BUILDING THE PROFILE OF THE SUBSCRIBER OF MOBILE NETWORKS BASED ON ONTOLOGICAL APPROACH

Цель. В связи с увеличением числа абонентов мобильных сетей, используемых абонентами устройств, а также высокой активностью абонентов агрегируемая об атрибутах абонентов информация необходима для выстраивания рекомендательных функций систем телекоммуникационных компаний, проведения маркетинговых инициатив, повышения качества оказываемых услуг, при прогнозировании потребностей и желаний клиентов, а также для многих других функций. Статья посвящена рассмотрение методов, направленных на формализацию предметной области при построении профилей абонентов мобильной связи.Методы. В работе рассматриваются метод формальных понятий, а также модель представления информации на концептуальном уровне в контексте представления знаний об абонентах мобильной связи.Результаты. На основе изучения методов структурирования знаний о предметной области авторами предлагается модель представления качественной и количественной информации об объекте исследования с использованием онтологического подхода.Purpose. In connection with the increase in the number of mobile network subscribers used by device users, as well as the high activity of subscribers, information aggregated about the attributes of subscribers is necessary for building advisory functions of telecommunications companies’ systems, conducting marketing initiatives, improving the quality of services provided, predicting the needs and desires of customers, and for many other functions. The article is devoted to the consideration of methods aimed at formalization of the subject domain in the construction of profiles of mobile communication subscribers.Methods. The paper considers the method of formal concepts, as well as the model of information representation at the conceptual level in the context of knowledge representation about mobile communication subscribers.Results. On the basis of studying the methods of structuring knowledge of the subject domain, the authors propose a model for presenting qualitative and quantitative information about the object of research using the ontological approach

A rule-based method for discovering trajectory profiles

The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people’s profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery.The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people's profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery

Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains

Big Data e grandi eventi: una prima analisi della "Barcolana" 2016

In recent years, the use of big data has become more and more pervasive in particular for social phenomena analysis. Among these the presences and movements data derived from mobile phone network are of great importance. Using these data and geomatics a first analysis on the 2016 edition of the Barcolana, the most crowded sailing regatta in the world, was carried out trying to estimate the presence of foreigners and Italian people, and to derive other information regarding those days in the municipality of Trieste and its surroundings

Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework

© 2017 by the authors. This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data

T-profiles: a method for inferring socio-demographic profiles from trajectories

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Ter o conhecimento sobre o perfil dos habitantes de uma cidade ou país tem grande valor para administrações públicas e empresas. Conhecer o perfil de uma população pode auxiliar o trabalho de planejadores urbanos, administradores de transporte público, serviços governamentais ou empresas de diferentes maneiras como, por exemplo, decidir onde é interessante instalar uma nova loja ou personalizar anúncios para um determinado público. A forma mais comum utilizada na análise de informações demográficas de uma população é através da segmentação da mesma em perfis sócio-demográficos, como idade, ocupação, estado civil ou renda mensal. Atualmente, para que essas informações sejam descobertas e analisadas, os dados são coletados através de entrevistas realizadas de casa em casa, periodicamente, em diversos países. No entanto, este tipo de abordagem possui algumas desvantagens: 1) os dados não são atualizados e precisos, pois são coletados em um intervalo de 5 - 10 anos; 2) a coleta é muito custosa e cobre apenas uma parcela da população por um curto período de tempo, apesar de ser estatisticamente significante; 3) não caracteriza as atividades completas do indivíduo, apenas o período de 1 dia de atividades, fornecidas através da entrevista realizada. Atualmente, é possível inferir muito conhecimento a partir do comportamento das pessoas analisando seu movimento do dia-a-dia, uma vez que grandes quantidades de dados de movimento estão disponíveis como: dados de telefone celular, redes sociais, dados de GPS, etc. Nesta dissertação, é proposto um método para a extração de perfis sócio-demográficos a partir de trajetórias de objetos móveis, e apresenta as seguintes contribuições: (i) proposta de um modelo de perfil geral para representar o perfil sócio-demográfico de pessoas, como trabalhador, estudante, desempregado, etc; (ii) proposta de um modelo para representar o histórico de movimentação diária dos indivíduos; (iii) proposta de funções de similaridade para fazer o casamento entre histórico e modelo de perfil e; (iv) um algoritmo chamado T-Profiles que realiza a comparação entre modelo de perfil e modelo de histórico, com o intuito de inferir o perfil sócio-demográfico de um objeto móvel a partir de sua trajetória. O algoritmo T-Profiles é validado utilizando dados reais de trajetórias, obtendo em torno de 90% de precisão.Abstract : The knowledge about people living in a city or country has great value for the public administration as well as for enterprises. To know the population profile may help the job of smart city planners, public transportation administrators, government services or companies in many different ways, such as to decide if and where to install a new store or to personalize an advertisement, for example. The usual approach for population demographic analysis is to segment the population in socio-demographic profiles, such as age, occupation, marital status or income. Most attempts to discover and measure the population profiles is through human surveys, and the most well-known example is the socio-demographic census with diary activities, done periodically in many countries. However, the main drawbacks of the census data is that they: 1) are not up to date since they are usually collected every 5 - 10 years; 2) are expensive to collect, and cover only a small - although statistically significant - part of the population for a short period of time; 3) do not collect the actual movement of the individuals, but only the activity performed during one day and which is mentioned by the user during the interview. We believe that nowadays we can infer much knowledge and the real behavior about people from their every day movement. In this thesis we propose a method to extract socio-demographic profiles from trajectories of moving objects, and make the following contributions: (i) we propose a general profile model to represent socio-demographic profiles of people such as worker, student, unemployed, etc; (ii) we propose a moving object history model to represent the daily movement of the object, and (iii) we propose similarity functions and an algorithm called T-Profiles for matching the profile model and the history model in order to infer the socio-demographic profile of a moving object from his/her trajectories. We validate T-Profiles with real trajectory data obtaining about 90% of precision

都市地域におけるソーシャルネットワーク利用者の活動性に関する研究--インドネシアマカッサル市を対象として--

This study aims to investigate the possibilities of using Twitter social media data as a source of knowledge for urban planning application. The author analyses 211,922 check-ins on Twitter. The dataset was utilized to analyses people\u27s movement by comparing the population on Twitter with the real urban population. Three data sources used: check-ins, population census, and questionnaire data. Secondly, with a mapping approach was used to study the dynamic urban land-use pattern by combining check-in features and individual text-posting activities. Thirdly, using a grid based on an aggregation method to analyze the city center\u27s location. Fourth, quantified the mobility of urban inhabitants by examining individuals\u27 movement patterns and calculated how far people travel in the city. Lastly, analyzed the social media users in the public spaces and public facilities. The thesis concludes that location based social media has great potential for helping understand the shape and structure of a city.北九州市立大

PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Knowledge Discovery through Mobility Data Integration

In the era of Big Data a huge amount of information are available from every sin- gle citizen of our hyper-connected world. A simple smartphone can collect data with different kinds of information: a big part of these are related to mobility. A smartphone is connected to networks, such as GSM, GPS, Internet (and then social networks): each of them can provide us information about where, how and why the user is moving across space and time. Data integration has a key role in this understanding process: the combination of different data sources increases the value of the extracted knowledge, even though such integration task is often not trivial. This thesis aim to represent a step toward a reliable Mobility Analysis framework, capable to exploit the richness of the spatio-temporal data nowadays available. The work done is an exploration of meaningful open challenges, from an efficient Map Matching of low sampling GPS data to Inferring Human Activities from GPS tracks. A further experimentation has been performed over GSM and Twitter data, in order to detect and recognize significant events in terms of people presence and related tweets. Another promising perspective is the use of such extracted knowledge to enrich actual geospatial Datasets with a ’Wisdom of the crowd’ dimension to derive, for instance, routing policies over road networks: most chosen paths among usual drivers are more meaningful than simple shortest paths