Turning big data into personalised diabetes care

The public sector, private firms, business community, and civil society are generating data that are high in volume, veracity, and velocity and come from a diversity of sources. This type of data is today known as big data. Public administrations pursue big data as “new oil” and implement data-centric policies to collect, generate, process, share, exploit, and protect data for promoting good governance, transparency, innovative digital services, and citizens’ engagement in public policy. All of the above constitute the Government Big Data Ecosystem (GBDE). Despite the great interest in this ecosystem, there is a lack of clear definitions, the various important types of government data remain vague, the different actors and their roles are not well defined, while the impact in key public administration sectors is not yet deeply understood and assessed. Such research and literature gaps impose a crucial obstacle for a better understanding of the prospects and nascent issues in exploiting GBDE. With this study, we aim to start filling the above-mentioned gaps by organizing our findings from an extended Systematic Literature Review into a framework to organise and address the above-mentioned challenges. Our goal is to contribute in this fast-evolving area by bringing some clarity and establishing common understanding around key elements of the emerging GBDE.

Big data are extremely large data sets that are so complex and unorganized. These large data sets are analyzed computationally to reveal patterns, trends related to human behavior and interactions. The three types of data are structured, semi-structured, and unstructured. Every big data has played a vital role in fighting COVID-19. Many countries used big data that helped them to reduce cross-infections, allowing the countries to get back to work and stabilize their economy. Big data was used to collect information for different other types of health crises also earlier. Artificial intelligence and machine learning involve many other types of novel data like images, movies, audio, and so many other things that don’t fit into standard relational databases; that’s the variety of big data where AI has been so useful. There are a lot of data science tools to help in analysis. Windows Azure is a Spark and Hadoop service in the cloud. It offers good enterprise-grade security.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The technological advancements in the field of computing are giving rise to the generation of gigantic volumes of data which are beyond the handling capabilities of the conventionally available tools, techniques and systems. These types of data are known as big data. Moreover with the emergence of Internet of Things (IoT), these types of data have increased in multiple folds in 7Vs (volume, variety, veracity, value, variability, velocity and visualisation). There are several techniques prevalent in today’s time for handling these types of huge data. Hadoop is one such open source framework which has emerged as a de facto technology for handling such huge datasets. In an IoT ecosystem, real-time handling of requests is an imperative requirement; however, Hadoop has certain limitations while handling these types of requests. In this article, we present an energy-efficient architecture for effective, secured and real-time handling of IoT big data. The proposed approach adopts atrain distributed system (ADS) to construct the core architecture. This study uses software-defined networking (SDN) framework for energy-efficient and optimal routing of data and requests from source to destination, and vice versa. Furthermore, to ensure secured handling of IoT big data, the proposed approach uses ‘Twofish’ cryptographic technique for encrypting the information captured by the sensors. Finally, the concept of ‘request-type’ identifying unit has been proposed. Instead of handling all the requests in an identical way, the proposed approach works by characterising the requests on the basis of certain criteria and parameters, which are identified here.

Disclosure: B. Ozturk: None. R. Ferraz-Bannitz: None. V. Efthymiou: None. H. Saifeldin, MD: None. M. Patti: Consulting Fee; Self; AstraZeneca, Hamni, MBX Biosciences. Grant Recipient; Self; Dexcom. Other; Self; data and safety monitoring board for Fractyl Health. Introduction: Paternal metabolic phenotypes prior to conception can influence metabolic health of offspring. We previously demonstrated treatment of mice with diet-induced obesity with canagliflozin (SGLT2i) or caloric restriction prior to breeding reduces adiposity, glucose intolerance, and hepatic steatosis in adult male offspring (9 months). To identify potential molecular contributors to this paternally-mediated intergenerational disease risk in later life, we analyzed the liver transcriptome and lipidome of young adult offspring. Method: C57BL/6J male mice, age 8 weeks, were treated with low fat (10% fat) or high fat diet (HFD, 60%) to induce obesity/hyperglycemia; after 5 weeks, HFD mice were randomized to HFD, HFD with canagliflozin (25 mg/kg/day) (HFD-CANA) or 15-30% caloric restriction (HFD-CR). HFD-CANA and HFD-CR had lower weight and glucose vs. HFD (P<0.05). After 5 weeks of treatment, males were bred with chow-fed females, yielding 4 groups of F1 generation offspring (pLFD, pHFD, pHFD-CANA, pHFD-CR). F1 were fed chow at weaning and sacrificed at 4 months. Results: pHFD offspring have greater hepatic steatosis vs. pLFD, partially reversed in pHFD CANA and pHFD-CR, as determined by histology and total lipid weight. To identify potential molecular mediators of differential steatosis, we analyzed the liver transcriptome (RNA-seq) in offspring. Gene ontology analysis (ClusterFinder, padj<0.05) revealed genes downregulated in pHFD vs. pLFD (n=495) were enriched for lipid localization/transport and phospholipid and carbohydrate metabolism. Strikingly, genes upregulated in pHFD-CANA (n=449) and pHFD CR (n=875) vs. pHFD were also enriched for lipid localization/transport and fatty acid and steroid metabolism. 20% and 33% of genes downregulated in pHFD vs. pLFD were reversed in pHFD-CANA and pHFD-CR, respectively. Reversed genes were enriched for lipid and mitochondrial/peroxisomal fatty acid metabolism. ChIP Enrichment Analysis of reversed genes indicated Ppara, Lxr and Rxr as potential upstream regulators. Parallel lipidomic analysis using mass-spectrometry revealed upregulation of multiple ceramide species in pHFD vs. pLFD, with downregulation in pHFD-CANA and pHFD-CR vs. p-HFD. Top lipids altered in pHFD were reversed in pHFD-CANA or pHFD-CR. Conclusion: Paternal diet-induced obesity modulates offspring hepatic transcriptome and lipidome, with dominant impact on genes regulating lipid metabolism and increased ceramide species. Moreover, treatment of obese males prior to conception with canagliflozin or caloric restriction yields substantial reversal of these patterns. Taken together, these data suggest that perturbations in offspring lipid metabolism may be an early phenotype altered in response to paternal obesity. Interventions to improve paternal health may interrupt nongenetic intergenerational transmission of metabolic disease risk. Presentation: 6/2/2024

As population aging accelerates, multimorbidity among older adults poses a growing public health challenge, highlighting the need to identify modifiable behavioral risk factors. Sleep, a core aspect of daily life, remains underexplored in the context of healthy aging. Utilizing five waves (2011-2020) of nationally representative longitudinal data from China (N = 10,065), this study employs individual fixed effects and instrumental variable methods to examine the impact of sleep duration on chronic disease risk. The findings indicate that longer nighttime sleep is associated with a lower likelihood of having chronic diseases, and this pattern remains consistent across multiple robustness checks. Mechanism analyses suggest that sleep promotes health by improving physiological function, psychological well-being, and behavioral vitality. Effects are particularly pronounced among the oldest-old, non-drinkers, and those not living with children. These results offer empirical support for incorporating sleep-focused strategies into interventions aimed at mitigating chronic disease risks in aging populations.

There has been an increased interest in the role of the childhood social environment in the etiology of adult diseases in recent years. The present study examines whether the experience of adversity during childhood increases risk for disease in later life independent of later life socioeconomic, behavioral, and psychosocial factors. The study involved a nationally representative sample of 6,912 persons aged 50 years and older who were participating in the first wave of the Irish Longitudinal Study on Ageing. Childhood adversity was indexed using a 4-item measure that captured challenging and potentially noxious childhood environmental exposures including, socioeconomic disadvantage, substance abuse among parents, physical abuse, and sexual abuse. A doctor diagnosis of disease across 9 chronic disease types represented the primary outcome variables. The experience of adversity during childhood was associated with increased risk of disease in midlife and older ages across a large number of chronic disease types including cardiovascular disease, lung disease, and emotional, nervous, or psychiatric disorders. Analysis of the dose-response pattern revealed positively graded associations between the number of adverse events experienced during childhood and the occurrence of chronic disease in later life. Cox proportional hazard models revealed that the experience of adversity during childhood was associated with earlier age of onset for any physical disease type or emotional, nervous, or psychiatric disorders. These findings indicate that childhood may represent a sensitive or critical period in the development of disease and reinforces the necessity of adopting a life-course approach to the study of chronic diseases.

BackgroundEarly-life exposure to famine is associated with an increased risk of various metabolic disorders. Nevertheless, evidence regarding its long-term effects on thyroid function and disease risk in older adulthood remains scarce. This study investigates the impact of fetal and childhood exposure to the Chinese Great Famine (1959–1961) on thyroid function and disorders in late life.MethodsThis cross-sectional study enrolled 1,956 participants who completed health examinations at a public hospital-based Physical Examination Center in Chongqing between 2022 and 2023. Based on birth cohorts, participants were stratified into three groups: the unexposed group (individuals born in 1963.1.1–1967.12.31), the fetal-exposed group (individuals born in 1959.1.1–1962.12.31), and the childhood-exposed group (individuals born in 1949.1.1–1958.12.31). Binary logistic regression models were used to evaluate the association between famine exposure and thyroid disease risk in later life. Multiple linear regression analyses compared thyroid function biomarkers between famine-exposed and non-exposed groups, adjusting for potential confounders.ResultsIn this study, 373 participants (19.1%) were exposed to the Chinese Great Famine during the fetal period, with 597 individuals (30.5%) experiencing childhood exposure. After adjusting for gender, smoking history, drinking history, dietary salt preference, current exercise status, educational level, body mass index (BMI), the fetal-exposed group demonstrated significantly elevated risks of both overt hyperthyroidism [OR = 4.36, 95% CI (1.02–18.71)] and subclinical hyperthyroidism [OR = 3.13, 95% CI (1.03–9.51)] compared to the non-exposed group. After adjusting for multiple comparisons using the Benjamini-Hochberg FDR method, fetal famine exposure maintained a statistically significant inverse association with thyroid nodule risk relative to childhood exposure [OR = 0.69, 95% CI (0.51–0.93)]. No significant associations were observed between famine exposure and hypothyroidism, thyroid autoantibody positivity, or autoimmune thyroid disorders. Notably, childhood-exposed participants exhibited higher thyroglobulin antibody (TgAb) levels versus non-exposed individuals [β = 40.30, 95% CI (2.21–78.40)].ConclusionFetal exposure to the Chinese Great Famine reduced thyroid nodule risk whereas childhood exposure increased TgAb levels, revealing distinct developmental windows for nutritional programming of thyroid health. These findings underscore the importance of timing in malnutrition-related thyroid dysfunction.

Background: Addressing obesity in childhood may alleviate disease risk in later life. Using real-world data from a school health obesity screening program, we examined the percentage of overweight (POW) by sex, grade, blood test results, and lifestyle factors to inform programmatic improvements.Materials and methods: We analyzed data from lifestyle-related disease prevention examinations conducted in Setagaya Ward for children and adolescents between 2015 and 2019 (n = 571). Schoolchildren were categorized into three groups based on POW at the school health exam and change in POW at follow-up. Descriptive analyses were performed to identify factors associated with these outcomes.Results: Comparing the percentage of students in the decreased POW group, 72.3% (115/159) of junior high school students experienced a decrease, compared with 49.5% (204/412) of elementary school students. There was a tendency for no change in POW or increased POW among schoolchildren with higher aspartate aminotransferase, alanine aminotransferase, and total cholesterol. Sleep time tended to be longer in the lower POW group and shorter in the POW 50 group. Television viewing time on weekdays and holidays was shorter in the lower POW groups.Conclusions: Higher obesity percentages were observed among boys and among children in higher grades. Given the associations with lifestyle factors as well, it may be essential to integrate more targeted health guidance in the screening program.

BackgroundBirthweight marks an important milestone of health across the lifespan, including cardiometabolic disease risk in later life. The placenta, a transient organ at the maternal-fetal interface, regulates fetal growth. Identifying genetic loci where DNA methylation in placenta is associated with birthweight can unravel genomic pathways that are dysregulated in aberrant fetal growth and cardiometabolic diseases in later life.ResultsWe performed placental epigenome-wide association study (EWAS) of birthweight in an ethnic diverse cohort of pregnant women (n = 301). Methylation at 15 cytosine-(phosphate)-guanine sites (CpGs) was associated with birthweight (false discovery rate (FDR) < 0.05). Methylation at four (26.7%) CpG sites was associated with placental transcript levels of 15 genes (FDR < 0.05), including genes known to be associated with adult lipid traits, inflammation and oxidative stress. Increased methylation at cg06155341 was associated with higher birthweight and lower FOSL1 expression, and lower FOSL1 expression was correlated with higher birthweight. Given the role of the FOSL1 transcription factor in regulating developmental processes at the maternal-fetal interface, epigenetic mechanisms at this locus may regulate fetal development. We demonstrated trans-tissue portability of methylation at four genes (MLLT1, PDE9A, ASAP2, and SLC20A2) implicated in birthweight by a previous study in cord blood. We also found that methylation changes known to be related to maternal underweight, preeclampsia and adult type 2 diabetes were associated with lower birthweight in placenta.ConclusionWe identified novel placental DNA methylation changes associated with birthweight. Placental epigenetic mechanisms may underlie dysregulated fetal development and early origins of adult cardiometabolic diseases.Clinical trial registrationClinicalTrials.gov, NCT00912132

There is evidence that hyperemesis gravidarum (HG) is associated with a predominance of female fetuses, lower birthweights and shorter gestational ages at birth. As the adverse effects of prematurity and low birthweight on disease risk in later life have become increasingly clear, the repercussions of HG might not be limited to adverse perinatal outcomes. To summarise the evidence on short- and long-term outcomes of pregnancies with HG. A literature search was conducted in the electronic databases Medline and Embase. Studies were included that reported on the fetal, neonatal and long-term outcome of pregnancies complicated by HG. Two authors independently selected studies and extracted data. Meta-analysis was performed using review manager. Women with HG during pregnancy were more likely to have a female child (OR 1.27; 95% CI 1.21-1.34). They were also more likely to have a baby with low birthweight (LBW, <2500 kg; OR 1.42; 95% CI 1.27-1.58) that was small for gestational age (SGA; OR 1.28; 95% CI 1.02-1.60), and to deliver prematurely (OR 1.32; 95% CI 1.04-1.68). There was no association with Apgar scores, congenital anomalies or perinatal death. One study described an association between HG and testicular cancer in the offspring. There is evidence that HG is associated with a higher female/male ratio of offspring and a higher incidence of LBW, SGA and premature babies. Little is known about the long-term health effects of babies born to mothers whose pregnancies were complicated by HG.

Background and Aims The causal relationship between body mass index (BMI) across developmental stages (from birth through childhood) and cardiac outcomes in later life remains unclear. Using longitudinal Mendelian randomization (MR) analysis, we investigated the causal effects of early-life weight indicators on cardiovascular disease risk and cardiac structure and function. Methods We selected independent genetic instruments (r2 &amp;lt; 0.001) associated with BMI at different developmental stages at genome-wide significance level (p &amp;lt; 5 × 10-8) from large-scale genome-wide association studies. The primary analysis used inverse-variance weighted MR to examine the causal effects on cardiovascular diseases (coronary artery disease, heart failure, atrial fibrillation, and ischemic stroke) and 16 measures of cardiac structure and function. Multiple testing correction was performed using the Benjamini-Hochberg procedure. Results Birth weight showed positive associations with coronary heart disease (OR 1.31 [1.17-1.47]) but negative associations with atrial fibrillation (OR 0.82 [0.75-0.91]). The strongest associations were observed during later childhood (7-8 years), with higher BMI being associated with increased risks of coronary heart disease (OR 1.16 [1.06-1.28]), atrial fibrillation (OR 1.20 [1.12-1.29]), and heart failure (OR 1.42 [1.27-1.59]). For cardiac structure, birth weight was negatively associated with ventricular volumes, while BMI at 7-8 years showed positive associations with these parameters. Mediation analyses revealed that type 2 diabetes and blood pressure partially mediated the effects of BMI on cardiovascular outcomes, with mediation proportions ranging from 18.6% to 65.2%. Conclusion Our findings provide genetic evidence for stage-specific effects of BMI on cardiovascular outcomes, with particularly strong associations during later childhood (7-8 years). The adverse impact of childhood BMI on cardiovascular health is partially mediated through type 2 diabetes and blood pressure, emphasizing the importance of early-life weight management for cardiovascular disease prevention.

DNA methylation is one of several epigenetic mechanisms that play a regulatory role in genome programming and imprinting during embryogenesis. Aberrant DNA methylation has been implicated in the pathogenesis of a number of diseases associated with aging, including cancer and cardiovascular and neurological diseases. Evidence is accumulating that dietary factors in utero modulate disease risk in later life. Although folic acid is a key component of DNA methylation, the impact of folic acid availability in utero on DNA methylation patterns and disease risk in adulthood is at present poorly characterized. This review describes the relationship between folic acid and DNA methylation, and the association between DNA methylation during in utero development and aging.

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.

In this developing paradigm, heterogeneous network infrastructure is combined with ubiquitous devices for sensing, smart control systems and big data analytics to create “smart cities.” Enhancing the overall quality of life is one of their main goals and they do so through smart services they offer in a broad range of areas such as transportation, healthcare, entertainment (including virtual reality), the environment (including pollution) and energy. To be able to offer those services, the job of big data and their analysis is complicated, since it allows for the extraction of useful insights from the enormous amounts of data that the smart cities produce. The current state-of-the-art research efforts aimed at big data analytics in a smart city setting are investigated and presented in this chapter. In particular, a comprehensive literature review on the big data analytic frameworks for smart cities is provided. Following that, a short talk on IoT and big data is briefed. Owing to different data that the smart cities collect, which frequently have contradictory processing needs, appropriate investigative methods based on the data type are also recommended, depending on the data type. The many smart city applications were investigated and a perspective on the difficulties associated with smart city development was developed. In this chapter, the methods addressing the smart city issues and development are examined, and the possibility for the employment of integrated IoT and big data analytics is presented. Finally, as a case study, a briefing on a call service application for the fire department in a smart city is provided to round up the presentation.