A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered

CDR-based location analytics & gender prediction from subscribers’ list of installed mobile applications

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsBig Data is big news in most industries, and telecommunication is no exception. Over the last decades, telecom operators experienced numerous changes in their business models, driven by technological innovations. Although, telecom operators have long had access to substantial bits of data, the scenario has radically evolved with the advent of smartphones, mobile broadband, rapid development of internet, growth of mobile services and Big Data Analytics capabilities (BDA). In today’s data intensive world of communications, tremendous amount of diverse type of data are generated by telecom, bringing both challenges and opportunities to the table. This present internship report summarises my contribution part of the Big Data & Advanced Analytics team of Vodafone Portugal with two research projects; The first one consisted in studying human mobility from cellular network-based data, considering the so-called Call Detail Records (CDR) as a core proxy to extract spatiotemporal density distribution at finer geospatial granularity levels. The second consisted in conducting an observational study of the predictability of mobile subscribers’ demographic traits from their installed mobile applications. The latter has the use-case of predicting the gender of mobile subscribers. Both research projects draw attention to the particular ubiquity aspect of connected mobile devices, being widely available and used all over the world.A área de Big Data é uma grande novidade para a maioria das empresas, incluindo as companhias de telecomunicação. Durante as últimas décadas, e graças às inovações tecnológicas, os operadores de telecomunicações viveram muitas mudanças nos seus modelos de atividade comercial. Embora as empresas de telecomunicação já tinham acesso a uma quantidade considerável de dados (bits), o cenário mudou por completo com a chegada dos smartphones, a banda larga, o rápido desenvolvimento de internet, um grande crescimento dos serviços móveis e o Big Data Analytics Capabilities (BDCA). A frenética realidade atual do mundo das comunicações, cria uma grande e diversa quantidade de dados, gerada pelas empresas de telefonia, supondo ao mesmo tempo novos desafios e oportunidades. No seguinte relatório de estágio, resume-se a minha contribuição à equipa de Big Data e Advanced Analytics de Vodafone com dois projetos de investigação: O primeiro projeto consistiu em estudar a mobilidade dos humanos baseando-se nos dados extraídos da rede móvel, considerando o chamado Call Detail Records (CDR) como principal variável para poder obter informação mais detalhada sobre a densidade espácio-temporal em níveis de granularidade. O segundo projeto é um estudo observacional sobre a previsibilidade das características demográficas dos utentes tendo em conta as aplicações instaladas nos seus telemóveis. O caso prático deste último pretende predizer o género dos clientes da rede móvel. Estes dois projetos de investigação pretendem chamar a atenção para a posição onipresente que ocupam os dispositivos móveis ligados à rede na nossa sociedade, estando disponíveis e sendo utilizados no mundo inteiro

Моделювання інвестиційної діяльності підприємства

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. № 311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник проекту: к.е.н., доцент КУЗЬМІНОВА ОльгаМоделювання інвестиційної діяльності підприємства є актуальною та важливою задачею в умовах сучасної ринкової економіки. Зараз бізнессередовище змінюється дуже швидко, виникають нові технології та інноваційні рішення, що потребують великих вкладень. При цьому існує велика кількість ризиків та невизначеностей, які можуть негативно вплинути на фінансові результати підприємства. Тому дуже важливо мати точні та обґрунтовані моделі, які дозволять зменшити ризики та забезпечити оптимальні рішення при інвестуванні

Modeling the Impact of Big Data Analysis Investments on the Dynamics of Customer Acquisition: A Case Study of Telecommunication Sector in the United States

Postponed access: the file will be accessible after 2022-08-14In the age of data explosion, many firms are heavily investing in big data and big data analytics (BDA) without being able to anticipate how much value they will receive. Thus, there is a growing body of research that has been focusing on the impact of big data and BDA investments on firm performance. Nevertheless, most of these studies use self-reported data and none of them has addressed the dynamics in the firm outcomes as well as the continuous feedback processes between BDA investment, firm performance, and other intermediate variables. In this thesis, I collected data about two telecommunication firms in the U.S., namely T-Mobile and Verizon, to build up a system dynamics model that helps to answer two research questions that have not been properly investigated hitherto: 1) How do BDA investments dynamically influence firm performance? and 2) Which policies can help large and small firms to enhance the outcomes of their BDA investments? My simulation results reveal that when the industry develops in favor of BDA activities (i.e., lower data acquisition and data storage costs, more data generated by customers), small firms will be put at a disadvantage. In contrast, large firms with larger customer bases will be able to exploit their economies of scale in BDA investments to quickly increase their market share and gain higher profits. Thus, large firms are advised to increase their investments in BDA and data acquisition, in addition to increase their data volume more quickly even at the cost of lower data quality. As an increase in data volume will typically lead to a decrease in data storage cost, this policy will help large firms effectively increase their total number of customers, which will lead to a further decrease in the data acquisition cost, resulting in higher firm revenues and firm profits. Small firms, instead, are advised to sacrifice their profits for market share. Specifically, they should invest more heavily than large firms to lift the volume of their data up to the point that it can nullify the cost advantage of large firms. It is unclear that, though, whether small firms can survive when making such a big trade-off. Future research might explore whether the intervention from governments might help resolve this inequality between small and large firms.Master's Thesis in System DynamicsGEO-SD351MASV-SYSD

Automated network optimisation using data mining as support for economic decision systems

The evolution from wired voice communications to wireless and cloud computing services has led to the rapid growth of wireless communication companies attempting to meet consumer needs. While these companies have generally been able to achieve quality of service (QoS) high enough to meet most consumer demands, the recent growth in data hungry services in addition to wireless voice communication, has placed significant stress on the infrastructure and begun to translate into increased QoS issues. As a result, wireless providers are finding difficulty to meet demand and dealing with an overwhelming volume of mobile data. Many telecommunication service providers have turned to data analytics techniques to discover hidden insights for fraud detection, customer churn detection and credit risk analysis. However, most are illequipped to prioritise expansion decisions and optimise network faults and costs to ensure customer satisfaction and optimal profitability. The contribution of this thesis in the decision-making process is significant as it initially proposes a network optimisation scheme using data mining algorithms to develop a monitoring framework capable of troubleshooting network faults while optimising costs based on financial evaluations. All the data mining experiments contribute to the development of a super–framework that has been tested using real-data to demonstrate that data mining techniques play a crucial role in the prediction of network optimisation actions. Finally, the insights extracted from the super-framework demonstrate that machine learning mechanisms can draw out promising solutions for network optimisation decisions, customer segmentation, customers churn prediction and also in revenue management. The outputs of the thesis seek to help wireless providers to determine the QoS factors that should be addressed for an efficient network optimisation plan and also presents the academic contribution of this research

Reaping the benefits of big data in telecom

We collect big data use cases for a representative sample of telecom companies worldwide and observe a wide and skewed distribution of big data returns, with a few companies reporting large impact for a long tail of telecom companies with limited returns. Using a joint model of adoption and returns to adoption, we find that the skewness of the distribution arises from a few telecom companies being able to follow key big data managerial and organizational practices. We also find that big data returns exhibit economies of scope, decreasing returns to scale, while big data talents are complementary to big data capex investments.SCOPUS: ar.jinfo:eu-repo/semantics/publishe