904 research outputs found

    Colossal Trajectory Mining: A unifying approach to mine behavioral mobility patterns

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    Spatio-temporal mobility patterns are at the core of strategic applications such as urban planning and monitoring. Depending on the strength of spatio-temporal constraints, different mobility patterns can be defined. While existing approaches work well in the extraction of groups of objects sharing fine-grained paths, the huge volume of large-scale data asks for coarse-grained solutions. In this paper, we introduce Colossal Trajectory Mining (CTM) to efficiently extract heterogeneous mobility patterns out of a multidimensional space that, along with space and time dimensions, can consider additional trajectory features (e.g., means of transport or activity) to characterize behavioral mobility patterns. The algorithm is natively designed in a distributed fashion, and the experimental evaluation shows its scalability with respect to the involved features and the cardinality of the trajectory dataset

    Serverless Strategies and Tools in the Cloud Computing Continuum

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    Tesis por compendio[ES] En los últimos años, la popularidad de la computación en nube ha permitido a los usuarios acceder a recursos de cómputo, red y almacenamiento sin precedentes bajo un modelo de pago por uso. Esta popularidad ha propiciado la aparición de nuevos servicios para resolver determinados problemas informáticos a gran escala y simplificar el desarrollo y el despliegue de aplicaciones. Entre los servicios más destacados en los últimos años se encuentran las plataformas FaaS (Función como Servicio), cuyo principal atractivo es la facilidad de despliegue de pequeños fragmentos de código en determinados lenguajes de programación para realizar tareas específicas en respuesta a eventos. Estas funciones son ejecutadas en los servidores del proveedor Cloud sin que los usuarios se preocupen de su mantenimiento ni de la gestión de su elasticidad, manteniendo siempre un modelo de pago por uso de grano fino. Las plataformas FaaS pertenecen al paradigma informático conocido como Serverless, cuyo propósito es abstraer la gestión de servidores por parte de los usuarios, permitiéndoles centrar sus esfuerzos únicamente en el desarrollo de aplicaciones. El problema del modelo FaaS es que está enfocado principalmente en microservicios y tiende a tener limitaciones en el tiempo de ejecución y en las capacidades de computación (por ejemplo, carece de soporte para hardware de aceleración como GPUs). Sin embargo, se ha demostrado que la capacidad de autoaprovisionamiento y el alto grado de paralelismo de estos servicios pueden ser muy adecuados para una mayor variedad de aplicaciones. Además, su inherente ejecución dirigida por eventos hace que las funciones sean perfectamente adecuadas para ser definidas como pasos en flujos de trabajo de procesamiento de archivos (por ejemplo, flujos de trabajo de computación científica). Por otra parte, el auge de los dispositivos inteligentes e integrados (IoT), las innovaciones en las redes de comunicación y la necesidad de reducir la latencia en casos de uso complejos han dado lugar al concepto de Edge computing, o computación en el borde. El Edge computing consiste en el procesamiento en dispositivos cercanos a las fuentes de datos para mejorar los tiempos de respuesta. La combinación de este paradigma con la computación en nube, formando arquitecturas con dispositivos a distintos niveles en función de su proximidad a la fuente y su capacidad de cómputo, se ha acuñado como continuo de la computación en la nube (o continuo computacional). Esta tesis doctoral pretende, por lo tanto, aplicar diferentes estrategias Serverless para permitir el despliegue de aplicaciones generalistas, empaquetadas en contenedores de software, a través de los diferentes niveles del continuo computacional. Para ello, se han desarrollado múltiples herramientas con el fin de: i) adaptar servicios FaaS de proveedores Cloud públicos; ii) integrar diferentes componentes software para definir una plataforma Serverless en infraestructuras privadas y en el borde; iii) aprovechar dispositivos de aceleración en plataformas Serverless; y iv) facilitar el despliegue de aplicaciones y flujos de trabajo a través de interfaces de usuario. Además, se han creado y adaptado varios casos de uso para evaluar los desarrollos conseguidos.[CA] En els últims anys, la popularitat de la computació al núvol ha permès als usuaris accedir a recursos de còmput, xarxa i emmagatzematge sense precedents sota un model de pagament per ús. Aquesta popularitat ha propiciat l'aparició de nous serveis per resoldre determinats problemes informàtics a gran escala i simplificar el desenvolupament i desplegament d'aplicacions. Entre els serveis més destacats en els darrers anys hi ha les plataformes FaaS (Funcions com a Servei), el principal atractiu de les quals és la facilitat de desplegament de petits fragments de codi en determinats llenguatges de programació per realitzar tasques específiques en resposta a esdeveniments. Aquestes funcions són executades als servidors del proveïdor Cloud sense que els usuaris es preocupen del seu manteniment ni de la gestió de la seva elasticitat, mantenint sempre un model de pagament per ús de gra fi. Les plataformes FaaS pertanyen al paradigma informàtic conegut com a Serverless, el propòsit del qual és abstraure la gestió de servidors per part dels usuaris, permetent centrar els seus esforços únicament en el desenvolupament d'aplicacions. El problema del model FaaS és que està enfocat principalment a microserveis i tendeix a tenir limitacions en el temps d'execució i en les capacitats de computació (per exemple, no té suport per a maquinari d'acceleració com GPU). Tot i això, s'ha demostrat que la capacitat d'autoaprovisionament i l'alt grau de paral·lelisme d'aquests serveis poden ser molt adequats per a més aplicacions. A més, la seva inherent execució dirigida per esdeveniments fa que les funcions siguen perfectament adequades per ser definides com a passos en fluxos de treball de processament d'arxius (per exemple, fluxos de treball de computació científica). D'altra banda, l'auge dels dispositius intel·ligents i integrats (IoT), les innovacions a les xarxes de comunicació i la necessitat de reduir la latència en casos d'ús complexos han donat lloc al concepte d'Edge computing, o computació a la vora. L'Edge computing consisteix en el processament en dispositius propers a les fonts de dades per millorar els temps de resposta. La combinació d'aquest paradigma amb la computació en núvol, formant arquitectures amb dispositius a diferents nivells en funció de la proximitat a la font i la capacitat de còmput, s'ha encunyat com a continu de la computació al núvol (o continu computacional). Aquesta tesi doctoral pretén, doncs, aplicar diferents estratègies Serverless per permetre el desplegament d'aplicacions generalistes, empaquetades en contenidors de programari, a través dels diferents nivells del continu computacional. Per això, s'han desenvolupat múltiples eines per tal de: i) adaptar serveis FaaS de proveïdors Cloud públics; ii) integrar diferents components de programari per definir una plataforma Serverless en infraestructures privades i a la vora; iii) aprofitar dispositius d'acceleració a plataformes Serverless; i iv) facilitar el desplegament d'aplicacions i fluxos de treball mitjançant interfícies d'usuari. A més, s'han creat i s'han adaptat diversos casos d'ús per avaluar els desenvolupaments aconseguits.[EN] In recent years, the popularity of Cloud computing has allowed users to access unprecedented compute, network, and storage resources under a pay-per-use model. This popularity led to new services to solve specific large-scale computing challenges and simplify the development and deployment of applications. Among the most prominent services in recent years are FaaS (Function as a Service) platforms, whose primary appeal is the ease of deploying small pieces of code in certain programming languages to perform specific tasks on an event-driven basis. These functions are executed on the Cloud provider's servers without users worrying about their maintenance or elasticity management, always keeping a fine-grained pay-per-use model. FaaS platforms belong to the computing paradigm known as Serverless, which aims to abstract the management of servers from the users, allowing them to focus their efforts solely on the development of applications. The problem with FaaS is that it focuses on microservices and tends to have limitations regarding the execution time and the computing capabilities (e.g. lack of support for acceleration hardware such as GPUs). However, it has been demonstrated that the self-provisioning capability and high degree of parallelism of these services can be well suited to broader applications. In addition, their inherent event-driven triggering makes functions perfectly suitable to be defined as steps in file processing workflows (e.g. scientific computing workflows). Furthermore, the rise of smart and embedded devices (IoT), innovations in communication networks and the need to reduce latency in challenging use cases have led to the concept of Edge computing. Edge computing consists of conducting the processing on devices close to the data sources to improve response times. The coupling of this paradigm together with Cloud computing, involving architectures with devices at different levels depending on their proximity to the source and their compute capability, has been coined as Cloud Computing Continuum (or Computing Continuum). Therefore, this PhD thesis aims to apply different Serverless strategies to enable the deployment of generalist applications, packaged in software containers, across the different tiers of the Cloud Computing Continuum. To this end, multiple tools have been developed in order to: i) adapt FaaS services from public Cloud providers; ii) integrate different software components to define a Serverless platform on on-premises and Edge infrastructures; iii) leverage acceleration devices on Serverless platforms; and iv) facilitate the deployment of applications and workflows through user interfaces. Additionally, several use cases have been created and adapted to assess the developments achieved.Risco Gallardo, S. (2023). Serverless Strategies and Tools in the Cloud Computing Continuum [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202013Compendi

    Accelerating finite state machine-based testing using reinforcement learning

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    Testing is a crucial phase in the development of complex systems, and this has led to interest in automated test generation techniques based on state-based models. Many approaches use models that are types of finite state machine (FSM). Corresponding test generation algorithms typically require that certain test components, such as reset sequences (RSs) and preset distinguishing sequences (PDSs), have been produced for the FSM specification. Unfortunately, the generation of RSs and PDSs is computationally expensive, and this affects the scalability of such FSM-based test generation algorithms. This paper addresses this scalability problem by introducing a reinforcement learning framework: the Q -Graph framework for MBT. We show how this framework can be used in the generation of RSs and PDSs and consider both (potentially partial) timed and untimed models. The proposed approach was evaluated using three types of FSMs: randomly generated FSMs, FSMs from a benchmark, and an FSM of an Engine Status Manager for a printer. In experiments, the proposed approach was much faster and used much less memory than the state-of-the-art methods in computing PDSs and RSs

    Machine Unlearning: A Survey

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    Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

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    The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

    Adoption of the big data concept in the construction industry

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The Big Data (BD) boom has increased exponentially in recent years, reaching even the most traditional industries. In construction, the migration towards sustainability and new technologies that produce user and environmentally friendly projects is now a requirement in almost every country. Meanwhile, BD technology has become a possible solution to the challenges that the industry faces nowadays with some authors naming this technology as the future of construction. However, despite this reception, studies that explain in detail the factors that favour the adoption of BD are scarce or non-existent and the adoption itself has proven to be a challenge, especially in industries such as construction that are not technology driven. Understanding the critical factors that influence BD adoption has become the focus of many industries that seek to exploit the benefits offered by this technology. Therefore, the aim of this research is to explore the adoption of BD in the construction industry. First, the awareness of the Dominican Republic’s construction industry on the BD concept, its characteristics, and benefits was assessed. The key drivers, strategies, and challenges regarding the adoption of BD in the industry were also investigated. A qualitative method was selected to identify these strategies due to the lack of maturity and the scarcity of sources that address the subject. Semi-structured interviews were selected as the data collection tool, and content and thematic analysis were chosen to acquire an in-depth knowledge of the interviews. Endsley’s model of situational awareness was adapted to provide a better understanding of the industry’s awareness of BD. The sampling technique adopted was non-probabilistic due to some of the specific criteria identified during the secondary data collection process. In the data collection process, 21 interviews were conducted with representatives of 19 organisations with an undoubted presence in the construction market of the Dominican Republic. The results showed that there is an overall basic level of awareness about BD in the construction industry of the Dominican Republic. Moreover, nine key drivers for BD adoption were identified and grouped into internal and external drivers. Additionally, four main strategies or central policies for adopting the technology and seven main challenges were identified. These findings were used to develop an organisation readiness assessment tool and a strategic framework for BD adoption in the construction industry. This study concluded that new technologies such as Big Data (BD) require a change in the industry's culture and the adoption of digital approaches to be fully implemented. The findings of this research provide valuable insights that can help the construction industry adopt BD technology, thus accessing the short and long-term benefits that this technology offers.Government of the Dominican Republic through the Ministry of Higher Education, Science and Technology

    A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

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    Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app
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