Big Data in Smart-Cities: Current Research and Challenges

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions

Big data usage in retail industry

This article is a review of the essence and application of Big Data and Analytics in the retail industry, including e-commerce. The vast and complex data generated nowadays is in the scope of the Big Data technologies. The computer-based automation of data management and analysis enables business organizations to discover hidden models and useful knowledge which refer to the business processes. The article highlights data-driven and analysis-based approaches to commerce and identifies the leading software solutions and their capabilities. The main aim is to bring out the business benefits of using Big Data and Analytics technologies in the retail industry. In the digital era the speed and breadth of knowledge turnover within the economy increases and the advantages become more accessible for the industries

Development of an IOT front-end with the Fiware platform for Smart City solutions

Internet de las Cosas es un tema de actualidad en el mundo de la tecnolog a. Poco a poco, el IoT se va convirtiendo en una necesidad para la poblaci on, y en una mina de oro para inversores y empresas. Por ello, ofrecer ofrecer una aplicaci on exitosa es el objetivo de muchas empresas, que quieren despuntar en campos como la telemedicina, la dom otica, el Internet de las Cosas industrial, o las Smart Cities. Dentro de este marco, pueden encontrarse colaboraciones entre empresas de la talla de Telefonica I+D, Orange, Thales, Siemens, o IBM, que han contribuido junto con la Uni on Europea en el desarrollo de una plataforma de IoT que busca integrar un entorno plani cado y com un para todos los componentes y clientes de la misma. Por la versatilidad y adaptabilidad de la plataforma, es altamente indicada para proyectos a gran escala, como el dise~no de Smart Cities. El proyecto que aqu se detalla tiene como objetivo demostrar las capacidades que caracterizan a la plataforma, utilizando el area de aplicaci on que mejor pueden exponerlas: el terreno de las Smart Cities. En primer lugar, se analizar an otras plataformas ya comercializadas de Internet de las Cosas. En segundo lugar, se investigar an otros modelos punteros de Smart City ya existentes. Finalmente, un modelo simulado de Smart City que satisfaga servicios haciendo uso de la plataforma ser a llevado a cabo. Utilizando datos reales de la ciudad de San Francisco, se ha modelado un sistema inteligente para satisfacer algunas de las necesidades de sus ciudadanos, de forma simulada. Con la nalidad de probar la versatilidad de la plataforma, se llevar an los datos publicados en ella a una herramienta online de visualizaci on ajena a la misma. Por otro lado, para probar la abilidad de la plataforma, esta ha sido sometida a tests de integraci on sobre los componentes, asegurando que estos pueden comunicarse e interactuar. El resultado de ambos ejercicios se encuentra documentado tambi en en este proyecto, que concluye con una revisi on positiva de la plataforma.Internet of Things is a cutting edge topic in technology nowadays. Little by little, IoT is becoming a need for people and a goldmine for research companies and investors, who aspire to provide a successful application in elds as eHealth, home automation, industrial IoT or Smart Cities. Inside this framework, companies as big as Telefonica I+D, Orange, Thales, Siemens and IBM, among others, have developed an IoT platform in cooperation with the European Union that looks for a solution to o er an common and organized environment for all the constituents of the platform, and all its clients. Due to the scalability and adaptability of the platform, it is particularly suitable for large-scale projects, such as the design and implementation of Smart Cities. This project attempts to be a demo capable of presenting the platform features, using the area of application that better exposes all its capabilities: a Smart City solution. Firstly, analysing other commercialised IoT platforms, more oriented to o er home automation solutions. Secondly, researching Smart City models already settled worldwide. Finally, a simulated Smart City was developed for this project. Using real data from the city of San Francisco, California, a simulated model of a smart system that satis es some services based in IoT solutions has been implemented. In order to test the adaptability of the platform, data can be visualized inside a graphical online tool. For testing the reliability of the platform, it was submitted to several integration tests among the components to make sure that they fully can communicate and interact. The result of both testing challenges also gures in this project, that concludes with a positive feedback for the platform.Ingeniería de Sistemas de Comunicacione

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms

A New Big Data and Logistic Regression-Based Approach for Small and Medium-Sized Enterprises

Businesses are being asked to assess an expanding volume of actual semi-structured and unstructured statistics to address the obstacles of internationalization and deal more effectively with the uncertainties of international integration. Big Data (BD) analytics can therefore play a strategic role in promoting the international expansion of Small and Medium-Sized Enterprises (SMEs). The exact connection between BD Analytics and globalization has, however, only been sporadically examined in the existing literature. In this study, a quantitative analysis using a Logistic Regression (LR) concept revealed that the interaction effects between BD Analytics architecture and BD Analytics functionality are both helpful and significant but the connection between the management of BD Analytics architecture and the Degree of Internationalization (DI) is not required for internationalization development. This shows that increasing internationalization in SMEs requires more than BD Analytics governance alone. Instead, this study emphasizes the importance of building particular BD Analytics abilities and the availability of a beneficial interaction between management of BD Analytics architecture and BD Analytics abilities that could take advantage of the new information gained via BD Analytics in SME global expansion