Abstract:Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies… Show more
“…Results for women: R = (4, 7, 3, 5, 1, 2, 8, 11, 6, 10, 9) N = (4, 9, 3, 6, 1, 2, 8, 11, 5, 10, 7), F = (4, 6, 2, 8, 1, 3, 9, 10, 5, 11, 7), R = (6, 4, 7, 6, 9, 8, 3, 1, 6, 2, 3) N = (8, 6,8,7,9,9,7,4,8,4,7) F = (9, 9,10,8,10,9,8,7,9,7,9).…”
Section: Prioritisation Results For Lithuanian Data Setmentioning
confidence: 99%
“…One of the most critical tasks in processing and analysing large volumes of information is to meet the needs of the target audience [4,5]. Meeting the needs of the target audience is accompanied by an increase in the volume of information [6], and the increase in the volume of information leads to new challenges for information providers and consumers [7]. Collecting and sending information to the end user determines its quantitative and qualitative changes.…”
This article presents the methodology and tools to evaluate the reliability of quantitative sociological research data. The problem of filtering unreliable data is usually solved by statistical methods. This article proposes an improved method for filtering unreliable data. In this case, the statistical methods are not applied to the initial data but the value of the distance function between the two preferences. This allows for the disclosure of conflicting or erroneous data. Calculation of the distance between two preferences and prioritisation of life goals are based on binary relation theory, where the properties of symmetry (antisymmetry) are very important. The article presents a case study on 11 life goals evaluation and ranking by Lithuanian and China students. The study revealed that the China student data filtered at least twice as much as the Lithuanian student data, i.e., they are less reliable. The filtered data show that students of both countries ranked the most and the least important life goals in a very similar way with minimum deviations detected in the ranking results.
“…Results for women: R = (4, 7, 3, 5, 1, 2, 8, 11, 6, 10, 9) N = (4, 9, 3, 6, 1, 2, 8, 11, 5, 10, 7), F = (4, 6, 2, 8, 1, 3, 9, 10, 5, 11, 7), R = (6, 4, 7, 6, 9, 8, 3, 1, 6, 2, 3) N = (8, 6,8,7,9,9,7,4,8,4,7) F = (9, 9,10,8,10,9,8,7,9,7,9).…”
Section: Prioritisation Results For Lithuanian Data Setmentioning
confidence: 99%
“…One of the most critical tasks in processing and analysing large volumes of information is to meet the needs of the target audience [4,5]. Meeting the needs of the target audience is accompanied by an increase in the volume of information [6], and the increase in the volume of information leads to new challenges for information providers and consumers [7]. Collecting and sending information to the end user determines its quantitative and qualitative changes.…”
This article presents the methodology and tools to evaluate the reliability of quantitative sociological research data. The problem of filtering unreliable data is usually solved by statistical methods. This article proposes an improved method for filtering unreliable data. In this case, the statistical methods are not applied to the initial data but the value of the distance function between the two preferences. This allows for the disclosure of conflicting or erroneous data. Calculation of the distance between two preferences and prioritisation of life goals are based on binary relation theory, where the properties of symmetry (antisymmetry) are very important. The article presents a case study on 11 life goals evaluation and ranking by Lithuanian and China students. The study revealed that the China student data filtered at least twice as much as the Lithuanian student data, i.e., they are less reliable. The filtered data show that students of both countries ranked the most and the least important life goals in a very similar way with minimum deviations detected in the ranking results.
“…The availability of such a quantity of information in smart city actually leads to a question: what about their actual usefulness? There are valuable examples of analysis based on big data and IoT as reviews in [ 37 ]. However, is possible to turn its cost into value for resilience-based decisions?…”
Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.
“…11, No. 6, December 2021 : 5099 -5106 5100 already exceeded 5 billion, and it is estimated that this number will reach 50 billion in the next decade, considering the proliferation of the internet of things (IoT) sensors [5]- [8]. On the other hand, non-ionizing radiation is being emitted by other telecommunication systems with technologies other than 2G, 3G, 4G and 5G, which although, as [9] says, are fantastic for IoT solutions, they present a disadvantage in terms of cost, and therefore lower cost systems are required for which new companies can emerge with this type of technology.…”
<span lang="EN-US">Currently, telecommunications systems have become more widespread and there is still a discrepancy between whether or not non-ionizing radiation produces health problems in living beings at cellular level. From an experimental point of view, it is interesting to raise the correlation of high levels of electromagnetic pollution with health problems in urban populations which would make it possible to clearly determine the effects of this type of radiation on human health and the environment. By means of remote sensing, a geographic information system (GIS) has been developed for the analysis of electromagnetic pollution levels generated by emissions from non-ionizing radiation (NIR) sources in a city. A method for measuring electromagnetic pollution was applied, which allows the generation of a table of attributes of the GIS that is the input to generate by inverse distance weighting (IDW), the layer of electromagnetic pollution. The method, as a case study, was applied in the city of Manizales, located in Colombia, obtaining as a result a layer that allows evidence that the highest levels of electromagnetic pollution are concentrated in the most central area of the city. In this way, the effects of NIR on public health can be analyzed by means of correlations.</span>
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