Big data analytics in smart cities’ transportation infrastructure modernization

Ushakov, Denis; Dudukalov, Egor; Mironenko, Ekaterina; Shatila, Khodor

doi:10.1016/j.trpro.2022.06.274

Cited by 38 publications

(7 citation statements)

References 8 publications

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“…The construction industry benefits greatly from this disruptive synergy since it radically changes how structures are planned, built, and maintained. IS plays a key part in this shift (Ushakov et al, 2022). IS is the cornerstone of informed choice-making in the context of energy-efficient design for rural homes.…”

Section: Figure 1 Model Of Energy Efficient Housementioning

confidence: 99%

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Hu,

2023

J INFORM SYSTEMS ENG

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The integration of Information Systems (IS), Big Data Analytics (BDA), and Artificial Intelligence (AI) has ushered in a new era of energy-efficient design for rural residences. This study delves into the intricate synergy between technology and sustainability, unveiling the transformative potential of these tools in reshaping rural living spaces. The exploration spans from the conceptualization of designs to their real-world implementation, highlighting the pivotal role of IS in facilitating collaborative efforts among stakeholders. The study further uncovers the power of Big Data Analytics in deciphering energy consumption patterns, climatic variations, and occupant behaviours. These insights lay the groundwork for AI-powered simulations that optimize energy efficiency while ensuring occupant comfort. The study underscores the consequences of ineffective design, elucidating how it amplifies energy consumption, escalates environmental impact, and compromises residents' quality of life. In contrast, the integration of IS, BDA, and AI results in energy-efficient residences, marked by reduced energy usage, enhanced indoor comfort, and economic savings. Despite challenges such as limited resources, harsh climates, and technical expertise gaps, innovative solutions in the form of training programs, data privacy protocols, and collaborations emerge as beacons of progress. Looking to the future, emerging trends like smart grids, Internet of Things (IoT) integration, and AI-driven predictive maintenance shape the narrative of rural residences design. Rural communities stand poised for self-sufficiency and sustainability, empowered by the fusion of technology and ecological mindfulness. The recommendations presented in this study offer actionable insights for construction professionals, policymakers, and researchers, emphasizing interdisciplinary collaboration, continuous monitoring, and ongoing training. Future directions include greater investigation of new trends in sustainability, smart grids, and predictive maintenance, which will help rural communities become self-sufficient and environmentally conscientious.

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Section: Figure 1 Model Of Energy Efficient Housementioning

confidence: 99%

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Hu,

2023

J INFORM SYSTEMS ENG

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“…The extensive study of startup investments, including venture capital and crowdfunding, has resulted from well-known funding challenges faced by startups such as inadequate marketing, skill shortage, inappropriate market niche, and shrinking funds (Bryant, 2022;Gerhardt et al, 2022;Ushakov et al, 2022). Televised Business Pitches (TBPs), where entrepreneurs orally present their ideas to potential investors for resources, expertise, and access, have been proposed as a solution Spinuzzi et al, 2014;Spinuzzi et al, 2015;Sabaj et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Investors of the final pitch on the role of entrepreneurial mentoring and financial investment on a green startup’s business growth

Ranieses,

Talob,

Mizayawa

et al. 2024

BIO Web Conf.

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Televised Business Pitches, such as Dragons' Den and Shark Tank, have gained popularity as entrepreneurs present their ideas to investors in hopes of securing funding. However, the focus of these shows is primarily on the pitching process itself. For this study, it examines the impact of these shows on its participants, specifically assessing their effectiveness in obtaining financial funding and mentoring and their influence on business growth. By analyzing the episodes and conducting semi-structured interviews with four technology startup ventures featured on the local show The Final Pitch, we collected relevant data. We utilized tables to summarize and tabulate the evidence, integrating identified themes into a cross-case report. Results reveal that The Final Pitch was effective in providing funding and mentorship opportunities for green business startups. However, the received mentoring was only a partial contributor to the businesses' longevity and growth. Meanwhile, the funding alleviated financial constraints and facilitated the realization of ideas and green business expansion. Moreover, newly emergent themes also came out such as signaling, an area that can be explored in future research. In conclusion, Televised Business Pitches offer valuable platforms to startups for acquiring financial support and mentorship.

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“…Over the last few years, big data is widely used in both business and public policy decisions [5]. In addition, many institutions around the world use big data statistics in their policies in various fields such as transportation [6], health [7], education [8], agriculture [9], etc. For its part, in recent years, Morocco has launched many initiatives to exploit the opportunities of big data.…”

Section: Introductionmentioning

confidence: 99%

A Web Scraping Framework for Descriptive Analysis of Meteorological Big Data for Decision-Making Purposes

Mhouti¹,

Fahim²,

Soufi³

et al. 2023

IJHIT

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With the large increase in the amount of heterogeneous, complex, and unstructured data issued from Web sources, there is an emerging need to develop Big Data technologies and tools to extract and manage this data. In this context, Web scraping for Big Data is a technique that has gained importance because of its rapidity and efficiency in gathering data for Big Data technology. This study was conducted to propose a Web Scraping framework for descriptive analysis of meteorological Big Data. The introduced framework makes it possible to extract a set of data to process it, present it in a form that is easier to analyze and understand, and use it for decision-making purposes about weather forecasts. The study employed descriptive analysis of available big data as it allows one to easily make quality and effective decisions. The web scraping process takes place in several stages, including data extraction, data archiving in a data warehouse, and finally data filtering and analysis. To test its applicability, the proposed web scraping framework was implemented and tested in the meteorological context to extract and present meteorological data issued from a specialized web source. The proposed system makes it possible to restore the data in the form of statistical models published in a dashboard. The results of the study revealed that the predictive models provided by the system are capable of predicting certain weather-related variables, such as humidity, precipitation, and temperature. The opportunities and implications to leverage the results of this study are many, including weather forecasting and decision support.

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Big data analytics in smart cities’ transportation infrastructure modernization

Cited by 38 publications

References 8 publications

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Investors of the final pitch on the role of entrepreneurial mentoring and financial investment on a green startup’s business growth

A Web Scraping Framework for Descriptive Analysis of Meteorological Big Data for Decision-Making Purposes

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