The data visualization results show that the weekend SMS and call activity pattern shows a completely different behavior compared to other days of the week. Call behavior between 12:00 and 7:00 is generally almost the same. The peak times for calls on weekdays are between 11:30 and 13:00 and for SMS between 19:30 and 22:00. Furthermore, the analysis of SMS activities does not reflect customer behavior, but much of the SMS activity is the result of electronic payment services during shopping. However, Libyan culture is more about making phone calls than sending SMS messages. since the number of calls at dawn is minimal, the number of SMS increased slightly at this time. In order to retain customers, decision makers should consider these results and develop appropriate plans and strategies. However, in order to reduce call congestion and increase efficiency, decision makers should set appropriate pricing during peak or pre-peak and post-peak hours to convert many customers before or after peak hours.Also, they should somehow encourage customers to use SMS instead of calls.

ASIO telecommunications interception and data access powers

Modern mobile phones bear no resemblance to rotary-dial phones of the 1960s. The volume and nature of data extracted from intercepting a ‘telecommunications service’ in today’s online world is fundamentally different to ‘wire-tapping’ a phone conversation. Yet the statutory threshold for ASIO to intercept a telecommunications service has not changed since 1960. There is no statutory requirement to consider privacy or proportionality when issuing ASIO a warrant. The situation is similar for access to telecommunications data (metadata), once just a list of numbers dialled but now a rich source of personal information. This article argues that it is time for the law to change.

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

AbstractWith the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.