Digitalization of Retail Stores using Bluetooth Low Energy Beacons

This thesis explores the domains of retail stores and the Internet of Things, with a focus on Bluetooth Low Energy beacons. It investigates how one can use the technology to improve physical stores, for the benefit of both the store and the customers. It does this by going through literature and information from academia and the relevant industry. Additionally, an interview with an expert in the retail domain is conducted, and a survey consisting of a series of interviews and questionnaire with what can be considered experts in the IT domain. A prototype app called Stass is developed, the app demonstrates some of the usages of the technology and is also used for evaluating the performance of the beacons.Masteroppgave i informasjonsvitenskapINFO39

Data Analysis From an Internet Of Things System in a Gas Station Convenience Store

RÉSUMÉ : Le numérique est de plus en plus populaire et peut être appliquée à plusieurs industries et entreprises afin d'améliorer la productivité et extraire des informations de marketing. Ce travail de recherche s’adresse sur le potentiel des applications d'exploration de données dans un magasin numérisé de vente au détail traditionnel. L'objectif est de démontrer que grâce à un système IoT, des informations peuvent être extraites à partir des données collectées à l'aide des méthodes appropriées, tel que les méthodes d'exploration de données. Nos objectifs ont été réalisés en installant des capteurs Bluetooth dans un dépanneur de station d’essence dans la ville de Laval et en recueillant des données provenant des appareils Bluetooth des clients. Ces appareils incluent tous les téléphones intelligents et les montres intelligentes équipés de la technologie Bluetooth. Une collecte automatisée a été faite sur une durée de une semaine. À partir des données collectées, une première analyse a été effectué pour trouver une corrélation entre le RSSI et les distances réelles dans le but de tracer le mouvement des clients dans le magasin. Ces analyses ont montré que la précision des capteurs n’est pas assez forte pour démontrer un mouvement précis des clients. Pour s’adapter au manque de précision observé, la prochaine étape a été de regarder les données des capteurs comme des événements de présences ou absences dans les zones autours de chaque capteur. Avec les présences identifiées, une proportion de volume d’activité dans chaque zone a été établi comme donnée pour être utilisée avec les rapports de ventes du magasin pour en construire un arbre de décision. Nos résultats ont démontré que des informations peuvent être extraites à partir de la construction de ces arbres de décision qui contiennent des données venant d'un système IoT bien mis en place dans un environnement de vente au détail traditionnel.----------ABSTRACT : Digitalization is increasingly popular and can be applied to multiple industries and businesses to improve productivity and extract marketing insights. This research work looks at the potential of data mining applications in a digitalized traditional retail store. The goal is to demonstrate that through the means of an IoT system, insight can be extracted from the collected data with the proper tools, such as data mining methods. This has been done by installing Bluetooth beacons in a gas station convenience store in the city of Laval and collecting data coming from the customers Bluetooth devices. These devices include all smartphones and smart watches equipped with Bluetooth. An automated collection of data was done for a duration of one week. From the collected data, a first analysis was done to find a correlation between the RSSI and real distances to trace customers pathways within the store. These analysis showed us that the sensors precisions are not high enough to show a precise client pathway within the store. To adapt to this lack of precision, the next step was to look at the data from the sensors as events of presences or absences in the zones around each sensor. With each presence identified, a proportion of volume of activity in each zone has been established as data to be used with the store’s sales report to build a decision tree. Our results have showed that useful information can be extracted from a properly constructed decision tree with data coming from an IoT system put in place in a traditional retail environment

Optical boundaries for LED-based indoor positioning system

Overlap of footprints of light emitting diodes (LEDs) increases the positioning accuracy of wearable LED indoor positioning systems (IPS) but such an approach assumes that the footprint boundaries are defined. In this work, we develop a mathematical model for defining the footprint boundaries of an LED in terms of a threshold angle instead of the conventional half or full angle. To show the effect of the threshold angle, we compare how overlaps and receiver tilts affect the performance of an LED-based IPS when the optical boundary is defined at the threshold angle and at the full angle. Using experimental measurements, simulations, and theoretical analysis, the effect of the defined threshold angle is estimated. The results show that the positional time when using the newly defined threshold angle is 12 times shorter than the time when the full angle is used. When the effect of tilt is considered, the threshold angle time is 22 times shorter than the full angle positioning time. Regarding accuracy, it is shown in this work that a positioning error as low as 230 mm can be obtained. Consequently, while the IPS gives a very low positioning error, a defined threshold angle reduces delays in an overlap-based LED IPS

Discovery of Transport Operations from Geolocation Data

Os dados de geolocalização identificam a localização geográfica de pessoas ou objetos e são fundamentais para empresas que dependem de veículos, como empresas logísticas e de transportes. Com o avanço da tecnologia, a recolha de dados de geolocalização tornou-se cada vez mais acessível e económica, gerando novas oportunidades de inteligência empresarial. Este tipo de dados tem sido utilizado principalmente para caracterizar o veículo em termos de posicionamento e navegação, mas também pode ter um papel preponderante na avaliação de desempenho em relação às atividades e operações executadas. A abordagem proposta consiste numa metodologia com várias etapas que recebe dados de geolocalização como entrada e permite a análise do processo de negócio no final. Em primeiro lugar, a preparação dos dados é aplicada para lidar com uma série de questões relacionadas com ruído e erros nos dados. Depois, a identificação dos eventos estacionários é realizada com base nos estados estacionários dos veículos. Em seguida, é realizada a inferência de operações com base numa análise espacial, que permite descobrir os locais onde os eventos estacionários ocorrem com frequência. Finalmente, as operações identificadas são classificadas com base nas suas características, e a sequência de eventos pode ser estruturada. A aplicação de técnicas de process mining é então possível e a consequente extração de conhecimento do processo. As etapas da metodologia também podem ser utilizadas separadamente para enfrentar desafios específicos, dando mais flexibilidade à sua aplicação. Três estudos de caso distintos são apresentados para demonstrar a eficácia e transversalidade da solução. Fluxos de dados de geolocalização em tempo real de autocarros de duas redes distintas de transporte público são usados para demonstrar a detecção de operações relacionadas com os veículos e comparar as distintas abordagens propostas por este trabalho. As operações dos autocarros produzem uma sequência estruturada de eventos que descreve o comportamento dos mesmos. Esse comportamento é mapeado por meio da aplicação de técnicas de process mining, para descobrir oportunidades de análise e gargalos no processo. Complementarmente, os dados de geolocalização de uma empresa de logística internacional são explorados para a monitorização de processos logísticos, nomeadamente para detecção de operações de logística em tempo real, demonstrando a eficácia da solução proposta para resolver problemas específicos da indústria. Os resultados deste trabalho revelam novas possibilidades no uso de dados de geolocalização e o seu potencial para gerar conhecimento acerca do processo. A exploração de dados de geolocalização nos contextos logísticos e de transportes públicos apresenta-se como uma oportunidade para melhorar a monitorização e gestão das operações baseadas em veículos. Isso pode originar melhorias na eficiência do processo e, consequentemente, maior lucro e melhor qualidade do serviço.Geolocation data identifies the geographic location of people or objects, and is fundamental for businesses relying on vehicles such as logistics and transportation. With the advance of technology, collecting geolocation data has become increasingly accessible and affordable, raising new opportunities for business intelligence. This type of data has been used mainly for characterizing the vehicle in terms of positioning and navigation, but it can also showcase its performance regarding the executed activities and operations. The proposed approach consists on a multi-step methodology that receives geolocation data as an input and allows the analysis of the business process in the end. Firstly, the preparation of the data is applied to handle a number of issues related to outliers, data noise, and missing or erroneous information. Then, the identification of stationary events is performed based on the motionless states of the vehicles. Next, the inference of operations based on a spatial analysis is performed, which allows the discovery of the locations where stationary events occur frequently. Finally, the identified operations are classified based on their characteristics, and the sequence of events can be structured into an event log. The application of process mining techniques is then possible and the consequently extraction of process knowledge. The steps of the methodology can also be used separately to tackle specific challenges, giving more flexibility to its application. Three distinct case studies are presented to demonstrate the effectiveness and transversality of the solution. Real-time geolocation data streams of buses from two distinct public transport networks are used to demonstrate the detection of vehicle-based operations and compare the distinct approaches proposed by this work. The buses operations produce a structured sequence of events that describes the behaviour of the buses. This behaviour is mapped through the application of process mining techniques uncovering analysis opportunities and discovering bottlenecks in the process. Geolocation data from an international logistics company is exploited for monitoring logistics processes, namely for detecting vehicle-based operations in real time, showing the effectiveness of the proposed solution to solve specific industry problems. The results of this work reveal new possibilities for geolocation data and its potential to generate process knowledge. The exploitation of geolocation data in the public transport and logistics contexts poses as an opportunity for improving the monitoring and management of vehicle-based operations. This can lead to into improvements in the process efficiency and consequently higher profit and better service quality

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. 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An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data. Environment and Planning B: Planning and Design, 41(6), 1113-1131. doi:10.1068/b130047pDe Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235-257. doi:10.1016/j.is.2015.07.003Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Arroyo, R., Yebes, J. J., Bergasa, L. M., Daza, I. G., & Almazán, J. (2015). Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Systems with Applications, 42(21), 7991-8005. doi:10.1016/j.eswa.2015.06.016Popa, M. C., Rothkrantz, L. J. M., Shan, C., Gritti, T., & Wiggers, P. (2013). Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. Pattern Recognition Letters, 34(7), 809-819. doi:10.1016/j.patrec.2012.04.015Kang, L., & Hansen, M. (2017). Behavioral analysis of airline scheduled block time adjustment. Transportation Research Part E: Logistics and Transportation Review, 103, 56-68. doi:10.1016/j.tre.2017.04.004Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). 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Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity