scholarly journals Smart cities beyond algorithmic logic: digital platforms, user engagement and data science

2019 ◽  
pp. 1-15
Author(s):  
Nicos Komninos ◽  
Anastasia Panori ◽  
Christina Kakderi
Keyword(s):  
Data Science ◽  
Smart Cities ◽  
User Engagement ◽  
Digital Platforms ◽  
Algorithmic Logic

scholarly journals Evaluating How ‘Smart’ Brașov, Romania Can Be Virtually via a Mobile Application for Cultural Tourism

2020 ◽  
Vol 12 (13) ◽  
pp. 5324 ◽  
Author(s):  
Arabela Briciu ◽  
Victor-Alexandru Briciu ◽  
Androniki Kavoura
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Smart Cities ◽  
Modern Society ◽  
Cultural Tourism ◽  
User Engagement ◽  
Heritage Sites ◽  
Information And Communication ◽  
Intelligent Methods

Global urbanization brings the urge to identify the most intelligent methods to cope with the challenges arising in the modern society. Sustainable and smart cities are the new target for urban development; their representatives are being forced to identify and develop new strategies to increase their city’s performance and ensure that it endures over time. Information and Communication Technology (ICT) contributes to this purpose. Brașov is one of the cities in Romania for which the process of modernization started years ago, and it is currently developing as a smart city. This paper focuses on the development of the city in terms of cultural tourism solutions by presenting a case study on the use of virtual reality with a mobile application and its evaluation on cultural heritage sites. The original contribution of the paper is to describe and analyze the quality of the mobile application by using a proposed analysis grid to identify the main elements of this app. The main findings suggest that the application may bring authenticity of experience through the lens of heritage preservation for further user engagement and participation in real-time, while suggestions are made for future enhancement. Implications are discussed for a) destination managers, b) for developers to improve the general quality of the mobile application in terms of design and features and to implement changes in the near future, and c) for visitors who engage in real-time and co-create experiences.

scholarly journals Weaving seams with data: Conceptualizing City APIs as elements of infrastructures

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171982761 ◽  
Author(s):  
Christoph Raetzsch ◽  
Gabriel Pereira ◽  
Lasse S Vestergaard ◽  
Martin Brynskov
Keyword(s):  
Data Science ◽  
Technological Development ◽  
Smart Cities ◽  
Data Sources ◽  
Renewal Processes ◽  
Application Programming Interfaces ◽  
Information Infrastructures ◽  
Application Programming ◽  
Programming Interfaces

This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of smart cities and communities. On top of the built infrastructures in cities, application programming interfaces allow to weave new kinds of seams from static and dynamic data sources into the urban fabric. Contributing to debates about “urban informatics” and the governance of urban information infrastructures, this article provides a technically informed and critically grounded approach to evaluating APIs as crucial but often overlooked elements within these infrastructures. The conceptualization of what we term City APIs is informed by three perspectives: In the first part, we review established criticisms of proprietary social media APIs and their crucial function in current web architectures. In the second part, we discuss how the design process of APIs defines conventions of data exchanges that also reflect negotiations between API producers and API consumers about affordances and mental models of the underlying computer systems involved. In the third part, we present recent urban data innovation initiatives, especially CitySDK and OrganiCity, to underline the centrality of API design and governance for new kinds of civic and commercial services developed within and for cities. By bridging the fields of criticism, design, and implementation, we argue that City APIs as elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation between data science, technological development, urban management, and civic participation.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Green Internet of Things and Big Data Application in Smart Cities Development

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhai Yang ◽  
Liu Jianjun ◽  
Humaira Faqiri ◽  
Wasswa Shafik ◽  
Alanazi Talal Abdulrahman ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Life Span ◽  
Information And Communication Technologies ◽  
Resource Sharing ◽  
Wireless Sensors ◽  
Data Science ◽  
Raw Materials ◽  
Technological Development ◽  
Smart Cities

This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G-IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

scholarly journals The Challenge of Big Data and Data Science

2019 ◽  
Vol 22 (1) ◽  
pp. 297-323 ◽  
Author(s):  
Henry E. Brady
Keyword(s):  
Big Data ◽  
Data Science ◽  
Smart Cities ◽  
Video Data ◽  
Cyber Warfare ◽  
Ethical Challenges ◽  
Cyber Terrorism ◽  
Social Scientists ◽  
The Media ◽  
Audio Video

Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability (Preprint)

2021 ◽  
Author(s):  
Max-Marcel Theilig ◽  
Ashley A Knapp ◽  
Jennifer M Nicholas ◽  
Rüdiger Zarnekow ◽  
David C Mohr
Keyword(s):  
Mental Health ◽  
Data Science ◽  
Digital Health ◽  
A Priori ◽  
User Interaction ◽  
User Engagement ◽  
Behavioral Strategies ◽  
Data Set ◽  
Public Data ◽  
Latent Features

BACKGROUND Using mobile health technology has sparked a broad engagement of data science and machine learning methods to leverage the complex, assorted amount of data for mental health purposes. Despite many studies, there is a reported underdevelopment of user engagement concepts, and the desire for high accuracy or significance has shown to lead to low explicability and irreproducibility. OBJECTIVE To overcome such reasons of poor analysis input and facilitate the reproducibility and credibility of artificial intelligence applications, we aim to explore principal characteristics of user interaction with digital mental health. METHODS We generated five latent features based on previous research, expert opinions from digital mental health, and informed by data. The features were analyzed with descriptive statistics and data visualization. We carried out two rounds of evaluations with data from 12,400 users of IntelliCare, a mental health platform with 12 apps. First, we focused to proof concept and second, we assessed reproducibility by drawing conclusion from distribution differences. User data was drawn from both research trials and public deployment on Google Play. RESULTS Our algorithms showed advantages over commonly used concepts and reproduce in our public data set with different underlying behavioral strategies. These measures relate to the distribution of a user’s allocated attention, users’ circadian behavior, their consecutive commitment to a specific strategy, and users’ interaction trajectory. Because distributions between research trial and public deployment were similar, consistency was implied regarding the underlying behavioral strategies: psychoeducation and goal setting are used as a catalyst to overcome the users’ primary obstacles, sleep hygiene is addressed most regularly, while regular self-reflective thinking is avoided. Relaxation as well as cognitive reframing have increased variance in commitment among public users, indicating the challenging nature of these apps. The relative course of users’ engagement is similar in research and public data. CONCLUSIONS We argue that deliberate, a-priori feature engineering is essential for reproducible, tangible, and explainable study analyses. Our features enable improved results as well as interpretability, providing an increased understanding of how people engage with multiple mental health apps over time. Since we based the generation of features on generic interaction, these methods are applicable to further methods of study analysis and digital health.

scholarly journals A Collaborative Pilot Platform for Data Annotation and Enrichment in Viticulture

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 149 ◽  
Author(s):  
Phivos Mylonas ◽  
Yorghos Voutos ◽  
Anastasia Sofou
Keyword(s):  
Digital Data ◽  
Free Text ◽  
User Engagement ◽  
Data Annotation ◽  
Digital Platforms ◽  
Cross Domain ◽  
Metadata Enrichment ◽  
Smart Agriculture ◽  
Iot Devices

It took some time indeed, but the research evolution and transformations that occurred in the smart agriculture field over the recent years tend to constitute the latter as the main topic of interest in the so-called Internet of Things (IoT) domain. Undoubtedly, our era is characterized by the mass production of huge amounts of data, information and content deriving from many different sources, mostly IoT devices and sensors, but also from environmentalists, agronomists, winemakers, or plain farmers and interested stakeholders themselves. Being an emerging field, only a small part of this rich content has been aggregated so far in digital platforms that serve as cross-domain hubs. The latter offer typically limited usability and accessibility of the actual content itself due to problems dealing with insufficient data and metadata availability, as well as their quality. Over our recent involvement within a precision viticulture environment and in an effort to make the notion of smart agriculture in the winery domain more accessible to and reusable from the general public, we introduce herein the model of an aggregation platform that provides enhanced services and enables human-computer collaboration for agricultural data annotations and enrichment. In principle, the proposed architecture goes beyond existing digital content aggregation platforms by advancing digital data through the combination of artificial intelligence automation and creative user engagement, thus facilitating its accessibility, visibility, and re-use. In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment, it offers a cornerstone for future researchers focusing on improving the quality of digital agricultural information analysis and its presentation, thus establishing new ways for its efficient exploitation in a larger scale with benefits both for the agricultural and the consumer domains.

scholarly journals Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3497 ◽  
Author(s):  
César Benavente-Peces ◽  
Nisrine Ibadah
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Science ◽  
Smart Cities ◽  
Thermal Modeling ◽  
Modern Society ◽  
Machine Learning Techniques ◽  
Single Family ◽  
Residential Areas ◽  
Learning Techniques

Energy efficiency is a major concern to achieve sustainability in modern society. Smart cities sustainability depends on the availability of energy-efficient infrastructures and services. Buildings compose most of the city, and they are responsible for most of the energy consumption and emissions to the atmosphere (40%). Smart cities need smart buildings to achieve sustainability goals. Building’s thermal modeling is essential to face the energy efficiency race. In this paper, we show how ICT and data science technologies and techniques can be applied to evaluate the energy efficiency of buildings. In concrete, we apply machine learning techniques to classify buildings based on their energy efficiency. Particularly, our focus is on single-family buildings in residential areas. Along this paper, we demonstrate the capabilities of machine learning techniques to classify buildings depending on their energy efficiency. Moreover, we analyze and compare the performance of different classifiers. Furthermore, we introduce new parameters which have some impact on the buildings thermal modeling, especially those concerning the environment where the building is located. We also make an insight on ICT and remark the growing relevance in data acquisition and monitoring of relevant parameters by using wireless sensor networks. It is worthy to remark the need for an appropriate and reliable dataset to achieve the best results. Moreover, we demonstrate that reliable classification is feasible with a few featured parameters.

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