Utilization Of Big Data Research Articles

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.

Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning

The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, with spatio-temporal information play a crucial role in constructing a comprehensive spatio-temporal knowledge graph and facilitating the effective utilization of spatio-temporal big data for knowledge-driven service applications. The existing knowledge graph (or geographic knowledge graph) takes spatio-temporal as the attribute of entity, ignoring the role of spatio-temporal information for accurate retrieval of entity objects and adaptive expression of entity objects. This study approaches the correlation between geographic knowledge and spatio-temporal information as a text classification problem, with the aim of addressing the challenge of establishing meaningful connections among spatio-temporal data using advanced deep learning techniques. Specifically, we leverage Wikipedia as a valuable data source for collecting and filtering geographic texts. The Open Information Extraction (OpenIE) tool is employed to extract triples from each sentence, followed by manual annotation of the sentences’ spatio-temporal relevance. This process leads to the formation of quadruples (time relevance/space relevance) or quintuples (spatio-temporal relevance). Subsequently, a comprehensive spatio-temporal classification dataset is constructed for experiment verification. Ten prominent deep learning text classification models are then utilized to conduct experiments covering various aspects of time, space, and spatio-temporal relationships. The experimental results demonstrate that the Bidirectional Encoder Representations from Transformer-Region-based Convolutional Neural Network (BERT-RCNN) model exhibits the highest performance among the evaluated models. Overall, this study establishes a foundation for future knowledge extraction endeavors.

빅데이터 활용한 시계열 데이터 텍스트 마이닝 및 시각화

빅데이터 활용한 시계열 데이터 텍스트 마이닝 및 시각화

Big Data Utilization on Tourism Marketing in Post-Pandemic Recovery Ear: Future Prospect for Indonesia

Along with the behaviour changes toward digitalisation in the pandemic, reliable data to manage tourism marketing in Indonesia is needed. The study objectives are to go into detail on the application of big data and identify what kind of data is necessary for tourism marketing based on the previous research. Most studies about big data on tourism marketing come from overseas, with only a few from Indonesian authors. Questions were raised on what big data is used in tourism marketing and what data is needed, mainly for Indonesia. The use of big data and the kind of data for the benefit of tourism marketing in this study has yet to be discovered. The study adopted SLR with the PRISMA approach and continued with bibliometric analysis using VOS Viewers based on keywords and co-occurrence. Scopus is a database source, and 105 articles published from 2002 to 2022 are found. There are six clusters from plotting extensive data application networks. Two forms of big data can be gained to foresee tourism marketing in post-pandemic recovery. The utilisation cluster is the consumer behaviour research, technology operation, social media, future prediction based on network sentiment analysis, and innovation in smart tourism. Data from the tourism industry and non-tourism sector can be utilised. The study implied combining both the tourism and non-tourism sectors as integrated big data in tourism marketing for decision-making in the future.
 Keywords— big data; marketing; non-tourism; tourism; utilization

Decision Making Performance of Business Data Analytical Capabilities: The Mediating Effect of Analytics Competency

This research study investigates the mediating influence of analytics competency (AC) on the relationship between business data analytical capabilities (BDAC) and decision-making performance (DMP). Using a quantitative approach, 167 managers with experience and competence in using data in Indonesian public service sector organizations were empirically evaluated. Structural Equation Modeling analysis was applied to examine the impact of BDAC on DMP and mediating effects of AC on this relationship. The results demonstrate that BDAC and AC significantly influence DMP, and BDAC significantly affects AC. Notably, AC was identified as a complementary mediator in the relationship between BDAC and DMP, explaining the majority of their effects. These findings suggest the significance for organizational leaders and managers to develop plans that nurture BDAC and the use of AC to maximize the business's utilization of big data. Thus, organizational leaders and managers can determine the policies and strategies needed to extract value from data, determine the direction of the organization in data-driven decision-making, thereby building better and faster decision quality through AC and BDAC. Moreover, the study highlights opportunities for future studies, including comparative analysis across different organizations, experimental research on AC and DMP, and the application of concurrent or sequential mixed methods to explore context and process relationships among variables.

Anticholesterol Activity and Mechanisms of Konjac Glucomannan: A Systematic Review

Big Data is the new oil. The real issue of the development of information technology is the existence of Big Data. Big Data is very important, talking about Big Data not only touches the issue of how big data you have, but what can be done with the data. The high cost of Big Data access is inversely proportional to the collection of data through surveys or censuses for free. analysis of the application of efficiency theory in an economic approach in imposing access costs to statistical activities, knowing the impact of imposing Big Data access fee tariffs, knowing the potential of Big Data as a supporter of official statistics in the future, what are the obstacles and solutions in implementing it. The preparation of this paper is juridical-empirical research with the nature of descriptive research with an economic law approach. Big Data is managed for different purposes using different systems and methods and not necessarily using statistical rules. The implementation of Big Data as a new data source is carried out through a combination of data sources. The imposition of Big Data access fees as a source of data supporting official statistics causes low utilization of Big Data as a new data source. The information technology revolution makes Big Data has the potential to complement, replace, improve, add, and improve the composition of existing statistical data sources, and produce more timely outputs. Difficult access and high costs for data collection are major obstacles. Therefore, it needs to be supported by legal instruments that facilitate its implementation in Indonesia. One is the reformulation of existing regulations that make it easier for basic statistical organizations to obtain such data sources for free and use only in the national interest

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data’s impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer’s Disease, Stroke, Depression, Parkinson’s Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Digital Transformation, Enterprise Artificial Intelligence Strategy for Big Data Utilization, Innovation and Collaboration

Digital Transformation, Enterprise Artificial Intelligence Strategy for Big Data Utilization, Innovation and Collaboration

Utility of Big Data to Explore Medication Adherence in Māori and Non-Māori Community-Dwelling Older Adults with Heart Failure in Aotearoa New Zealand: A Cross-sectional Study.

Medication adherence improves morbidity and mortality-related outcomes in heart failure, and knowledge of patterns of medication adherence supports patient and clinician decision-making. Routinely collected national data facilitate the exploration of medication adherence and associated factors in older adults with heart failure, including the association between ethnicity and adherence. There are known inequities in access to medicines between Māori (Indigenous People of Aotearoa New Zealand) and non-Māori, yet ethnic variation in medicines adherence in community-dwelling older adults with heart failure has not been explored. Here we identify medication adherence rates for community-dwelling older adults diagnosed with heart failure and differences in adherence rates between Māori and non-Māori. Cross-sectional analysis of interRAI (comprehensive standardised assessment) data in a continuously recruited national cohort from 2012 to 2019. Overall, 13,743 assessments (Māori N=1526) for older community-dwelling adults with heart failure diagnoses were included. The mean age of participants was 74.5years [standard deviation (SD) 9.1years] for Māori and 82.3 years (SD 7.8years) non-Māori. In the Māori cohort, 21.8% did not adhere fully to their medication regimen, whereas in the non-Māori cohort, this figure was 12.8%. After adjusting for confounders, the Māori cohort were more likely to be medication non-adherent than non-Māori [prevalence ratio 1.53, 95% confidence interval (CI) 1.36-1.73]. There was a significant disparity between Māori and non-Māori concerning medication adherence. Given the international use of the interRAI-HC assessment tool, these results have significant transferability to other countries and allow the identification of underserved ethnic groups for which culturally appropriate interventions can be targeted.

Big data technology framework and data utilization for urban environmental pollution management

Urban environmental pollution management is of great practical significance to achieving sustainable urban economic development. To improve the efficiency of urban environmental pollution management, we have established a big data technology framework for urban environmental pollution treatment. The relevant pollution data collected are used for targeted pollution treatment. The results show that the average efficiency of environmental pollution control in the whole province of China has increased from 33.67% to 63.67%, an increase of 46%. Among them, the efficiency of environmental pollution control in Guangdong has increased most significantly, with a relative position of 26.47%, which is at the top of the list. Inner Mongolia has the weakest increase in environmental pollution control efficiency, with an appreciation of 6.89% relative to its position, while the other 16 provinces and cities have little change in environmental pollution control efficiency. between 2010 and 2020, the urban pollution and environmental treatment costs that residents need to bear changed significantly, with fluctuations of around 30%.

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies

In Malaysia, Fintech companies are leveraging Big Data technology to serve effectively the targeted markets. As the financial industry continues to develop, there is a great potential for the further growth and use of Big Data in Fintech in Malaysia. This is an exploratory study which is set to identify factors that influence the use of Big Data in the Fintech sector. Using a systematic selection process, publications related to Big Data adoption were identified and reviewed. The results show that the technological, organisational and environmental factors have a clear impact on Big Data adoption. The paper also presents the challenges reflected on in the current research on Big Data adoption and recommends future research directions. A research model focused on utilisation of Big Data technology is proposed and presented, driven by the literature review.

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

The COVID-19 pandemic has prompted the adoption of distance education worldwide, including in 3T regions facing challenges of limited infrastructure and accessibility. The utilization of Big Data holds great potential in enhancing the quality and accessibility of education in these 3T regions. This study aims to analyze the utilization of Big Data in distance education during the pandemic in 3T regions, identify its benefits in personalized learning, monitoring the learning process, and predicting student needs, and provide recommendations for further research. The research approach employed data analysis from a survey involving 500 schools in 3T regions. Data was collected through questionnaires that encompassed the aspects of Big Data usage in distance education and its impact on educational quality in 3T regions. Data analysis reveals that the utilization of Big Data in distance education in 3T regions has significantly contributed to student data collection and analysis, monitoring the learning process, and predicting student needs. These findings are consistent with previous research that highlights the benefits of Big Data in personalized learning and educational performance management. The utilization of Big Data in distance education in 3T regions during the pandemic holds immense potential in improving the quality and accessibility of education. Adequate training and education should be provided to teachers and school staff regarding the utilization of Big Data.

Utilization of Big Data with a Focus on Administrative Claims Database

With the development of information technology, patient information is stored as electronic data, and huge amounts of such data are collected every day. Such a collection compiled over the course of clinical practice is called real-world data and is expected to be used for evaluating drug efficacy and safety. Real-world data such as health insurance association-based administrative claims databases, pharmacy-based dispensing databases, and spontaneous reporting system databases are mainly used in pharmaceutical research. Among them, claims databases are used for various observational studies such as studies on nationwide prescription trends, pharmacovigilance studies, and studies on rare diseases due to their large sample size. Although the nature of omics data is different from that of real-world data, it has become accessible on cloud platforms and are being used to broaden the scope of research in recent years. In this paper, we introduce a method for generating and further testing hypotheses through integrated analysis of real-world data and omics data, with a focus on administrative claims databases.

Utilization of Big Data in Libraries by Using Data Mining

This article analyzes extensive data libraries and how they benefit when completed. This research has results, namely library data can be called big data if the data fulfills the three characteristics of big data: volume, speed, and variety. Data mining can process big data, for example, by associations that create links between objects. If an extensive data library contains borrowed data, this means being able to establish connections between borrowed collections. Librarians, in particular, can use the results of this comprehensive data work to decide how to proceed. Therefore, it is essential to play a role in improving the library. This research aims to increase discussion between readers and librarians about using Big Data, especially in libraries. The benefit of this research is to broaden the knowledge of readers and librarians about how to utilize big data. The research method used is library research which analyzes writings from various sources.

Reformulation Of Statistical Data Sources: Big Data New Data Sources Supporting Future Official Statistics?

Big Data is the new oil. The real issue of the development of information technology is the existence of Big Data. Big Data is very important, talking about Big Data not only touches the issue of how big data you have, but what can be done with the data. The high cost of Big Data access is inversely proportional to the collection of data through surveys or censuses for free. analysis of the application of efficiency theory in an economic approach in imposing access costs to statistical activities, knowing the impact of imposing Big Data access fee tariffs, knowing the potential of Big Data as a supporter of official statistics in the future, what are the obstacles and solutions in implementing it. The preparation of this paper is juridical-empirical research with the nature of descriptive research with an economic law approach. Big Data is managed for different purposes using different systems and methods and not necessarily using statistical rules. The implementation of Big Data as a new data source is carried out through a combination of data sources. The imposition of Big Data access fees as a source of data supporting official statistics causes low utilization of Big Data as a new data source. The information technology revolution makes Big Data has the potential to complement, replace, improve, add, and improve the composition of existing statistical data sources, and produce more timely outputs. Difficult access and high costs for data collection are major obstacles. Therefore, it needs to be supported by legal instruments that facilitate its implementation in Indonesia. One is the reformulation of existing regulations that make it easier for basic statistical organizations to obtain such data sources for free and use only in the national interest

Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study.

This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care-related occupations and to clarify the obstacles to the use of health big data. From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care-related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. The respondents who revealed the need to use health big data in health care work-related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. Our findings indicate respondents' high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future.

Big data and infectious disease epidemiology: A bibliometric analysis and research agenda.

Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. This paper aims to synthesize research and identify hotspots of big data in infectious disease epidemiology. Bibliometric data from 3054 documents that satisfied the inclusion criteria were retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents. The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. It also placed the US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes. Proposals for future studies are made based on these findings. This study will provide healthcare informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Estimating Culprit Drugs for Adverse Drug Reactions Based on Bayesian Inference.

The utility of big data in spontaneous adverse drug reactions (ADRs) reporting systems has improved the pharmacovigilance process. However, identifying culprit drugs in ADRs remains challenging, although it is one of the foremost steps to managing ADRs. Aiming to estimate the likelihood of prescribed drugs being culprit drugs for given ADRs, we devised a Bayesian estimation model based on the Japanese Adverse Drug Events Reports database. After developing the model, a validation study was conducted with 67 ADR reports with a gross of 1,387 drugs (67 culprit drugs and 1,320 concomitant drugs) prescribed and recorded at Yamaguchi University Hospital. As a result, the model estimated a culprit drug of ADRs with acceptable accuracy (area under the receiver operating characteristic curve 0.93 (95% confidence interval 0.88-0.97)). The estimation results provided by the model to healthcare practitioners can be used as one clue to determine the culprit drugs for various ADRs, which will improve the management of ADRs by shortening the treatment turnaround time and increasing the precision of diagnosis, leading to minimizing the adverse effects on patients.

A Review of Big Data in Road Freight Transport Modeling: Gaps and Potentials

Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

Medical informatization has initially demonstrated its advantages in improving the medical service industry. Over the past decade, the Chinese government have made a lot of effort to complete infrastructural information construction in the medical and health domain, and smart hospitals will be the next priority according to policies released by Chinese government in recent years. To provide strategic support for further development of medical information construction in China, this study aimed to investigate the current situation of medical information construction in tertiary class-A public hospitals and analyze the existing problems and countermeasures. This study surveyed 23 tertiary class-A public hospitals in China who voluntarily responded to a self-designed questionnaire distributed in April 2020 to investigate the current medical information construction status. Descriptive statistics were used to summarize the current configurations of hospital information department, hospital information systems, hospital internet service and its application, and the satisfaction of hospital information construction. Interviews were also conducted with the respondents in this study for requirement analysis. The results show that hospital information construction has become one of the priorities of the hospitals' daily work, and the medical information infrastructural construction and internet service application of the hospitals are good; however, a remarkable gap among the different level of hospitals can be observed. Although most hospitals had built their own IT team to undertake information construction work, the actual utilization rate of big data collected and stored in the hospital information system was not satisfactory. Support for the construction of information technology in primary care institutions should be increased to balance the level of development of medical informatization in medical institutions at all levels. The training of complex talents with both IT and medical backgrounds should be emphasized, and specialized disease information standards should be developed to lay a solid data foundation for data utilization and improve the utilization of medical big data.