scholarly journals Crowdsourcing Research for Social Insights into Smart Cities Applications and Services

2021 ◽  
Vol 13 (14) ◽  
pp. 7531
Author(s):  
Wadee Alhalabi ◽  
Miltiadis Lytras ◽  
Nada Aljohani
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Human Behavior ◽  
Business Models ◽  
Smart Cities ◽  
Open Data ◽  
Well Being ◽  
Prototype System ◽  
Positioning System ◽  
Social Challenges

The evolution in knowledge management and crowdsourcing research provides new data-processing capabilities. The availability of both structured and unstructured open data formats offers unforeseen opportunities for analytics processing and advanced decision-making. However, social sciences research is facing advanced, complicated social challenges and problems. The focus of this study is to analyze the contribution of crowdsourcing techniques to the promotion of advanced social sciences research, exploiting open data available from the geographical positioning system (GPS) to analyze human behavior. In our study, we present the conceptual design of a device that, with the help of a global positioning system-data collection device (GPS-DCD), associates behavioral aspects of human life with place. The main contribution of this study is to integrate research in computer science and information systems with that in social science. The prototype system summarized in this work, proves the capacity of crowdsourcing and big data research to facilitate aggregation of microcontent related to human behavior toward improved quality of life and well-being in modern smart cities. Various ethical issues are also discussed to promote the scientific debate on this matter. Our study shows the capacity of emerging technologies to deal with social challenges. This kind of research will gain increased momentum in the future due to the availability of big data and new business models for social platforms.

Open Data in a Big Data World

2016 ◽  
Vol 38 (2) ◽  
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Open Access ◽  
Environmental Protection ◽  
Health Research ◽  
Smart Cities ◽  
Open Data ◽  
Scientific Process ◽  
Guiding Principles ◽  
Global Research

AbstractFour major international science organisations (ICSU, ISSC, IAP and TWAS) have joined together to develop and support an accord that includes a set of guiding principles on open access to big data, which is necessary to protect the scientific process and assure that developing countries can participate more fully in the global research enterprise. Limits on access to big data knowledge, they warn, raises the risk that progress will slow in areas such as advanced health research, environmental protection, food productio,n and the development of smart cities.

scholarly journals Smart Cities, Big Data, Artificial Intelligence and Respect for the European Union Data Protection Rules

2020 ◽  
Vol 3 (2) ◽  
pp. 101
Author(s):  
Francisco Javier Durán Ruiz
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Big Data ◽  
Data Protection ◽  
Smart City ◽  
Smart Cities ◽  
Open Data ◽  
Personal Data ◽  
Point Of View ◽  
The European Union

The importance of cities and their populations grow more and more, as well as the need to apply ICT in their management to reduce their environmental impact and improve the services they offer to their citizens. Hence the concept of smart city arises, a transformation of urban spaces that the European Union is strongly promoting which is largely based on the use of data and its treatment using Big data and Artificial Intelligence techniques based in algorithms. For the development of smart cities it is basic, from a legal point of view, EU rules about open data and the reuse of data and the reconciliation of the massive processing of citizens' data with the right to privacy, non-discrimination and protection of personal data. The use of Big data and AI needed for the development of smart city projects requires a particular respect to data protection regulations. In this sense, the research explores in depth the specific hazards of vulnerating this fundamental right in the framework of smart cities due to the use of Big Data and AI.

Political Networks and Computational Social Science

2017 ◽  
Author(s):  
David Lazer ◽  
Stefan Wojcik
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Human Behavior ◽  
Inflection Point ◽  
Human Life ◽  
Computational Social Science ◽  
The Internet ◽  
Computational Approaches ◽  
Political Networks ◽  
The Social

The last half century has witnessed the digitization of human life, with a sharp inflection point being the widespread adoption of the Internet. In the wake of this digitization the phrase “big data” has been coined. Because many big data are explicitly or implicitly relational, this digitization of humanity has been critical in the increase in the study of networks. Further, since this digitization process continues not only forward but backward (e.g., through the scanning of millions of books and news periodicals going back for centuries), it is likely that the social sciences will be recentered over the next generation around computational approaches to data emphasizing (1) the relational aspects of human behavior, (2) phenomena that exist on societal scales rather than just individual ones, and (3) the dynamics of human behavior. This chapter discusses, in particular, the potential transformation of political science in these directions.

scholarly journals From Big Data to Knowledge in the Social Sciences

2015 ◽  
Vol 659 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Bradford W. Hesse ◽  
Richard P. Moser ◽  
William T. Riley
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Open Data ◽  
High Volume ◽  
National Institutes Of Health ◽  
Scientific Enterprise ◽  
Data Intensive ◽  
Scientific Disciplines ◽  
The Social ◽  
Systemic Problems

One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.

scholarly journals Co-owned resources: IP and data in smart cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angeliki Maria Toli ◽  
Niamh Murtagh ◽  
Hedley Smyth
Keyword(s):  
Intellectual Property ◽  
Smart City ◽  
Business Models ◽  
Smart Cities ◽  
Open Data ◽  
Structured Interviews ◽  
Content Type ◽  
New Business ◽  
Value Propositions ◽  
Operant Resources

PurposeSmart city projects typically operate in consortia of actors that lead to the co-creation of jointly owned intellectual property (IP) and data. While IP and data are significant for economic development, there are very limited studies on their co-ownership regimes especially on co-ownership of open data and open intellectual property. This study address this gap.Design/methodology/approachThis study is qualitative. In total, 62 in-depth semi-structured interviews were carried out, with predominantly senior members of organisations actively involved in smart city projects. Thematic analysis was used to analyse the data.FindingsThere are three models of co-ownership of IP and data: contractual joint ownership, undetermined or not-yet-determined ownership and open ownership. Each ownership model impacts differently the value-in-use. The relationships between actors in the consortia affect the way in which they co-create IP and data.Originality/valueThis study demonstrates how projects that operate in new models of innovation-led consortia produce new types of resources that are not simply co-created but co-owned. Co-owned resources have different value-in-use for each one of the different actors, independently of the fact that they jointly own them. This is influenced by the type of ownership model and predisposition of the actors to initially share resources and be flexible. Co-owned resources may generate future value propositions, act as interconnected operant resources and lead to the creation of new business models.

scholarly journals Big Data in Smart City: Management Challenges

2021 ◽  
Vol 11 (10) ◽  
pp. 4557
Author(s):  
Mladen Amović ◽  
Miro Govedarica ◽  
Aleksandra Radulović ◽  
Ivana Janković
Keyword(s):  
Big Data ◽  
Smart City ◽  
Urban Areas ◽  
Management System ◽  
Smart Cities ◽  
Open Data ◽  
Apache Spark ◽  
City Management ◽  
Real Time System ◽  
Data Framework

Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.

scholarly journals Big Data Analytics in Australian Local Government

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 657-675
Author(s):  
Richard B. Watson ◽  
Peter J. Ryan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Local Governments ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
Big Data Analytics ◽  
State Level ◽  
Local Council ◽  
Open Datasets

Australian governments at all three levels—local (council), state, and federal—are beginning to exploit the massive amounts of data they collect through sensors and recording systems. Their aim is to enable Australian communities to benefit from “smart city” initiatives by providing greater efficiencies in their operations and strategic planning. Increasing numbers of datasets are being made freely available to the public. These so-called big data are amenable to data science analysis techniques including machine learning. While there are many cases of data use at the federal and state level, local councils are not taking full advantage of their data for a variety of reasons. This paper reviews the status of open datasets of Australian local governments and reports progress being made in several student and other projects to develop open data web services using machine learning for smart cities.

scholarly journals Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

2021 ◽  
Vol 5 (3) ◽  
pp. 220
Author(s):  
Prakoso Bhairawa Putera ◽  
Rostiena Pasciana
Keyword(s):  
Big Data ◽  
United Kingdom ◽  
Data Privacy ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
The United States ◽  
Scientific Publications ◽  
Productive Author ◽  
The University

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science.

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