1.0 Introduction
1.1 Definition of data infrastructure.
The term “data infrastructure” pertains to the various technical and organizational constituents that facilitate data acquisition, retention, manipulation, interpretation, and dissemination. The domain of information technology comprises the complete set of physical components, computer programs, communication channels, established procedures, accepted norms, and governing principles that are indispensable for facilitating the seamless transmission and effective exploitation of information across and among entities. Comprehending the contextual framework of data infrastructure is imperative for scrutinizing its influence on societal interactions and devising and executing data infrastructure that fosters favorable social consequences. The factors mentioned earlier encompass political and regulatory frameworks, socioeconomic factors, cultural factors, and historical context. Through a comprehensive analysis of social, political, economic, and cultural factors, designers and policymakers can ensure that the development and implementation of these technologies are conducive to advancing social inclusion, equality, and overall well-being.
Servers constitute a crucial element of the physical layer of data infrastructure. They are created to process, manage and store data for various users and organizations. Cloud computing platforms possess the potential to function as hosts for diverse software systems, such as databases, web applications, and other data-dependent applications, which corporations utilize. Hence, the Networks are an essential component of the physical data infrastructure layer (Reisi et al., 2020). These Networks enable the transfer of data between various devices and geographic locations. The Internet, Wide Area Networks (WANs), and Local Area Networks (LANs) are examples of these Networks.
1.2 Significance of data infrastructures in contemporary society
Contemporary society heavily relies on data infrastructure, which facilitates organizations’ collection, storage, analysis, and sharing of data. This process aids in decision-making, innovation, and progress. The primary benefit of data infrastructure lies in its ability to facilitate organizations in gaining valuable insights into their operations, clientele, and markets. For an organization to maintain a competitive advantage, they must be keen on systematic processes like effective data collection and analysis, being on the watch out for repetitive patterns or trends.
Data infrastructure creation allows researchers to find and process considerable amounts of data from different sources. The significance of this matter is particularly notable in disciplines such as genomics, climate science, and astronomy, wherein extensive datasets are employed to substantiate intricate simulations and analyses. Moreover, the significance of data infrastructure is pivotal in public policy and governance. Governments employ data to formulate policies and assess the effectiveness of programs and services. Diverse categories of data, including but not limited to health outcomes, crime rates, and economic indicators, can be utilized as significant resources to inform policymaking and evaluate progress over a specific duration.
In contemporary times, the significance of data infrastructure is progressively escalating due to the generation and dependence of organizations and societies on copious amounts of data. A robust and reliable data infrastructure is essential for organizations to maintain competitiveness and enable informed decision-making. This infrastructure should facilitate efficient storage, analysis, and dissemination of data. The present discourse aims to analyze the fundamental characteristics of data infrastructures and their effects on social interactions.
2.0 Key Features of Data Infrastructures
2.1 Scalability
Understanding the fundamental characteristics of data infrastructures is imperative. The items mentioned above encompass The ability of data infrastructures to accommodate expanding amounts of data and rising demands for data processing and analysis is crucial for scalability. The capacity to seamlessly incorporate additional storage and processing resources without causing any interference with the current systems is imperative (Zhao et al., 2021). The attainment of scalability is facilitated by utilizing technologies such as distributed computing, which enables data processing across numerous nodes or servers. The approach, as mentioned above, facilitates the integration of additional storage and processing resources into data infrastructures in a non-disruptive manner.
The capacity to expand operations is significant for enterprises that handle substantial volumes of data, specifically those operating in the healthcare, finance, and retail sectors. Healthcare providers must store and analyze substantial amounts of patient data, whereas financial institutions manage significant quantities of transactional data.
Scalability caters to the needs of increasing data volumes and guarantees that data infrastructures can satisfy growing requirements for data processing and analysis. In the contemporary data-centric economy, prompt and well-informed decision-making is crucial for organizations that rely on extensive data analysis (Shamim et al., 2019). In general, scalability holds significant importance in data infrastructures as it facilitates efficient handling and examination of substantial volumes of data. Organizations can optimize their ability to leverage data for innovation and growth by implementing data infrastructures that can efficiently integrate additional resources to meet the increasing demands of their business.
2.2 Accessibility
The availability and accessibility of data infrastructures are crucial factors that must be ensured to meet the desired requirements. Ensuring constant accessibility of data necessitates a significant level of reliability and uptime, in addition to redundancy and failover capabilities (Zhao et al., 2021). In order to maintain the uninterrupted availability of data, data infrastructures must exhibit a substantial degree of dependability and operational continuity. The infrastructure must be engineered with redundancy and failover capabilities to guarantee the availability of alternative components in the event of a component failure, thereby ensuring uninterrupted task execution.
A data center may comprise numerous servers and storage devices that are capable of assuming the responsibilities of one another in the event of a malfunction. Regular maintenance and monitoring are necessary for data infrastructures to attain optimal levels of availability. The tasks above encompass the execution of regular maintenance procedures such as updating software, replacing hardware components, and creating backups. Additionally, it involves the continuous monitoring of the infrastructure to identify potential issues and promptly address any arising problems. Accessibility is an important factor to consider for data infrastructures. This means that users will obtain the required information within a reasonable amount of time. Storage of data in an easily accessible format or ensuring safe remote access are methods that organizations can use to attain the mentioned objectives. Data infrastructure must be available and accessible for all organizations relying on data to run their day-to-day activities.
2.3 Security and privacy
Data security is a very important factor in data infrastructures. This calls for implementing risk mitigation strategies to curb insecurities like data breaches and unauthorized access. Adequate measures for authentication and access controls, encryption of confidential information, and monitoring and recording of access and activity are imperative. Implementing robust authentication and access controls is a crucial measure for safeguarding the security of data infrastructures (Zhao et al., 2021). The task at hand pertains to guaranteeing that solely sanctioned individuals are granted entry to the information and that their admittance is suitably limited per their designated roles and obligations within the establishment. Selected authorization, role-based control, and other authentications can be employed to realize this objective.
Data encryption can be implemented to protect sensitive data in data infrastructures. This is where sensitive information is encoded using a cryptographic algorithm to enable only authorized access. This measure ensures that in case of data interception or theft, unauthorized individuals cannot access or utilize the information. Implementing surveillance and documentation of entry and actions are essential to security protocols. The process entails the systematic recording and monitoring of all instances of entry to the data infrastructure, encompassing the user’s identity, the time of access, and the location of origin. The capability above allows entities to identify and scrutinize any dubious conduct or endeavors to breach the data framework by unapproved personnel.
Conducting routine security audits and testing is imperative for upholding the security of data infrastructures. Data infrastructure protection should be prioritized against breaches, unauthorized access, and security. This can be achieved by implementing robust authentication, continuous monitoring, and time-to-time security assessment (Piryonesi & El-Diraby, 2020). Incorporating interoperability within data infrastructures is fundamental as it facilitates the seamless amalgamation and dissemination of data amidst diverse platforms and applications. This underscores the importance of data systems that can seamlessly interface with diverse data sources and systems in a standardized fashion. The task necessitates adherence to established formats and protocols and proficiency in harmonizing data from diverse origins into a unified schema.
2.4 Flexibility
Data infrastructures’ flexibility is important in their ability to facilitate the smooth assimilation of new data sources, applications, and tools as and when necessary. For a smooth integration of data infrastructure and external systems, application programming interfaces (APIs)must be effectively incorporated. Data infrastructures should have the capability to evolve and adjust. The significance of this matter is particularly noteworthy in the current swiftly changing technological scenario, where novel data sources and technologies are persistently surfacing.
Data flexibility in data infrastructure should be enhanced by employing open architectures and APIs. Open architectures’ defining features are their modularity, interoperability, and extensibility capacity. These qualities enable the seamless integration of supplementary data sources, applications, and tools. APIs provide a standardized interface to access and interact with data and applications, enabling efficient data exchange and communication across heterogeneous systems. The ability to modify the magnitude of data infrastructures following evolving requirements is a pivotal aspect of adaptability. The infrastructure must be designed with sufficient flexibility to accommodate fluctuations in demand, such as sudden surges in data volumes or the need to support innovative applications or services (Zhao et al., 2021). The above objective can be accomplished by employing elastic scaling and automatically provisioning supplementary resources to fulfill the demand.
The capacity to adjust to evolving business requirements constitutes a crucial facet of flexibility in data infrastructures. The ability of organizations to remain competitive and meet evolving customer demands necessitates an agile and responsive infrastructure that facilitates rapid and effortless adaptation of data management strategies (Piryonesi & El-Diraby, 2020). Flexibility is a crucial element of data infrastructures, enabling organizations to seamlessly integrate new data sources, applications, and tools as required. Flexible data infrastructure necessitates crucial elements such as open architectures and APIs, elastic scaling, and adaptability to dynamic business requirements. Effective management of data infrastructures necessitates sound governance, which can be facilitated by enforcing policies and protocols that ensure data accuracy, validity, and consistency. Implementing data management frameworks, data standardization, and data quality controls is crucial in guaranteeing the precision and authenticity of data. The attributes above are essential for data infrastructures to furnish the requisite security, dependability, and flexibility to facilitate uninterrupted progress and guarantee their ability to meet the demands of modern enterprises and societies adequately.
2.5 Transparency
Effective implementation of data infrastructures is important in obtaining better governance, ensuring trustworthiness and coherence, and avoiding harming anyone. The effects of data infrastructures on interpersonal relationships are diverse, with pros and cons. Diverse entities like governmental and corporate institutions can benefit from heightened transparency and responsibility by leveraging data infrastructures. Furthermore, they have the potential to improve the decision-making process through the provision of a more diverse range of information, the expansion of service accessibility, and the promotion of greater participation in democratic processes (Shamim et al., 2019). Data infrastructures have the potential to sustain prevailing power differentials and bolster societal disparities. Smart devices may present substantial privacy and security hazards, given that they can gather, scrutinize, and potentially exploit personal information by malicious individuals or entities.
Generally, it is crucial to guarantee that data infrastructures are governed proficiently and conscientiously, emphasizing the advancement of transparency, accountability, and equity. The establishment of sturdy data management frameworks, strict adherence to data standards, and the execution of data quality controls are necessary to guarantee the dependability and authenticity of data. Through this approach, data infrastructures can be a fundamental basis for promoting constructive societal transformation and bolstering the progress and growth of organizations and localities.
3.0 Impacts of Data Infrastructures on Social Relations
3.1 Power relation
Societal power structures can either be upheld or undermined by data infrastructures depending on their mode of creation and use. These are some ways in which data infrastructure may cause effects power dynamics: It can allow for easier for major actors like big firms and government to dominate power. The ability of these individuals to analyze data allows them a more strategic advantage over their adversaries. Data infrastructures facilitate the democratization of power by granting access to data and resources that were previously inaccessible to individuals and marginalized groups. The advent of social media platforms and other digital tools has enabled citizen journalism and grassroots movements freedom, providing individuals with the means to challenge the authority of traditional media and political elites (Piryonesi & El-Diraby, 2020). Data infrastructures can amplify existing biases, which can also contribute to social injustices and biases. For instance, the algorithms used to make employment or loan decisions might reflect historical discriminating tendencies, perpetuating current imbalances. Lowering entrance barriers: Data infrastructures can also make it easier for new social actors, like entrepreneurs or small enterprises, to enter the market.
Data infrastructures can promote more accountability and transparency in society by making it easier to monitor the actions of powerful individuals and hold them accountable. For instance, open data efforts and digital platforms can help citizens keep tabs on government expenditures and hold decision-makers responsible. Therefore, how these technologies are developed and applied will determine how power relations in society are affected by data infrastructures. Data infrastructures have the potential to advance more social equality and inclusivity by encouraging greater openness, accountability, and democratization of power. However, these technologies could increase social inequality and Exclusion if they reinforce existing power disparities and biases.
3.2 Democratic processes
Data infrastructures have the potential to impact democratic processes, including political engagement and decision-making, positively and, therefore, greatly benefiting society. Numerous examples highlight the positive influence of data infrastructures on democratic processes, including the following; The utilization of data infrastructures can help increase voter turnout during elections and encourage more people to participate in the political process in China. Digital platforms like social media and mobile apps have the potential to encourage voter registration and share important information about voting in terms of locations and candidates. It is exciting to consider the potential positive impact of data infrastructures on fundraising and campaign financing in political campaigns. Political candidates can acquire significant financial resources from individual donors through online fundraising tools and social media marketing (Reisi et al., 2020). This could lead to greater independence from influential donors and interest groups.
Using data infrastructures presents an opportunity to gauge the general public’s sentiment toward political matters and aspirants. By employing online surveys and social media platforms, one can gain important information from the public, which is of imperative importance in creating political policies.
Using online surveys and social media platforms presents an exciting opportunity to gather valuable insights from the general public, which could positively influence the creation of political policies. Using data infrastructures has great potential to improve evidence-based decision-making by the government, leading to more effective and efficient policies. The use of predictive modeling and data analytics has the potential to accurately pinpoint specific areas of concern, making it easier to create focused interventions (Himanen et al., 2019). While there may be privacy and security concerns, using data infrastructures in democratic procedures can bring about positive changes. Using personal information for political advertising or voter targeting may elicit privacy apprehension. In contrast, the possibility of cyber assaults on voting machines or other digital systems may elicit security concerns.
The design and utilization of data infrastructures can positively impact democratic processes. Data infrastructures have the potential to positively impact democratic institutions and increase citizen participation by promoting transparency, accountability, and inclusivity in political decision-making (Reisi et al., 2020). Although there is a possibility that these technologies could be misused to manipulate public opinion or interfere with democratic processes, we can take steps to prevent such outcomes and maintain trust in our political system
3.3 Economic relations
The potential impact of data infrastructures on economic relations is noteworthy, as it can reduce economic inequality and enhance opportunities for all. The positive impact of data infrastructures on economic relations is an exciting phenomenon demonstrated by various examples. Leveraging data infrastructures presents a significant prospect for enhancing economic expansion and fostering ingenuity by facilitating novel business models, commodities, and amenities. Incorporating big data analytics and artificial intelligence can yield novel findings and enhanced decision-making capabilities for enterprises, culminating in heightened efficiency and competitiveness.
Implementing data infrastructures can generate novel employment prospects and revolutionize labor practices. The deployment of automation and digital technologies possesses the capacity to generate novel prospects in domains such as data analytics and cybersecurity, despite the possibility of job displacement in some areas. The implementation of data infrastructures holds promise in addressing economic inequality by lessening pre-existing discrepancies in wealth and opportunity. Despite apprehensions regarding the concentration of data and resources within a limited number of dominant entities, it is possible to strive towards discovering resolutions that foster financial parity for society (Shamim et al., 2019). The employment of digital platforms and other technological resources has the potential to create promising prospects for entrepreneurship and economic expansion.
Implementing data infrastructures can enable novel lending and risk assessment methods, ultimately enhancing the availability of credit and financial services. Using alternative data sources, such as social media and mobile phone activity, can aid in evaluating the creditworthiness of individuals needing a conventional credit background. This could potentially enhance credit accessibility for marginalized communities. Data infrastructures can influence the ownership of innovation and intellectual property. The utilization of open data and open innovation platforms has the potential to facilitate increased collaboration and idea sharing, which may result in accelerated innovation and heightened economic advancement. However, this approach also raises inquiries regarding ownership and intellectual property rights.
The influence of data infrastructures on economic relations is contingent upon their design and utilization. By promoting increased innovation, accessibility, and economic mobility, data infrastructures can potentially improve economic systems and reduce economic inequality. These technologies can worsen economic inequality and marginalization if they reinforce already-existing power gaps and economic disparities.
3.4 Social inclusion and Exclusion
Data infrastructures can impact social inclusion and Exclusion by altering the accessibility of social networks and resources. You may see how data infrastructures affect social inclusion and Exclusion by looking at several instances. Data infrastructures are to blame for a digital gap since they may lead to uneven access to social networks and digital resources for various demographic groups. The existence of certain factors, such as geography, geography, and money, may have an impact on the accessibility of technology and broadband internet.
By enabling new forms of engagement and communication, data infrastructures’ effects on social networks and relationships may be seen. Particularly, the installation of such infrastructures may have an impact on social networks (Fu & Soman, 2020). Social media platforms may help people connect with others with similar interests and experiences, potentially promoting social inclusion. Despite their potential advantages, these platforms may also help to reinforce existing social hierarchies and isolate some groups of people, such as those who are less tech-savvy or who experience cyberbullying and harassment.
The accessibility of resources, including but not limited to healthcare, education, and career opportunities, can be impacted by the availability of data infrastructures. By enabling more specialized and targeted services, data analytics and machine learning can potentially improve the availability of resources for underserved groups (Shamim et al., 2019). However, it is crucial to remember that these technologies can magnify already-present prejudices and discriminatory behaviors, leading to greater Exclusion and marginalization.
Data infrastructures’ possible privacy and security ramifications may impact social inclusion and Exclusion. The collection and use of personal data have the potential to provide tailored facilities, but it also runs the risk of increasing monitoring and compromising anonymity. Using digital platforms could improve social relationships but also increase the chance of running into cyberbullying online harassment, and other types of digital damage. Data infrastructures’ impact on social inclusion and Exclusion depends on their use and design. By enabling increased access, connection, and inclusion, data infrastructures have the potential to improve social networks and lessen social isolation. These technologies can worsen social Exclusion and marginalization if they reinforce pre-existing power disparities and societal structures.
3.5 Cultural values and Norms
Data infrastructures can impact social inclusion and Exclusion by altering the accessibility of social networks and resources. Data infrastructures affect social inclusion and Exclusion by looking at several instances. Data infrastructures are to blame for a digital gap since they may lead to uneven access to social networks and digital resources for various demographic groups. The existence of certain factors, such as geography, geography, and money, may have an impact on the accessibility of technology and broadband internet.
By enabling new forms of engagement and communication, data infrastructures’ effects on social networks and relationships may be seen. Particularly, the installation of such infrastructures may have an impact on social networks. Social media platforms may help people connect with others with similar interests and experiences, potentially promoting social inclusion. Despite their potential advantages, these platforms may also help to reinforce existing social hierarchies and isolate some groups of people, such as those who are less tech-savvy or who experience cyberbullying and harassment. The accessibility of resources, including but not limited to healthcare, education, and career opportunities, can be impacted by the availability of data infrastructures. By enabling more specialized and targeted services, data analytics and machine learning can potentially improve the availability of resources for underserved groups. However, it is crucial to remember that these technologies can magnify already-present prejudices and discriminatory behaviors, leading to greater Exclusion and marginalization.
Data infrastructures’ possible privacy and security ramifications may impact social inclusion and Exclusion. The collection and use of personal data have the potential to provide tailored facilities, but it also runs the risk of increasing monitoring and compromising anonymity. Using digital platforms could improve social relationships but also increase the chance of running into cyberbullying online harassment, and other types of digital damage. Data infrastructures’ impact on social inclusion and Exclusion depends on their use and design. By enabling increased access, connection, and inclusion, data infrastructures can improve social networks and lessen social isolation (Shamim et al., 2019). These technologies can worsen social Exclusion and marginalization if they reinforce pre-existing power disparities and societal structures.
4.0 Surveillance and social control in China
The Chinese government is taking various measures to ensure its citizens’ safety and security, including using advanced technologies like facial recognition, biometric data collection, and mobile phone monitoring. The data mentioned is used to keep track of citizens’ movements and activities and to identify individuals who may pose a threat to the state. The Chinese government’s employment of the social credit system is a notable illustration, wherein a numerical value is assigned to every individual based on their conduct and engagements. The metric above is utilized to ascertain eligibility for a range of amenities and assets, including but not limited to travel permits, financial advances, and employment prospects (Ahmad et al., 2021).
Individuals who obtain lower scores may face limitations on mobility and engagement in various activities and could be denied access to specific services. China’s use of surveillance and social control can be seen through its management of ethnic and religious minority populations, particularly in the region of Xinjiang. The Chinese government is implementing monitoring and control measures on the Uighur Muslim population in Xinjiang, which involve facial recognition technology, biometric data collection, and mobile phone tracking (Mehmood et al., 2020). As per documented accounts, a considerable proportion of individuals belonging to the Uighur community have been detained and subjected to confinement in facilities designated for “re-education” purposes. These individuals are reportedly subjected to political indoctrination and various forms of maltreatment.
Critics have scrutinized the potential implications of China’s surveillance and social control measures on human rights and civil liberties. The measures above have faced criticism due to their perceived infringement upon the privacy of citizens, curtailment of freedom of expression, and imposition of limitations on individual mobility and agency. Certain people are concerned about the possibilities of using these tactics for political oppression and social management, particularly within China’s single-party political system.
5.0 Social media and political polarization In the United States
Social media has been chastised for its role in spreading propaganda and disinformation, particularly during political elections. The extensive use of social media has aided the quick spreading of false or misleading information. At the same time, political campaigns may utilize customized messaging and advertising to achieve their goals and appeal to certain groups. Furthermore, the growth of social media has aided the spread of fringe and extremist viewpoints, possibly worsening political polarization and causing civil upheaval. Extremist groups such as QAnon and the Proud Boys, which use social media platforms to organize and further their goals, have grown in popularity.
The impact of social media on the political landscape of the United States has been significant, and it is believed that these impacts will remain and fluctuate in reaction to technological improvements and societal changes (Mehmood et al., 2020). To reduce political polarization, individuals must be aware of the possible risks and downsides of using social media platforms and actively seek out alternative opinions and sources of information.
6.0 Smart city initiatives and urban governance in Europe
Smart city initiatives are gaining popularity in Europe as towns seek to use technology and data to enhance local administration and the quality of life for their citizens. Sensors, data analytics, and other technologies are frequently used in these initiatives to collect and analyze data on several urban challenges, such as traffic flow, energy use, and air quality. One of the primary benefits of smart city projects is that they can promote more effective and efficient urban administration. City governments may better understand their inhabitants’ needs and preferences and use this information to develop more effective policies and programs by collecting and analyzing data on several urban issues. For example, a city may use traffic flow data to improve the timing of traffic lights or air quality data to implement pollution-reduction initiatives.
Civic involvement and public engagement may be increased through smart city projects. By utilizing digital platforms and tools for data collection and sharing, city governments may enable residents to participate in decision-making processes and give input on policies and initiatives. This might promote more transparency and accountability in urban governance, improving the efficacy and variety of policymaking. Smart city initiatives, on the other hand, may be fraught with dangers and challenges (Mehmood et al., 2020). Data collection and usage may raise privacy and security issues, particularly if people’s personal information is collected without their knowledge or is used in mysterious ways. If smart city activities are not designed and managed inclusively and fairly, there is also concern that they worsen pre-existing socioeconomic imbalances. By leveraging data and technology to improve citizen quality of life and create more inclusive and effective policymaking, smart city initiatives have the potential to reshape urban government in Europe profoundly. Local governments must address the possible hazards and problems involved with these initiatives to ensure that they are planned and carried out transparently and inclusively and that they respect individuals’ rights and privacy.
7.0 Conclusion
The phrase “data infrastructure” refers to a collection of components such as hardware, software, communication channels, protocols, standards, and regulatory frameworks that help with various elements of data management, such as capture, storage, processing, analysis, and sharing. The classification is divided into three main dimensions: physical, intellectual, and social. Storage devices, servers, and networks comprise the physical layer, while databases and data warehouses comprise the logical layer. Designers and policymakers can support the increase of social integration, parity, and well-being by deploying new technologies by performing a complete examination of social, political, economic, and cultural aspects. Implementing data infrastructure allows businesses to get significant insights into their business operations, consumer behavior, and market trends (Mehmood et al., 2020). “data infrastructure” refers to hardware, software, communication channels, protocols, standards, and regulatory frameworks that operate together to support data collecting, storage, processing, analysis, and sharing. The classification has three dimensions: physical, intellectual, and social. The physical layer includes hardware components like storage devices, servers, and networks, whereas the logical layer includes software components like databases and data warehouses. Designers and policymakers can support the increase of social integration, parity, and well-being by deploying new technologies by examining social, political, economic, and cultural aspects. Implementing data infrastructure makes gaining useful insights about a company’s operations, customers, and markets easier.
References
Fu, Y., & Soman, C. (2021, June). Real-time data infrastructure at Uber. In Proceedings of the 2021 International Conference on Management of Data (pp. 2503–2516).
Reisi, M., Sabri, S., Agunbiade, M., Rajabifard, A., Chen, Y., Kalantari, M., … & Li, Y. (2020). Transport sustainability indicators for an enhanced urban analytics data infrastructure. Sustainable Cities and Society, 59, 102095.
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data‐driven materials science: status, challenges, and perspectives. Advanced Science, 6(21), 1900808.
Ahmad, U., Alsaqer, M., Alelyani, S., Safder, I., Iqbal, S., Hassan, S. U., & Aljohani, N. R. (2021). Big data infrastructure: data mining, text mining, and citation context analysis in scientific literature. In Artificial Intelligence and Big Data Analytics for Smart Healthcare (pp. 25-46). Academic Press.
Zhao, X. G., Zhou, K., Xing, B., Zhao, R., Luo, S., Li, T., … & Zhang, L. (2021). JAMIE: an artificial-intelligence aided data-driven infrastructure for computational materials informatics. Science Bulletin, 66(19), 1973-1985.
Piryonesi, S. M., & El-Diraby, T. E. (2020). Role of data analytics in infrastructure asset management: Overcoming data size and quality problems. Journal of Transportation Engineering, Part B: Pavements, 146(2), 04020022.
Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135.
Mehmood, R., Katib, S. S. I., & Chlamtac, I. (2020). Smart infrastructure and applications. Berlin/Heidelberg, Germany: Springer International Publishing.
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