Relevant bibliographies by topics / CDR (Call Detail Records)

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Academic literature on the topic 'CDR (Call Detail Records)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "CDR (Call Detail Records)"

1

Zawra, Mohamed A., O. E. Emam, and M. Elemam Shehab. "A Survey of Call Detail Records (CDR) Analysis." International Journal of Computer Applications 185, no. 13 (2023): 27–29. http://dx.doi.org/10.5120/ijca2023922808.

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2

Mishra, Sejal, and Abhinav Shukla. "A Comparative result-based study on Criminal Call Data Record Analysis." RESEARCH REVIEW International Journal of Multidisciplinary 8, no. 5 (2023): 131–45. http://dx.doi.org/10.31305/rrijm.2023.v08.n05.018.

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All calls that travel through a phone exchange are recorded in detail in call detail records or CDRs. The telephone exchange maintains this CDR, which includes the time of the call, the length of the call, the source and destination numbers, the type of the call, etc. Call data logs are crucial for handling serious criminal cases. Processing of Call Detail Records is now moving towards real-time streaming data. It assists in real-time call detail record analysis, real-time criminal location tracking, and real-time network behaviour analysis. However, the number, diversity, and data rate of these Call Detail Records are enormous, and the current telecom systems were not developed with these challenges in mind. The largest source, which can be viewed as Call Detail Records, can be used (for storage, processing, and analysis). The issues that the telecom sector has with call detail records analysis are the subject of extensive research. In this paper we demonstrate how to use Excel, data mining & graph mining to analyse call detail records of criminal case.
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3

Camilovic, Dragana, Dragana Becejski-Vujaklija, and Natasa Gospic. "A call detail records data mart: Data modeling and OLAP analysis." Computer Science and Information Systems 6, no. 2 (2009): 87–110. http://dx.doi.org/10.2298/csis0902087c.

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In order to succeed in the market, telecommunications companies are not competing solely on price. They have to expand their services based on their knowledge of customers' needs gained through the use of call detail records (CDR) and customer demographics. All the data should be stored together in the CDR data mart. The paper covers the topic of its design and development in detail and especially focuses on the conceptual/logical/physical trilogy. Some other design problems are also discussed. An important area is the problem involving time. This is why the implication of time in data warehousing is carefully considered. The CDR data mart provides the platform for Online Analytical Processing (OLAP) analysis. As it is presented in this paper, an OLAP system can help the telecommunications company to get better insight into its customers' behavior and improve its marketing campaigns and pricing strategies.
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4

Aziz, Zagroz, and Robert Bestak. "Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records." Sensors 24, no. 6 (2024): 1716. http://dx.doi.org/10.3390/s24061716.

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The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.
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5

Gibbs, Hamish, Anwar Musah, Omar Seidu, et al. "Call detail record aggregation methodology impacts infectious disease models informed by human mobility." PLOS Computational Biology 19, no. 8 (2023): e1011368. http://dx.doi.org/10.1371/journal.pcbi.1011368.

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This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, “all pairs,” is designed to retain long distance network connections while the other, “sequential” methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
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6

Pinter, Gergo, Amir Mosavi, and Imre Felde. "Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach." Entropy 22, no. 12 (2020): 1421. http://dx.doi.org/10.3390/e22121421.

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Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.
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7

Semassel, Imed Eddine, and Sadok Ben Yahia. "Effective Optimization of Billboard Ads Based on CDR Data Leverage." Journal of Telecommunications and the Digital Economy 10, no. 2 (2022): 76–95. http://dx.doi.org/10.18080/jtde.v10n2.527.

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Call Detail Records (CDRs) provide metadata about phone calls and text message usage. Many studies have shown these CDR data to provide gainful information on people's mobility patterns and relationships with fine-grained aspects, both temporal and spatial elements. This information allows tracking population levels in each country region, individual movements, seasonal locations, population changes, and migration. This paper introduces a method for analyzing and exploiting CDR data to recommend billboard ads. We usher by clustering the locations based on the recorded activities' pattern regarding users' mobility. The key idea is to rate sites by performing a thorough cluster analysis over the achieved data, having no prior ground-truth information, to assess and optimize the ads' placements and timing for more efficiency at the billboards.
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8

Semassel, Imed Eddine, and Sadok Ben Yahia. "MOBILITY EMBEDDING FROM CALL DATA RECORD USING WORD2VEC TO SUPPORT NETWORK WITH UNMANNED AERIAL VEHICLE." Herald of the Kazakh-British technical university 20, no. 1 (2023): 45–53. http://dx.doi.org/10.55452/1998-6688-2023-20-1-45-53.

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Call Detail Records (CDRs) are records that provide information about phone conversations and text messages. CDR data has been proved in several studies to give useful information on people's mobility patterns and associations with fine-grained temporal and geographical characteristics. This paper proposes to embed the traces recorded in the CDRs to extract meaningful information. These latter provide insights about the location that may need support to cover or recover the network. After embedding the users' trajectories step, we use the embedding results to recommend the antennas with coordinates and support demand needed to a fleet of Unmanned Aerial Vehicle. Finally, we ended up with a capacitated vehicle routing problem that we solved using a Google open-source software named OR-Tools.
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9

Zhang, Guangyuan, Xiaoping Rui, Stefan Poslad, Xianfeng Song, Yonglei Fan, and Bang Wu. "A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records." Remote Sensing 12, no. 16 (2020): 2572. http://dx.doi.org/10.3390/rs12162572.

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Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution.
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10

Sultan, Kashif, Hazrat Ali, Adeel Ahmad, and Zhongshan Zhang. "Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization." Information 10, no. 6 (2019): 192. http://dx.doi.org/10.3390/info10060192.

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The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.
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