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Paper Title (use style: paper title) Doi: 10.48161/issn.2709-8206 This is an open access article distributed under the Creative Commons Attribution License 91 A Survey of Data Mining Implementation in Smart City Applications 1st Zainab Salih Ageed Translation Dept. Nawroz University Duhok, Iraq
[email protected] 2nd Subhi R. M. Zeebaree Culture Center Duhok Polytechnic University Duhok, Iraq
[email protected] 3rd Mohammed A. M.Sadeeq Quality Assurance Duhok Polytechnic University Duhok, Iraq
[email protected] 4th Shakir Fattah Kak Information Technology Dept. Duhok Polytechnic University Duhok -Iraq
[email protected] 5th Zryan Najat Rashid Computer Network Dept. Sulaimani Polytechnic University Sulaimani-Iraq
[email protected] 6th Azar Abid Salih Information Technology Management Dept. Duhok Polytechnic University Duhok -Iraq
[email protected] 7th Wafaa M. Abdullah Computer Science Dept. Nawroz University Duhok, Iraq
[email protected] https://doi.org/10.48161/qaj.v1n2a52 Abstract— Many policymakers envisage using a community model and Big Data technology to achieve the sustainability demanded by intelligent city components and raise living standards. Smart cities use different technology to make their residents more successful in their health, housing, electricity, learning, and water supplies. This involves reducing prices and the utilization of resources and communicating more effectively and creatively for our employees. Extensive data analysis is a comparatively modern technology that is capable of expanding intelligent urban facilities. Digital extraction has resulted in the processing of large volumes of data that can be used in several valuable areas since digitalization is an essential part of daily life. In many businesses and utility domains, including the intelligent urban domain, successful exploitation and multiple data use is critical. This paper examines how big data can be used for more innovative societies. It explores the possibilities, challenges, and benefits of applying big data systems in intelligent cities and compares and contrasts different intelligent cities and big data ideas. It also seeks to define criteria for the creation of big data applications for innovative city services. Keywords— Data Computing, Internet of Things, Data Mining, Smart City, Cloud Computing. I. INTRODUCTION The Internet has experienced immense growth in recent years, and its content is continually growing and extending. The Internet of Things is viewed as an Internet application creation [1]. This means that customers, customers, paper, objects, and paper are all linked through contact and exchange of information, but the Internet is central to IoT [2]. The International Telecommunications Union Internet Report provides the following definition of IoT: The connection between the objects and the Internet and the interaction and communication of information through different protocols is achieved through various kinds of sensor systems to achieve an intelligent network identity, location, management, and control [3]. It has three different features: Intelligent Twitter sharing in real-time [4]. There have also been many studies in this area on innovative and wired companies' needs. There have been many facts, such as fixed and mobile sensors, internet data, and social data, from several outlets [5, 6]. Just a few of the data collection fields include agriculture, civic infrastructure, catastrophe management, education and apprehension, electricity, the efficiency of the environment, health and wellbeing, including medical, resilience, welfare, social services, telecommunications, transport, and mobility [7]. In several ways, a Smart City should be able (including Big-Data, traditional data sources, and personal information for users) to retrieve historical and real-time data from a wide range of sources [8]. The paper structure: have the Background Theory in Section II, Literature Review In Section III, discussion in Section IV, the conclusion in section V. mailto:
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[email protected] https://doi.org/10.48161/qaj.v1n2a52 92 II. BACKGROUND THEORY A. Data Mining Significant figures are explosive, with an expected annual increase in global data production of 40% compared to just 5 percent in global IT expenditure. About 90% of digitized data worldwide have been registered over the past two years [9]. As a result, many municipalities worldwide have started using big data to help intelligent cities develop and sustain [10]. By recognizing their key smart city features, the cities have retained standards, values, and specifications for innovative city applications. Sustainability, long service cycle, governance, improved quality of life, and smart use of natural and urban resources comprise these qualities [11]. Smart City's well-defined components are mobility, governance, climate, people, and applications and services such as healthcare, transport, smart education, and electricity [12]. Sustainability, longevity, management, more outstanding living standards, and intelligent utilization of natural and urban resources are all virtues. The smart city is well developed in mobility, governance, atmosphere, inhabitants, wellbeing, mobility, smart education, and energy facilities [13, 14]. Big data has developed into a strategic weapon of immense potential importance that promotes industry's upgrade and growth as a critical driver for advancement [15]. It also affects science and methodology. Big data offers many benefits, including a large pool of capital and specialized training measurement technology [16]. As a result of large, complex, and volatile data, the storage and computational bottlenecks impede conventional data processing systems [17]. Working environments improved with time and resolved a range of measurement problems, including administrative functions at a high level, program upgrades, and the use of other computer series [18]. Big data mining is a service that collects the essential information and expertise from and provides the customer with a large, complex, competitive, high-volume, and low- density data set [19]. It helps to find valuable knowledge and expertise instead of traditional data mining. There are, however, technological, historical shortcomings, data environments, and mining scope [20]. The diagram below shows the layout of big data based on data mining techniques. The three levels of the architecture are networks, operating layers, and facility supports [21]: 1) Layer platform: The integration of big mixed data with a range of support technologies dependent on cloud infrastructure can support big data mining. The integration of big mixed data with a range of cloud computing support technologies can support them. Big data mining is also supported [22]. This cloud environment can not only provide the rest of the world with information, hardware, and software. However, it can also quantify moving data to allow more efficient preprocessing, analysis, and mining of complex data in several sources [23]. 2) Functional layer: this layer can interpret and dig out data depending on users' requirements. The high efficiency of storages and computers made available to users as visuals, data sources, and other high-scalabilité and expandable technologies is essential for scientific, mining, and other tools [24]. 3) Application Level Layers: Big data mining communicates automatically with customers, service providers, and users. The mining results lead to preprocessing, analysis, and extraction from various dynamic data sources [25]. B. Smart City and Cloud City and metropolitan areas are abstruse social ecosystems, like local government, residents, and organizations [26]. The ICT is becoming progressively facilitating and enabling the ICTs to meet particular criteria relating to key themes, including enterprise and job growth, economic development, energy and water, public security, the atmosphere, health care, education, and public services. Simultaneously, urban spending is gradually being pressured by the new tumultuous global economic crisis, which is causing disastrous impacts not only on maintaining and upgrading existing ICT infrastructures and facilities but also on future innovation policies [27]. However, it has been identified, as an exemplary example of an answer to current and future complex challenges of the resource efficiencies, reducing emissions, sustainable health care services for older people, strengthening young people, and integral cities, which can be used for intelligent cities, information cities, digital towns, e-cities, and virtual towns. Smart cities, with their cameras, built-in computers, vast data Fig. 1. Smart city G-Cloud platform [28]. Fig. 2. Pilot smart city G-Cloud project [28]. 93 Sets and knowledge, and responses have been characterized to generate a specific spatial intelligence and creativity [29]. It proposes a comprehensive concept of a smart city that embodies a city that fosters sustainable economic development and high quality of life through investment in human and social capital, transportation and modern ICT infrastructure, and sound management of natural resources by participatory governments [30]. The requirement for investing in modern ICT and participatory government requirements include, in particular, the concept of empowering cities [31]. A form of democratic innovation that first becomes popular with the increasing capacity of businesses and consumers, using software products and services, t Two of the main issues highlighted in the definition mentioned above [32]. Propose a typology that defines the traditional roles inherent in the intelligent City that include smart market (competitiveness), intelligent citizens (social and human capital); intelligent administration (intervention); smart mobility (transport and ICTs) (quality of life) [33]. It is possible to use creative ICT to build sustainable strategies that cut expense, concentrate attention on top public challenges and align groups with common agendas by providing visual and innovative leadership [34]. The role of innovation facilitators in vital sectors such as industry, health, the environment, and ICT is played by cities worldwide. In their administration of daily public utilities, city councils look for bright, cost-efficient ICT strategies [35]. City and urban policymakers will use advanced low- cost technology tools such as cloud analytics to analyze data and market indicators for effective decision-making and predict problems to