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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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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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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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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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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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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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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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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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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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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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Lwin, Ko, Yoshihide Sekimoto, and Wataru Takeuchi. "Estimation of Hourly Link Population and Flow Directions from Mobile CDR." ISPRS International Journal of Geo-Information 7, no. 11 (2018): 449. http://dx.doi.org/10.3390/ijgi7110449.
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The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin–destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.
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Mamei, Marco, Nicola Bicocchi, Marco Lippi, Stefano Mariani, and Franco Zambonelli. "Evaluating Origin–Destination Matrices Obtained from CDR Data." Sensors 19, no. 20 (2019): 4470. http://dx.doi.org/10.3390/s19204470.
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Understanding and correctly modeling urban mobility is a crucial issue for the development of smart cities. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) can naturally support urban and transport studies as well as marketing applications. Individual trips are often aggregated in an origin–destination (OD) matrix counting the number of trips from a given origin to a given destination. In the literature dealing with CDR data there are two main approaches to extract OD matrices from such data: (a) in time-based matrices, the analysis focuses on estimating mobility directly from a sequence of CDRs; (b) in routine-based matrices (OD by purpose) the analysis focuses on routine kind of movements, like home-work commute, derived from a trip generation model. In both cases, the OD matrix measured by CDR counts is scaled to match the actual number of people moving in the area, and projected to the road network to estimate actual flows on the streets. In this paper, we describe prototypical approaches to estimate OD matrices, describe an actual implementation, and present a number of experiments to evaluate the results from multiple perspectives.
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Biba, Marenglen, and Enes Çela. "Avoiding Risk of Disputes by Re-Engineering Telecommunication Services With Blockchain Technologies." International Journal of Risk and Contingency Management 10, no. 4 (2021): 1–13. http://dx.doi.org/10.4018/ijrcm.2021100101.
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Blockchain is a technology used to immutably and transparently store information that has gained wide popularity due to the use with cryptocurrency, but it is suitable for many other business scenarios. In this paper, the authors deal with carriers providing voice services by exchanging calls with each other. These companies need to transparently store call detail records (CDR) in order to avoid billing discrepancies which can lead to disputes and risk of interruption of services with heavy consequences from the legal point of view. In this paper, the authors present a solution to this problem by using hyperledger fabric to develop smart contracts, which are invoked to store information about each CDR generated. The proposed solution initially stores CDRs before inputting these to the blockchain network. The paper presents experiments with thorough testing on the blockchain network and also some performance improvements. Results show the effectiveness of avoiding disputes by guaranteeing that CDRs are exchanged effectively and immutably without room for ambiguities or misinterpretation.
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Xing, Jian, Miao Yu, Shupeng Wang, Yaru Zhang, and Yu Ding. "Automated Fraudulent Phone Call Recognition through Deep Learning." Wireless Communications and Mobile Computing 2020 (August 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8853468.
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Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.
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Chouiekh, Alae, and El Hassane Ibn El Haj. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (2020): 1–16. http://dx.doi.org/10.4018/ijcini.2020010101.
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Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were performed to evaluate the performance of the method. The authors found that deep convolutional neural networks (DCNN) outperformed other traditional machine learning algorithms (support vector machines, random forest, and gradient boosting classifier) with F1 score of 91%. Thus, the use of this approach can reduce the cost related to customer loss and fits better the churn prediction business use case.
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Qin, Siyang, Jie Man, Xuzhao Wang, Can Li, Honghui Dong, and Xinquan Ge. "Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management." Discrete Dynamics in Nature and Society 2019 (January 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/8239047.
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Considering the rapid development of the tourist leisure industry and the surge of tourist quantity, insufficient information regarding tourists has placed tremendous pressure on traffic in scenic areas. In this paper, the author uses the Big Data technology and Call Detail Record (CDR) data with the mobile phone real-time location information to monitor the tourist flow and analyse the travel behaviour of tourists in scenic areas. By collecting CDR data and implementing a modelling analysis of the data to simultaneously reflect the distribution of tourist hot spots in Beijing, tourist locations, tourist origins, tourist movements, resident information, and other data, the results provide big data support for alleviating traffic pressure at tourist attractions and tourist routes in the city and rationally allocating traffic resources. The analysis shows that the big data analysis method based on the CDR data of mobile phones can provide real-time information about tourist behaviours in a timely and effective manner. This information can be applied for the operation management of scenic areas and can provide real-time big data support for “smart tourism”.
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Qurbanov Azad, Qurbanov Azad, and Abışov Azər Abışov Azər. "YÜKSƏK INTENSIVLIKLI TELEKOMMUNIKASIYA CDR QEYDLƏRI ÜÇÜN LSM ƏSASLI SAXLAMA MÜHƏRRIKLƏRINDƏ DÜZGÜN SIXILMA TEXNIKASININ SEÇILMƏSI." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 29, no. 06 (2023): 15–23. http://dx.doi.org/10.36962/pahtei29062023-15.
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Böyük verilənlər dövründə yüksək intensivlikli telekommunikasiya Call Detail Records (CDRs) idarə edilməsi getdikcə çətinləşir. Log-Structured Merge (LSM) ağac əsaslı saxlama mühərrikləri böyük miqyaslı, yazmaq üçün intensiv iş yüklərini idarə etmək üçün məşhur həll yolu kimi ortaya çıxdı. Bu tədqiqat yüksək intensivlikli telekommunikasiya CDR qeydləri üçün performansı artırmaq və resurs istifadəsini optimallaşdırmaq üçün LSM əsaslı saxlama mühərriklərində müxtəlif sıxlaşdırma üsullarının effektivliyini araşdırır. Tapıntılar onu göstərir ki, sıxılma texnikasının diqqətlə seçilməsi LSM əsaslı saxlama mühərriklərinin ümumi performansını xeyli yaxşılaşdıra bilər. Məlumatların sıxlaşdırılması LSM əsaslı saxlama mühərriklərinin əsas komponentidir, çünki o, boş yerin həcmini azaltmaq və sorğu performansını optimallaşdırmaq üçün SSTables-in birləşdirilməsi və sıxılmasına cavabdehdir. Bu məqalədə yüksək intensivlikli telekommunikasiya CDR qeydləri üçün LSM əsaslı saxlama mühərriklərində müxtəlif sıxılma üsullarının performansını qiymətləndirilib. Konkret olaraq, sintetik məlumat generatorundan istifadə etməklə yaradılan 10 milyon CDR qeydinin real iş yükü əsasında leveled sıxlaşdırma, size-tiered sıxlaşdırma və date-tiered sıxlaşdırmanın performansını müqayisə edir və müqayisə edilir. Performansı üç ölçüdən istifadə edərək ölçürük: yazma qabiliyyəti, sorğu gecikmə müddəti və boşluqdan istifadə. Nəticələri göstərir ki, düzgün sıxılma texnikasının seçilməsi xüsusi performans tələblərindən və istifadə halının məhdudiyyətlərindən asılıdır. Size-tiered sıxılma texnikası bəzi sorğu gecikmələrini qurban verərək, ən yüksək yazma qabiliyyətinə və yerdən istifadəyə nail oldu. Date-tiered sıxılma texnikası bəzi yazma qabiliyyətini və boş yerdən istifadəni qurban verərkən ən aşağı sorğu gecikməsinə nail oldu. Leveled sıxlaşdırma texnikası yazma qabiliyyəti, sorğu gecikməsi və boş yerdən istifadə baxımından ən aşağı performansa nail oldu. Bu tədqiqat LSM əsaslı saxlama mühərrikləri və məlumatların sıxlaşdırılması texnikaları sahəsində davam edən tədqiqat və inkişafa töhfə verir və yüksək intensivlikli telekommunikasiya CDR məlumatlarının səmərəli və effektiv saxlanması və axtarışı üçün düzgün sıxlaşdırma texnikasının seçilməsinin vacibliyini vurğulayır. Açar sözlər: LSM əsaslı saxlama mühərrikləri, Sıxlaşdırma üsulları, Telekommunikasiya CDR qeydləri, Məlumat həcmi, Yazma sürəti, STCS, LCS, TWCS, Performansın optimallaşdırılması, Resurs istifadəsi.
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Pintér, Gergő, and Imre Felde. "Evaluating the Effect of the Financial Status to the Mobility Customs." ISPRS International Journal of Geo-Information 10, no. 5 (2021): 328. http://dx.doi.org/10.3390/ijgi10050328.
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In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.
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Leng, Yan, Alejandro Noriega, and Alex Pentland. "Tourism Event Analytics with Mobile Phone Data." ACM/IMS Transactions on Data Science 2, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3479975.
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Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a low granularity. Anonymous call detail record (CDR) is a novel source of data with enormous potential in areas of high societal value: epidemics, poverty, and urban development. This study demonstrates the added value of CDR in event tourism, especially for the analysis and evaluation of marketing strategies, event operations, and the externalities at the local and national levels. To achieve this aim, we formalize 14 indicators in high spatial and temporal resolutions to measure both the positive and the negative impacts of the touristic events. We exemplify the use of these indicators in a tourism country, Andorra, on 22 high-impact events including sports competitions, cultural performances, and music festivals. We analyze these touristic events using the large-scale CDR data across 2 years. Our approach serves as a prescriptive and a diagnostic tool with mobile phone data and opens up future directions for tourism analytics.
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Bonnetain, Loïc, Angelo Furno, Jean Krug, and Nour-Eddin El Faouzi. "Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (2019): 74–88. http://dx.doi.org/10.1177/0361198119847472.
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Mobile phone data collected by network operators can provide fundamental insights into individual and aggregate mobility of people, at unprecedented spatiotemporal scales. However, traditional call detail records (CDR) have fundamental issues because of low accuracy in both spatial and temporal dimensions, which limits their applicability for detailed studies on mobility, especially in urban scenarios. This paper focuses on a new generation of mobile phone passive data, individual cellular network signaling data, characterized by higher spatiotemporal resolutions than traditional CDR. A framework based on unsupervised hidden Markov model is designed for map-matching such data on a multimodal transportation network, aimed at accurately inferring the complex multimodal travel itineraries and popular paths people follow in their urban daily mobility. This information, especially if computed at large spatiotemporal scales, can represent a solid basis for studying actual and dynamic travel demand, to properly dimension multimodal transport systems and even perform anomaly detection and adaptive network control. The approach is evaluated in a case study based on real cellular traces collected by a major French operator in the city of Lyon, and a validation study at both microscopic and macroscopic levels proposed. The results show that this approach can properly handle sparse and noisy cell phone trajectories in complex urban environments. Moreover, the results are promising concerning popular paths detection and reconstruction of origin–destination matrices.
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Novović, Olivera, Sanja Brdar, Minučer Mesaroš, Vladimir Crnojević, and Apostolos N. Papadopoulos. "Uncovering the Relationship between Human Connectivity Dynamics and Land Use." ISPRS International Journal of Geo-Information 9, no. 3 (2020): 140. http://dx.doi.org/10.3390/ijgi9030140.
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CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.
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Sowkhya, B., S. Amaduzzi, and D. Raawal. "VISUALIZATION AND ANALYSIS OF CELLULAR & TWITTER DATA USING QGIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 199–209. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-199-2018.
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<p><strong>Abstract.</strong> The study is to understand individual presence and movement in Friuli Venezia Giulia region. It is important for tourism planning, hazard management, business marketing, implementing government lifetime policies and benefit. The aim of this study is achieved by advanced web<span class="thinspace"></span>2.0 applications. We need real time and geo-located data to monitor the inflow of tourist and to come up with effective promoting and benefiting plans for tourism, the evacuation and mitigation strategies during hazards to protect social life and environment with less infrastructure damage, marketing plans for advertising or selling of products. Despite wide spread success in predicting specific aspects of human behavior by social media information, a little attention is given to twitter and cell phone data. Accessibility to detailed human movements with fine spatial and temporal granularity is challenging due to confidentiality and safety reasons. With rapid development of web<span class="thinspace"></span>2.0 applications people can post about events, share opinion and emotions online. Using twitter data, how short term travelers, such as tourists, can be recognized and how their travel pattern can be analyzed. Study of finding tourist dynamics such as arriving and outgoing of tourist, sum of trips, sum of days and night spent, number of unique visitors, country of residence, main destination, secondary destination, transits pass through, repeat visits are achieved using CDR (call detail records) and DDR (data detail records).</p>
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Perrotta, Daniela, Enrique Frias-Martinez, Ana Pastore y Piontti, et al. "Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia." PLOS Neglected Tropical Diseases 16, no. 7 (2022): e0010565. http://dx.doi.org/10.1371/journal.pntd.0010565.
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Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.
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Sana, Bhargava, Joe Castiglione, Drew Cooper, and Dan Tischler. "Using Google’s Passive Data and Machine Learning for Origin-Destination Demand Estimation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 46 (2018): 73–82. http://dx.doi.org/10.1177/0361198118798298.
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Origin-destination (OD) data collection methods are steadily attempting to move from conventional survey techniques (roadside interview, license plate, etc.) toward using passively collected big data sources such as those based on global positioning system (GPS) and cell phone call detail records (CDR). In this study, a new passive data source, Google’s Aggregated and Anonymized Trips (AAT), was used to derive hourly OD demand matrices for the San Francisco Bay Area. Since the AAT dataset contains relative flows or weights as opposed to absolute trips, machine learning techniques were applied to convert them with the help of observed OD flows from expanded household travel survey. Several machine learning models were trained to perform quite well for both training and test data. However, it was found that the multi-layer perceptron (MLP), a neural networks approach, resulted in the best performing model for the conversion. Additionally, all models were used for predictions in a hypothetical application context where input AAT data were scaled by different growth factors. This exercise showed that, even though the trip predictions of all models were close to each other initially, they varied widely for different magnitudes of OD markets and growth factors.
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Hu, Xinxin, Haotian Chen, Hongchang Chen, Xing Li, Junjie Zhang, and Shuxin Liu. "Mining Mobile Network Fraudsters with Augmented Graph Neural Networks." Entropy 25, no. 1 (2023): 150. http://dx.doi.org/10.3390/e25010150.
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With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.
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G V, Ashok, and Vasanthi Kumari P. "A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 593–603. http://dx.doi.org/10.17762/ijritcc.v11i7s.7147.
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Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system.
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Watson, James R., Zach Gelbaum, Mathew Titus, Grant Zoch, and David Wrathall. "Identifying multiscale spatio-temporal patterns in human mobility using manifold learning." PeerJ Computer Science 6 (June 15, 2020): e276. http://dx.doi.org/10.7717/peerj-cs.276.
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When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.
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Sandhya B S. "Detection and analysis of cellular network traffic anomalies and SMS spammers." Journal of Electrical Systems 20, no. 3 (2024): 685–700. http://dx.doi.org/10.52783/jes.2994.
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The unfolding usage of mobile users inclusive of both 4G and 5G creates huge accumulation of data in cellular network. The network service providers need to ensure proper management of resource in terms of uninterrupted service with cost-effectiveness. The detection of cellular traffic and Short Message Service (SMS) spammers is very challenging. In this paper a novel method is proposed to analyse and detect the traffic anomalies and SMS spammers. To achieve this, Call Detail Record (CDR) issued by service provider is used. The CDR is pre-processed to convert into machine understandable format using mean-normalization technique. K-means clustering elbow method proves to be the best tool in identifying the traffic clusters in the network that detects both high and low traffic in the network. The novelty of the proposed work is the detection of low traffic clusters which usually is misled as sleeping cell or cell outage. The paper also presents a model designed to predict whether the message is spam SMS or ham SMS. The proposed model is suitable to run different classifiers like Logistic Regression, Multi nominal Naive Bayes, Support Vector Machine (SVM), Random Forest Classifier. The model gives the highest accuracy rate of 98.277% with SVM in detecting SMS spam.
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Ayling, Sophie, Sveta Milusheva, Faith Maidei Kashangura, Yi Rong Hoo, Hugh Sturrock, and George Joseph. "A stitch in time: The importance of water and sanitation services (WSS) infrastructure maintenance for cholera risk. A geospatial analysis in Harare, Zimbabwe." PLOS Neglected Tropical Diseases 17, no. 6 (2023): e0011353. http://dx.doi.org/10.1371/journal.pntd.0011353.
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Understanding the factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. Using a rich set of georeferenced case data from the cholera epidemic that occurred in Harare from September 2018 to January 2019, we apply spatio-temporal modelling to better understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. Using Call Detail Records (CDR) to estimate weekly population movement of the community throughout the city, results suggest that broader human movement (not limited to infected agents) helps to explain some of the spatio-temporal patterns of cases observed. In addition, results highlight a number of socio-demographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure. The analysis shows that populations living close to the sewer network, with high access to piped water are associated with at higher risk. One possible explanation for this observation is that sewer bursts led to the contamination of the piped water network. This could have turned access to piped water, usually assumed to be associated with reduced cholera risk, into a risk factor itself. Such events highlight the importance of maintenance in the provision of SDG improved water and sanitation infrastructure.
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Witayangkurn, Apichon, Ayumi Arai, and Ryosuke Shibasaki. "Development of Big Data-Analysis Pipeline for Mobile Phone Data with Mobipack and Spatial Enhancement." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 196. http://dx.doi.org/10.3390/ijgi11030196.
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Frequent and granular population data are essential for decision making. Further-more, for progress monitoring towards achieving the sustainable development goals (SDGs), data availability at global scales as well as at different disaggregated levels is required. The high population coverage of mobile cellular signals has been accelerating the generation of large-scale spatiotemporal data such as call detail record (CDR) data. This has enabled resource-scarce countries to collect digital footprints at scales and resolutions that would otherwise be impossible to achieve solely through traditional surveys. However, using such data requires multiple processes, algorithms, and considerable effort. This paper proposes a big data-analysis pipeline built exclusively on an open-source framework with our spatial enhancement library and a proposed open-source mobility analysis package called Mobipack. Mobipack consists of useful modules for mobility analysis, including data anonymization, origin–destination extraction, trip extraction, zone analysis, route interpolation, and a set of mobility indicators. Several implemented use cases are presented to demonstrate the advantages and usefulness of the proposed system. In addition, we explain how a large-scale data platform that requires efficient resource allocation can be con-structed for managing data as well as how it can be used and maintained in a sustainable manner. The platform can further help to enhance the capacity of CDR data analysis, which usually requires a specific skill set and is time-consuming to implement from scratch. The proposed system is suited for baseline processing and the effective handling of CDR data; thus, it allows for improved support and on-time preparation.
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Yang, Jianxi, Manoranjan Dash, and Sin G. Teo. "PPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 224. http://dx.doi.org/10.3390/ijgi10040224.
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As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.
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Ranjan, Preetish, Vrijendra Singh, Prabhat Kumar, and Satya Prakash. "Models for the Detection of Malicious Intent People in Society." International Journal of Digital Crime and Forensics 10, no. 3 (2018): 15–26. http://dx.doi.org/10.4018/ijdcf.2018070102.
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This article describes how in less than two decades, internet in mobile phones has grown from a curiosity to an essential element of modern life. Although, this mind-boggling growth has no doubt facilitated international commerce, trade, and travel, it is also being used in the planning and coordination of criminal activities. These types of attacks are often referred to as socio-technical attacks. These attacks are targeted at these sensitive points to society or national security and may have a devastating impact. Often, organized, sponsored, and trained groups are involved to disguise the intelligence system, deployed for the detection of such attacks. Prior detection of such attacks may reduce its impact. In this article, the authors have developed an efficient model to detect malicious node in huge and complex corpus of data associated with call detail record (CDR). This model analyses CDRs to identify covert nodes operating within society for malicious intent.
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DEUSSOM DJOMADJI, Eric Michel, Bequerelle MATEMTSAP MBOU, Aurelle TCHAGNA KOUANOU, Michael EKONDE SONE, and Parfait BAYONBOG. "Machine learning-based approach for designing and implementing a collaborative fraud detection model through CDR and traffic analysis." Transactions on Machine Learning and Artificial Intelligence 10, no. 4 (2022): 46–58. http://dx.doi.org/10.14738/tmlai.104.12854.
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Fraud in telecommunications networks is a constantly growing phenomenon that causes enormous financial losses for both the individual user and the telecommunications operators. We can denote many researchers who have proposed various approaches to provide a solution to this problem, but still need to be improve to ensure the efficiency. Detecting fraud is difficult and, it's no surprise that many frauds schemes have serious limitations. Different types of fraud may require different systems, each with different procedures, parameter adjustments, database interfaces, and case management tools and capabilities. This article uses the K-Means algorithm to handle fraud detection based on Call Detail Record (CDR) and traffic analysis in a telecommunication industry. Our algorithm consists to compare traffic and CDR generated in the network and check if there is abnormal behavior and if yes, our model is used to confirm if users suspecting of fraud are really fraudster or not. To build our model we used real word CDR data collected in November 2021. Our model associates the Differential Privacy model to encrypt users' personal information, and the k-means algorithm to group users into different clusters. Those clusters represent non fraud users having similar characteristics based on criteria used to build the model. Users having abnormal behavior that can be assimilated to fraudsters are those who are far from the different clusters center. Thanks to a representation in a plan, we better visualize user’s behavior. We validated our model by evaluating our segmentation method. The interpretation of the results shows sufficiently that our approach allows to obtain better results. Our approach can be used by all telecommunications operator to reduce the impact of fraud on internet services.
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Pintér, Gergo, and Imre Felde. "Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data." Information 13, no. 3 (2022): 114. http://dx.doi.org/10.3390/info13030114.
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In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.
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Cottineau, Clémentine, and Maarten Vanhoof. "Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities." ISPRS International Journal of Geo-Information 8, no. 1 (2019): 19. http://dx.doi.org/10.3390/ijgi8010019.
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Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions.
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Gaál, Péter, Tamás Joó, Tamás Palicz, Péter Pollner, István Schiszler, and Miklós Szócska. "Adattudományi innováció az egészségügy környezeti kihívásainak kezelésében: a nagy adatállományok hasznosításának jelentősége és lehetőségei a járványkezelésben." Scientia et Securitas 2, no. 1 (2021): 2–11. http://dx.doi.org/10.1556/112.2021.00014.
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Összefoglaló. A COVID-19 járvány rámutatott arra, hogy az egészségügy kiemelt nemzetbiztonsági terület. Az egészségbiztonsági kockázati tényezőkkel szemben ellenálló egészségügyi rendszerek adattudományi innováció nélkül nem képzelhetők el. A közlemény két esettanulmányon keresztül mutatja be, hogy a nagy, működés során generálódó adatbázisok elemzése hogyan segítheti a járványokkal szembeni védekezést. A mobilcella információk elemzése a leghatékonyabb eszköz a tömeges népességmozgások nyomon követésére, így a vesztegzár intézkedések hatásának döntéstámogatási célú vizsgálatára, az oltásellenes közösségimédia-aktivitás hálózatelemzése pedig segíti az immunizációs kampányok tervezését és megvalósítását. Tanulmányunkban amellett érvelünk, hogy az egészségügy információ- és kommunikációtechnológia fejlődésére építő digitalizációja a kulcsa egy környezeti változásokkal megbirkózni képes egészségügy kialakításának. Summary. The COVID-19 pandemic has shown that health and health care should be considered a top priority area of national security. Health security risks can only be addressed with resilient health systems, which are not possible to be established without innovation in health data science. This publication introduces two examples to illustrate this point, both in the field of the management of epidemics. The first case provides a summary of our previous publication about how mobile phone Call Detail Records can be used to trace population movement to evaluate the effectiveness of movement restriction measures, such as the lock down, which was implemented in Hungary during the first phase of the COVID-19 pandemic. Our analysis shows that the collation and processing of Call Detail Records is an effective and inexpensive method to monitor mass population movement, and complements well the GPS-based smartphone method, which is more suitable for contact tracing and controlling of home quarantine of individuals. Our CDR-based method could be used by other countries, as well as to monitor movement between countries at the European level or internationally, with minimal adaptation effort. The second case introduces a study to gain insight into and better understanding of the potential impact of antivaccination social media activism on the Human Papilloma Virus vaccination campaign in Hungary in 2014. The network analysis of Facebook antivaccination posts and comments showed that during this period, the activists in this network were unable to reach a wider population and were not able to disturb the implementation of the expansion of the well functioning Hungarian public vaccination programme. Unfortunately, this is not the case regarding the COVID-19 vaccination campaign in progress, which suggests that the antivaccination activism is a real and serious security threat to be dealt with. In conclusion, we argue in this paper that the digital transformation of health care, based on the explosive development of information and communication technologies, is of key importance to the establishment of resilient health systems, which are able to cope efficiently with the challenges posed by the rapid environmental changes generated by societal transformation of the 21st century.
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Zhang, Guangyuan, Stefan Poslad, Xiaoping Rui, et al. "Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China." Sensors 21, no. 16 (2021): 5569. http://dx.doi.org/10.3390/s21165569.
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This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people’s activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples’ density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people’s desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens’ activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens’ activities of daily living.
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Aditya, Yudha, Adian Fatchur Rochim, and Eko Didik Widianto. "Rancang Bangun Sistem Telekomunikasi Konvergen Berbasis Voice over Internet Protocol Menggunakan Virtualbox." Jurnal Teknologi dan Sistem Komputer 3, no. 2 (2015): 282. http://dx.doi.org/10.14710/jtsiskom.3.2.2015.282-294.
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Perkembangan teknologi yang sangat pesat, membuat teknologi telekomunikasi semakin berkembang. Voice over Internet Protocol (VoIP), Public Switched Telephone Network (PSTN), Global System for Mobiles (GSM) dan internet adalah teknologi terkini dalam memenuhi kebutuhan seseorang dalam berkomunikasi. Di sebagian besar implementasinya, penyedia layanan PSTN dan GSM memberikan sebuah tarif dalam setiap panggilan yang terjadi. Perancangan dan pembangunan sistem ini bertujuan untuk menciptakan sebuah sistem telekomunikasi berbasis VoIP, yang dapat menghubungkan jaringan lokal, GSM dan internet secara terpusat, demi memenuhi kebutuhan komunikasi seseorang dengan mobilitas tinggi disertai fleksibilitas pengaturan alur panggilan, untuk menghemat anggaran penggunaan layanan telekomunikasi. Metodologi penelitian tugas akhir ini dibagi menjadi 4 tahapan. Tahapan tersebut diantaranya adalah definisi sistem, spesifikasi kebutuhan, konfigurasi sistem dan pengujian sistem. Definisi sistem dibuat dengan mendefinisikan gambaran dan cara kerja sistem secara umum. Spesifikasi kebutuhan dibuat dengan menyesuaikan kebutuhan perangkat keras dan perangkat lunak yang dibutuhkan oleh sistem. Konfigurasi dilakukan untuk mengimplementasikan pengaturan mengenai dialplan, Interactive Voice Response (IVR) dan kotak suara. Pengujian sistem adalah tahap untuk memeriksa keseluruhan fungsi pada sistem. Sistem ini diuji dengan melakukan panggilan dari setiap klien. Hasil dari pengujian menunjukkan bahwa sistem mampu memenuhi kebutuhan komunikasi seseorang dengan mobilitas tinggi. Fleksibilitas pengaturan panggilan membuat sistem dapat berkomunikasi dengan jaringan GSM dan VoIP Rakyat serta dapat menghemat tarif penggunaan layanan telekomunikasi. Sistem juga dapat mencatat aktivitas panggilan dengan memanfaatkan fitur Call Detail Record (CDR). Penelitian ini dapat dijadikan sebagai alternatif bagi perkantoran maupun instansi, untuk menggunakan layanan telekomunikasi secara terpusat, agar penghematan anggaran dalam penggunaan telekomunikasi menjadi lebih efisien.
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Kalila, Adham, Zeyad Awwad, Riccardo Di Clemente, and Marta C. González. "Big Data Fusion to Estimate Urban Fuel Consumption: A Case Study of Riyadh." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 24 (2018): 49–59. http://dx.doi.org/10.1177/0361198118798461.
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Falling oil revenues and rapid urbanization are putting a strain on the budgets of oil-producing nations, which often subsidize domestic fuel consumption. A direct way to decrease the impact of subsidies is to reduce fuel consumption by reducing congestion and car trips. As fuel consumption models have started to incorporate data sources from ubiquitous sensing devices, the opportunity is to develop comprehensive models at urban scale leveraging sources such as Global Positioning System (GPS) data and Call Detail Records. This paper combines these big data sets in a novel method to model fuel consumption within a city and estimate how it may change in different scenarios. To do so a fuel consumption model was calibrated for use on any car fleet fuel economy distribution and applied in Riyadh, Saudi Arabia. The model proposed, based on speed profiles, was then used to test the effects on fuel consumption of reducing flow, both randomly and by targeting the most fuel-inefficient trips in the city. The estimates considerably improve baseline methods based on average speeds, showing the benefits of the information added by the GPS data fusion. The presented method can be adapted to also measure emissions. The results constitute a clear application of data analysis tools to help decision makers compare policies aimed at achieving economic and environmental goals.
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Egedy, Tamás, and Bence Ságvári. "Urban geographical patterns of the relationship between mobile communication, social networks and economic development – the case of Hungary." Hungarian Geographical Bulletin 70, no. 2 (2021): 129–48. http://dx.doi.org/10.15201/hungeobull.70.2.3.
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In the post-industrial age, the transformation of urban networks and urban regions was fundamentally influenced by the rapid spread of infocommunication technologies (ICT) and the Internet. People share information in their daily lives with the help of various ICT devices and ultimately generate georeferenced data that could obtain important information about people’s use of space, spatial movement and social connections. The main aim of the study is to explore the urban geographical and spatial impacts of ICT and social media networks in Hungarian cities. We focus on drawing territorial and settlement hierarchical patterns and clusters based on the mobile communication and online social network relationship data of Hungarian cities. The paper highlights the relationship between the intensity of mobile communication and the density and expansion of intercity social relations and the settlements’ level of economic development, respectively. The methodology is based on mobile phone call detail record (CDR) analysis and intercity network analysis of social media activities. Our findings suggest that different communication networks follow divergent spatial patterns in Hungary. The traditional East–West dichotomy of the Hungarian spatial divide is still reflected in mobile communication, but intercity clusters based on social media activities are usually aligned to the borders of administrative structures. In several cases, we were able to identify strong intercity links between settlements with a similar level of economic development of the mesolevel spatial structure that traverses over different counties and regional borders. Results on social and demographic issues suggest that ‘generation Z’ could play a key role in dampening the social and economic tensions created by the digital divide in the long run. Using a multidimensional explanatory model, we could demonstrate the growing interconnectedness between digital networks and economic development.
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Xie, Xiaoyang, Zhiqing Hong, Zhou Qin, Zhihan Fang, Yuan Tian, and Desheng Zhang. "TransRisk." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (2022): 1–19. http://dx.doi.org/10.1145/3534581.
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Human mobility data may lead to privacy concerns because a resident can be re-identified from these data by malicious attacks even with anonymized user IDs. For an urban service collecting mobility data, an efficient privacy risk assessment is essential for the privacy protection of its users. The existing methods enable efficient privacy risk assessments for service operators to fast adjust the quality of sensing data to lower privacy risk by using prediction models. However, for these prediction models, most of them require massive training data, which has to be collected and stored first. Such a large-scale long-term training data collection contradicts the purpose of privacy risk prediction for new urban services, which is to ensure that the quality of high-risk human mobility data is adjusted to low privacy risk within a short time. To solve this problem, we present a privacy risk prediction model based on transfer learning, i.e., TransRisk, to predict the privacy risk for a new target urban service through (1) small-scale short-term data of its own, and (2) the knowledge learned from data from other existing urban services. We envision the application of TransRisk on the traffic camera surveillance system and evaluate it with real-world mobility datasets already collected in a Chinese city, Shenzhen, including four source datasets, i.e., (i) one call detail record dataset (CDR) with 1.2 million users; (ii) one cellphone connection data dataset (CONN) with 1.2 million users; (iii) a vehicular GPS dataset (Vehicles) with 10 thousand vehicles; (iv) an electronic toll collection transaction dataset (ETC) with 156 thousand users, and a target dataset, i.e., a camera dataset (Camera) with 248 cameras. The results show that our model outperforms the state-of-the-art methods in terms of RMSE and MAE. Our work also provides valuable insights and implications on mobility data privacy risk assessment for both current and future large-scale services.
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Goel, Rahul, Rajesh Sharma, and Anto Aasa. "Understanding gender segregation through Call Data Records: An Estonian case study." PLOS ONE 16, no. 3 (2021): e0248212. http://dx.doi.org/10.1371/journal.pone.0248212.
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Understanding segregation plays a significant role in determining the development pathways of a country as it can help governmental and other concerned agencies to prepare better-targeted policies for the needed groups. However, inferring segregation through alternative data, apart from governmental surveys remains limited due to the non-availability of representative datasets. In this work, we utilize Call Data Records (CDR) provided by one of Estonia’s major telecom operators to research the complexities of social interaction and human behavior in order to explain gender segregation. We analyze the CDR with two objectives. First, we study gender segregation by exploring the social network interactions of the CDR. We find that the males are tightly linked which allows information to spread faster among males compared to females. Second, we perform the micro-analysis using various users’ characteristics such as age, language, and location. Our findings show that the prime working-age population (i.e., (24,54] years) is more segregated than others. We also find that the Estonian-speaking population (both males and females) are more likely to interact with other Estonian-speaking individuals of the same gender. Further to ensure the quality of this dataset, we compare the CDR data features with publicly available Estonian census datasets. We observe that the CDR dataset is indeed a good representative of the Estonian population, which indicates that the findings of this study reasonably reflect the reality of gender segregation in the Estonian Landscape.
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Natarajan, R. "Implementing Artificial Intelligence in CDR & Links Failure in Telecom Technology." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3309–12. http://dx.doi.org/10.22214/ijraset.2021.37127.
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This Paper is about implementing Machine Language Technology in Important day to day operations of Telecom Industry. CDR (Call Details Record) is one of the Primary Operations of Telecom service Provider for Charging Monthly Expenses to the Subscribers.Implementing AI in Telecom Links Failure is another Agenda of this Paper.
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Vancea, Florin, Codruţa Vancea, Daniela Elena Popescu, Doina Zmaranda, and Gianina Gabor. "Secure Data Retention of Call Detail Records." International Journal of Computers Communications & Control 5, no. 5 (2010): 961. http://dx.doi.org/10.15837/ijccc.2010.5.2260.
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In today’s world communication is relying heavily on electronic means, both for voice and other native data. All these communication sessions leave behind journaling information by the very nature of the underlying services. This information is both sensitive with respect to user’s rights and important for law enforcement purposes, so proper storage and retrieval techniques have to be taken into consideration. The paper discusses such techniques in relation with recent EU recommendations and suggests some methods for achieving good performance while preserving the required security levels.
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Botta, Federico. "Quantifying the differences in call detail records." Royal Society Open Science 8, no. 6 (2021): 201443. http://dx.doi.org/10.1098/rsos.201443.
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The increasing availability of mobile phone data has attracted the attention of several researchers interested in studying our collective behaviour. Our interactions with the phone network can take several forms, from SMS messages to phone calls and data usage. Typically, mobile phone data are released to researchers in the form of call detail records , which contain records of different types of interactions, and can be used to analyse various aspects of our behaviour. However, the inherently behavioural nature of these interactions may result in differences between how we make phone calls and receive text messages. Studies which rely on data derived from these interactions, therefore, need to carefully consider these differences. Here, we aim to investigate differences and limitations of different types of mobile phone interactions data by analysing a large mobile phone dataset. We study the relationship between different types of interactions and show how it changes over time. We anticipate our findings to be of interest to all researchers working in the area of computational social science.
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Gao, Zhi Heng, Kang Chen, and Ling Yan Bi. "Study of CDR Real-Time Query Based on Big Data Technologies." Applied Mechanics and Materials 462-463 (November 2013): 845–48. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.845.
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This paper describes big data technology layers, analyses the CDR (Call Data Records) real-time query scenario of telecommunications and brings forward a fast indexing and query solution based on the open source Hadoop platform. A CDR real-time query system was built according to the solution. A performance test was conducted with the real dataset of a city with 3 million subscribers. Compared with the existing system, the big data solution can greatly improve data processing performance and support real-time query with lower hardware and software investment.
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Georges, Olle Olle, Shu Qin Cai, and Qian Yuan. "A New Framework for Churners’ Influence Analysis Using Call Data Records." Advanced Materials Research 989-994 (July 2014): 4200–4204. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4200.
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Predicting customer churn is of paramount importance in telecommunication companies. The taxonomy of churn reports that, not only individual constraints but also social factors can create users’ propensity to churn. This study uses real world call data records (CDR) to extract the social relationships among mobile phone users and build a multi relational social network, where the influence of users diffuses. The research is conducted to propose a framework that enhances the actionable value of social influence of predicted churners and to examine the parameters that control the churn information diffusion in the telecommunication networks.
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von Mörner, Moritz. "Application of Call Detail Records - Chances and Obstacles." Transportation Research Procedia 25 (2017): 2233–41. http://dx.doi.org/10.1016/j.trpro.2017.05.429.
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Chen, Guangshuo, Sahar Hoteit, Aline Carneiro Viana, Marco Fiore, and Carlos Sarraute. "Enriching sparse mobility information in Call Detail Records." Computer Communications 122 (June 2018): 44–58. http://dx.doi.org/10.1016/j.comcom.2018.03.012.
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Ranjan, Gyan, Hui Zang, Zhi-Li Zhang, and Jean Bolot. "Are call detail records biased for sampling human mobility?" ACM SIGMOBILE Mobile Computing and Communications Review 16, no. 3 (2012): 33–44. http://dx.doi.org/10.1145/2412096.2412101.
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