A Call Graph Reduction based Novel Storage Allocation Scheme for Smart City Applications

Today s world is going to be smart even smarter day by day Smart cities play an important role to make the world smart Thousands of smart city applications are developing in every day Every second very huge amount of data is generated The data need to be managed and stored properly so that information can be extracted using various emerging technologies The main aim of this paper is to propose a storage scheme for data generated by smart city applications A matrix is used which store the information of each adjacency node of each level as well as the weight and frequency of call graph It has been experimentally depicted that the applied algorithm reduces the size of the call graph without changing the basic structure without any loss of information Once the graph is generated from the source code it is stored in the matrix and reduced appropriately using the proposed algorithm The proposed algorithm is also compared to another call graph reduction techniques and it has been experimentally evaluated that the proposed algorithm significantly reduces the graph and store the smart city application data efficientl

Making Sense of the Big Data Mess: Why Interdisciplinarity Matters in Smart Cities

Smart cities use vast amounts of (big) data, often creating what we call an urban "data mess". In this article, we show the diversity and complexity of data that make up this mess and outline examples of urban data processing. Furthermore, we point out problems with the sector-specific perspective that is usually taken when dealing with smart cities. We argue that a collective way of dealing with data across sectors and disciplines needs to be found. To achieve that, we advocate for more interdisciplinary cooperation between different disciplines and stakeholder groups. The Pandemic Recovery Dashboard of the City of Los Angeles gives a first impression of how this could work. We aim to show that approaching data in smart cities from an interdisciplinary angle may help deal with the data mess in smart cities - both for researchers and city developers."Smart Cities" stützen sich auf große Datenmengen ("Big Data") - wobei die unterschiedlichen Daten häufig in ungeordneter Form vorliegen (engl.: "data mess"). Im Beitrag widmen wir uns dieser Diversität im städtischen Datenbestand und skizzieren Beispiele urbaner Datenverarbeitung. Dabei verweisen wir auf Probleme und Herausforderungen einer engen, an einzelne Bereiche gebundenen Datennutzung: Aus unserer Sicht fehlt bislang ein gemeinschaftlicher, sektorübergreifender Ansatz zum Umgang mit Smart-City-Daten. Aus diesem Grund sind mehr interdisziplinäre Kooperationen erforderlich, d.h. die Zusammenarbeit unterschiedlicher Disziplinen und Stakeholder-Gruppen. Das Pandemic Recovery Dashboard der Stadt Los Angeles gibt einen ersten Eindruck davon, wie urbane Daten erfolgreich genutzt werden können. Wir argumentieren dafür, dass Daten in Smart Cities am besten in ganzheitlicher Perspektive bearbeitet und der städtische Datendschungel so übersichtlicher gestaltet werden kann - für Wissenschaft und Praxis

The Design of a Smart City Sonification System Using a Conceptual Blending and Musical Framework, Web Audio and Deep Learning Techniques

Presented at the 26th International Conference on Auditory Display (ICAD 2021) 25-28 June 2021, Virtual conference.This paper describes an auditory display system for smart city data for Dublin City, Ireland. It introduces and describes the different layers of the system and outlines how they operate individually and interact with one another. The system uses a deep learning model called a variational autoencoder to generate musical content to represent data points. Further data-to-sound mappings are introduced via parameter mapping sonification techniques during sound synthesis and post-processing. Conceptual blending and music theory provide frameworks, which govern the design of the system. The paper ends with a discussion of the design process that contextualizes the contribution, highlighting the interdisciplinary nature of the project, which spans data analytics, music composition and human-computer interaction

Big-But-Biased Data Analytics for Air Quality

[Abstract] Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.This research has been supported by MINECO Grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015), both of them through the ERDF; and by CITIC, a Research Centre of the Galician University System financed by the Consellería de Education, Universidades y Formación Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaría Xeral de Universidades (Ref. ED431G 2019/01)Xunta de Galicia; ED431C 2016/015Xunta de Galicia; ED431G 2019/0

AN EXPLORATORY INVESTIGATION OF THE DIGITAL ECONOMY ON FINANCIAL INSTITUTIONS IN PAKISTAN

The delivery of financial services was revolutionized by digitization, which compelled financial institutions to adopt technology that enables them to provide the best appropriate service at a reasonable price. However, due to recent disruption in the country's financial system, financial institutions must incorporate modern technologies to help lower ineffectiveness and improve service quality. This study investigates the impact of digital economy on financial sector competence. According to literature studies, financial organizations in Pakistan are attempting to adopt and integrate digitization into their operations. Given that a generous portion of Pakistan's population is considered digitally illiterate, the number of people who signed up for and used digital financial products was staggering. Therefore, financial institutions must incorporate their business functions with pertinent technology to maintain and improve profitability. Furthermore, they must invest in digital infrastructure and human resource training to incorporate technology in providing services. Policymakers should also strengthen data security and cybercrime laws, policies, and regulations to allow market participants for free and confident operation

Akıllı şehirlerdeki kritik altyapıların siber güvenliği

Smart city is a trending topic that many researchers from different disciplines are interested in. Even though it is supposed to be a study field of public administration, it has also technical dimensions which are focused on by researchers from engineering sciences. On the other hand, there is a security dimension of smart cities which has a boundary that includes multidisciplinary contributions. The security of cities has been an essential issue throughout the ages, but with the emergence of smart cities, the development of internet and communication technologies, and as a consequence of interconnection of critical infra structures in the smart cities, a new dimension of security has been emerged as the headline of security studies. This headline is cyber security. This study aims to investigate cyber security issues in smart cities particularly focusing on critical infrastructures and presents a recommendatory model for providing cyber security of critical infrastructures in smart cities.Akıllı şehir birçok farklı alandan araştırmacıların ilgisini çeken popüler bir konudur. Kamu yönetimi alanında bir çalışma alanı olmasına rağmen, mühendislik bilimlerindeki araştırmacılar tarafından odaklanılan teknik boyutlara da sahiptir. Öte yandan, çok disiplinli katkıları içeren bir sınırı olan akıllı şehirlerin bir de güvenlik boyutu vardır. Şehirlerin güvenliği, çağlar boyunca önemli bir mesele olmuştur, ancak akıllı şehirlerin ortaya çıkması, internet ve iletişim teknolojilerinin gelişimi ve akıllı şehirlerdeki kritik alt yapıların sanal ağlarla birbirlerine bağlanması sonucunda, güvenliğin yeni bir boyutu güvenlik çalışmalarının ana başlığı haline gelmiştir. Bu başlık siber güvenliktir. Bu çalışma, akıllı şehirlerde özellikle kritik altyapılara odaklanan siber güvenlik meselelerini sorgulamayı amaçlamakta ve akıllı şehirlerdeki kritikaltyapıların siber güvenliğini sağlamak için öneri niteliğinde bir model ortaya koymaktadır

The Status of Adoption of Social Media Analytics: Three Cases in South African and German Government Departments

Lack of access to technologies and quality data are key challenges for reducing the digital divide and developing digital citizens to support Smart City initiatives. This paper reviews efforts towards Smart Cities and access to smart technology and Open Data in developed economies globally and in South Africa. Reviews of literature and websites were conducted and the Qualitative Content Analysis method was used to analyse the data. The contributions are the commonalities and differences between Smart City initiatives in developed economies and in South Africa. The findings revealed that in developed countries the focus was mainly on e-services, citizen engagement, Intelligent Transport Systems and energy systems. They provided city-wide connectivity and addressed integration and interoperability challenges. The technologies included large IoT sensors and WiFi in-motion networks incorporating internationally accepted standards. Initiatives in South Africa were less mature, mostly in the initial stages and are not addressing other more urgent needs of the country such as water, food, shelter and education. Collaboration with best practice Smart Cities is needed to provide support to current and future initiatives in South Africa and for the development of African digital citizens

A Guide for selecting big data analytics tools in an organisation

Selection of appropriate big data analytics (BDA) tools (software) for business purposes is increasingly challenging, which sometimes lead to incompatibility with existing technologies. This becomes prohibitive in attempts to execute some functions or activities in an environment. The objective of this study was to propose a model, which can be used to guide the selection of BDA in an organization. The interpretivist approach was employed. Qualitative data was collected and analyzed using the hermeneutics approach. The analysis focused on examining and gaining better understanding of the strengths and weaknesses of the most common BDA tools. The technical and non-technical factors that influence the selection of BDA were identified. Based on which a solution is proposed in the form of a model. The model is intended to guide selection of most appropriate BDA tools in an organization. The model is intended to increase BDA usefulness towards improving organization’s competitiveness

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

FDR2-BD: A fast data reduction recommendation tool for tabular big data classification problems

In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide a smart combination between feature selection to generate dense clusters of data and uniform sampling reduction to keep only a few representative samples from each problem area. Its main advantage is allowing the model’s predictive quality to be kept in a range determined by a user’s threshold. Its robustness is built on a hyper-parametrization process, in which all data are taken into consideration by following a k-fold procedure. Another significant capability is being fast and scalable by using fully optimized parallel operations provided by Apache Spark. An extensive experimental study is performed over 25 big datasets with different characteristics. In most cases, the obtained reduction percentages are above 95%, thus outperforming state-of-the-art solutions such as FCNN_MR that barely reach 70%. The most promising outcome is maintaining the representativeness of the original data information, with quality prediction values around 1% of the baseline.Fil: Basgall, María José. Universidad de Granada; España. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Naiouf, Ricardo Marcelo. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Fernández, Alberto. Universidad de Granada; Españ