Evolution Of Big Data Research Articles

The AI-Powered Evolution of Big Data

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.

The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions

Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.

A Study on Historical Big Data Analysis of Surface Ecological Damage in the Coal Mining Area of Lvliang City Based on Two Mining Modes

Coal mining inevitably causes damage to the surface ecological environment. In response to the characteristics of multiple factors, wide scope, and long time series of surface ecological environment damage in coal mining subsidence areas, how to integrate multiple data sources and monitoring methods to achieve monitoring and trend analysis of ecological damage throughout the entire mining cycle still needs to be studied. In addition, the 110 mining method provides an innovative method for underground coal mining to reduce its surface ecological damage, but the differences in surface damage between the two mining modes and the effectiveness of the 110 method in realizing surface ecological damage-reducing mining still need to be studied in depth. Therefore, this study takes the surface ecological damage in the mining area of Lvliang City, a typical resource city in Shanxi Province, China, as the object. It establishes a four-in-one “Satellite–UAV–Ground–Underground” information monitoring method, proposes a historical big data evolution analysis method, constructs three spatial scales of historical big databases, clarifies the current situation and development trend of damage in coal mining areas in Lvliang City and explores the differences in surface ecological environment damage characteristics in coal mining areas based on the 121 and 110 mining methods. The results show that: (1) The existing coal mining subsidence area in Lvliang City is as high as 92,191.47 hectares, and it is expected to continue to increase to 130,739.55 hectares in the future 2035, with a growth rate of 41.812%, which realizes the goals of mapping the current situation, retracing the history and predicting the future of the ecological damage of the surface in Lvliang City. (2) The surface NDVI of the 110 working face is restored to the average level of the mining area faster than that of the 121 working face. The surface crack width, step displacement, length, distribution density, and surface settlement height of the 110 working face are all smaller than those of the 121 working face. It has been verified that the unique top-cutting and swelling filling effect of the 110 methods can effectively reduce the ecological damage caused by coal mining subsidence. And its widespread application can effectively realize the ecological environmental protection of the coal mine area and contribute to the high-quality development of the coal industry in Lvliang City.

Research and application of neural network‐based intelligent water‐saving systems

AbstractCapitalizing on the swift evolution of big data and information technology, conventional water‐saving endeavours have been revamped into ‘smart water‐saving’. This paradigm shift leverages cutting‐edge technologies like neural networks, the Internet of Things (IoT) and advanced sensor systems. These components seamlessly integrate with network infrastructure, mobile systems and sophisticated water‐saving information systems to forge comprehensive smart water‐saving management systems. These systems adeptly amass, process and dissect vast troves of water‐saving data, harmonizing with the requirements of water management authorities and consumers alike. This fusion of technology empowers us to furnish all‐encompassing information services, thereby elevating the standards of water resource management. In summary, the integration of state‐of‐the‐art technology into water‐saving initiatives represents a momentous leap forward in addressing critical water‐related challenges and realizing more efficient, sustainable water resource management practices.

Improving sentiment analysis of financial news headlines using hybrid Word2Vec-TFIDF feature extraction technique

With the evolution of big data and information technology, sentiment analysis has become a research hotspot in the financial market. Researchers are increasingly focused on improving the efficiency of sentiment analysis using different machine learning and deep learning architectures. Feature extraction is a fundamental process in sentiment analysis that enhances text representation and classification. Though various feature extraction techniques are present in the field, limited attention has been given to hybrid feature extraction techniques. The primary objective of this study is to improve the sentiment analysis of financial news headlines using a hybrid Word2Vec-TFIDF feature extraction technique. The study evaluates the performance of Word2Vec, Doc2Vec, TFIDF, Word2Vec-TFIDF, and Doc2Vec-TFIDF with six machine learning classifiers. The results find that the hybrid feature extraction technique, Word2vec-TFIDF with SVM classifier, outperforms all other sentiment analysis models with an accuracy of 82 percent.

지자체의 수요자 중심 주문형 데이터 제도 도입 필요성

This study examines the implementation of On-Demand Data Systems within the Seoul Metropolitan Government, a critical strategy in the digital era, focusing on public data usage in administration. It investigates the role of such systems in enhancing data accessibility, protection, and collaboration among stakeholders in local governments. The research discusses the ordinance direction for integrating these systems into local governance and observes the evolution of big data in relation to the evolving roles of local authorities. Findings reveal that On-Demand Data Systems can significantly aid community development and provide insights into future challenges and opportunities. Despite limitations in data usage in local governments, the study underscores potential advantages like improved administrative service quality and increased transparency in policy-making. Key challenges include data processing, protection, and the necessity for institutional improvements in data provision. The study concludes that effective utilization of On-Demand Data Systems in local governments can be vital for service and product development, fostering local economic growth and addressing social issues.

Revealing social dimensions of urban mobility with big data: A timely dialogue

Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility.

Exploring GPT-4's Characteristics Through the 5Vs of Big Data: A Brief Perspective

In the dynamic field of modern artificial intelligence, GPT-4 emerges as a key participant, addressing challenges similar to Big Data's 5Vs—Volume, Velocity, Variety, Veracity, and Value. This study explores the convergence of GPT-4's operational framework with the core aspects of Big Data, highlighting the model's flexibility and efficacy in handling intricate datasets. GPT-4 excels in managing extensive textual data, aligning with Big Data's voluminous nature, and demonstrates real-time processing capabilities to match the rapid evolution of Big Data. While initially text-oriented, GPT-4 expands into image recognition, enhancing versatility and aligning with Big Data's Variety aspect. The model's evolving proficiency in non-textual domains broadens its utility. Addressing Veracity, GPT-4 critically evaluates diverse training data, mirroring Big Data's challenges in ensuring accuracy. Its outputs, offering context and insights, contribute to actionable knowledge and align with Big Data's objectives. Despite differences, GPT-4 serves as a microcosm, providing scalable and accessible data processing capabilities, establishing itself as a crucial tool in the AI domain. This paper emphasizes the parallels and underscores GPT-4's adaptability in handling complex datasets.

Multi-attribute decision-making based on data mining under a dynamic hybrid trust network

The issues arising from the evolution of big data and network environment have rendered the relationship among experts to no longer be an independent multi-attribute group decision-making process. In response, a PROMETHEE multi-attribute decision-making method based on data mining under dynamic hybrid trust network is proposed. This method incorporates evaluation similarity based on degree centrality and expert trust values based on K-hop centrality in the social network to determine the experts’ weights. Furthermore, because the expert weight is somewhat affected by the attribute weight, the public-level attribute weight is obtained through data crawling and TF-IDF technology. The comprehensive attribute weight is acquired through a combination of expert-level attribute weights. Additionally, establishing a minimum adjustment cost model enables experts to follow a dynamic consensus reaching process in a composed hybrid trust network. Finally, to express the evaluation information more accurately in a complex linguistic environment, the probabilistic linguistic PROMETHEE method is introduced and applied to the site selection evaluation of charging and swapping stations. This highlights the feasibility and effectiveness of the proposed method through the comparison of decision-making methods and parameter sensitivity analysis.

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.

Identifying Buying Patterns From Consumer Purchase History Using Big Data and Cloud Computing

The consumer buying process refers to the procedures taken by a buyer when making a purchase. There are patterns that customers follow before they make purchases that can be described as consumer behavior. When making decisions, businesses and engineers turn to big data for the valuable insights it contains. Edge computing, although presenting processing issues, has aided in the evolution of big data by offering computational, networking, and storage capability. The process consists of identifying needs and wants, conducting research, evaluating options, and making a purchase, followed by evaluating the purchase. This is a considered major problem in the prediction history. To overcome these issues, here comes a framework of predicting customer purchasing using big data analytics (PCP-BDA) to determine the purpose of every customer becoming aware of the need or desire for a product and ends with the purchase transaction.

Some Features of Social Structures and Institutions Transformation in the Digital Age

The paper examines the peculiarities of digitalization processes influence on the architecture of emerging socio-economic relations. The legal regulation issues of digital technologies and the shifts they cause in public life are considered. The relations arising in connection with the regulation of big data are compared. The evolution of big data into smart content is described. The phenomenon of the "digital twin" is considered, as well as its impact on the social sphere. The tendency to move away from the policy of direct prohibitions in the field of digital technologies and the transition to the control of physical entities (data centers) and the regulation of methods and approaches to data processing (algorithms) is shown. It is noted that the existing expectations from digitalization are overstated. At the same time, the increasing influence of digital technologies significantly changes the existing socio-economic landscape, generating new risks. The answer to these challenges should be the joint work of authorities, business, society and the expert community on the formation of digital culture. It is shown that an important aspect should be the development of expert systems that translate qualitative characteristics into quantitative indicators.

The Evolution of Big Data in Knowledge Management: A Bibliometric Analysis

Recently, the close relationship between big data and knowledge management has become one of the important agendas of businesses. The aim of this study is to systematize the literature on big data and knowledge management from a bibliometric perspective and to create a general framework for the past, present and future of the field. The present study examined 622 papers acquired from the Clarivate Analytics Web of Science (WoS) Core Collection database between 2013 and 2020. The results showed that the annual growth rate of the relevant field was found to be 42.9% indicating a higher popularity among researchers. China and USA are home to the most productive authors and institutions in the field. Also, country collaboration network, institutional co-authorship network, co-word network and co-citation network are given to present the intellectual structure of the field. This study is useful to understand leading trends in the field in terms of the most influential authors, institutions and countries, the most productive journals, the most frequent keywords, the collaboration networks and the co-citation networks. To the best of researchers’ knowledge, this study is the first bibliometric examination attempt to understand the flow at the intersection of big data and knowledge management over time.

Impact of the Internet finance on Small and Medium Enterprises Financing under Big Data evolution

Internet financial model innovation is mainly reflected in big data Internet financial companies, third-party payment platforms, Peer-to-Peer Internet lending platforms, crowdfunding Internet financing platforms, etc. platform and Internet financial portal platform. The impact of Internet finance on the financing of Small and medium-sized enterprises is mainly reflected in four aspects: that is, the mechanism of reversal of traditional financial institutions; the financing objects of Small and medium-sized enterprises More comprehensive and scientific; financing services for Small and medium-sized enterprises are more targeted; financing costs for Small and medium-sized enterprises have dropped significantly.

Improved Technique for Preserving Privacy while Mining Real Time Big Data

With the evolution of Big data, data owners require the assistance of a third party (e.g.,cloud) to store, analyse the data and obtain information at a lower cost. However, maintaining privacy is a challenge in such scenarios. It may reveal sensitive information. The existing research discusses different techniques to implement privacy in original data using anonymization, randomization, and suppression techniques. But those techniques are not scalable, suffers from information loss, does not support real time data and hence not suitable for privacy preserving big data mining. In this research, a novel approach of two level privacy is proposed using pseudonymization and homomorphic encryption in spark framework. Several simulations are carried out on the collected dataset. Through the results obtained, we observed that execution time is reduced by 50%, privacy is enhanced by 10%. This scheme is suitable for both privacy preserving Big Data publishing and mining.

How can the current evolution of big data be applied in influencing organisational behaviour in the retail sector?

With the new development of Large Information with its Volume, Assortment and Speed (3v's), information examination has arisen as a urgent area of study for the two experts and scientists, mirroring the extent and effect of information related issues to be settled in business associations, counting the retail business. This study has purposefully distinguished and examined four elements, in particular, information source, information examination apparatuses, monetary and financial results and information security and information protection, to measure their effect on the effect of Huge Information in the retail business. This research investigations the effect of huge information examination on retail firms that utilization information and business investigation to simply decide, named an information driven navigation (DDD) approach. The new finding is shown up that monetary and financial result showed major areas of strength for an and have direct relationship with information examination apparatuses of retail industry. Information for the review were gathered utilizing a overview of different strategic approaches and interests in data innovation by retail associations. The information examination showed that retail associations which use DDD have higher result and efficiency. Utilizing Brilliant PLS information examination techniques with strong help of survey from ISI Diaries, the connection among DDD and execution is likewise obvious in parts of association, for example, the usage of stock, client commitment and market esteem in the retail business.

BIGDATA AND ITS APPLICATIONS

Technology has changed a lot in this digital era. Earlier we had landline phones but now wehave Smartphones, Laptops and Tablets that are making our life smarter. We were usingbulky desktops for processing huge amounts of data, we were using floppies and hard disksto store the data earlier. Now we can store data in the cloud. Due to the enhancement oftechnologyweweregeneratingalotofdata,forexampleeachsmartphoneuserapproximately generates 40 Exabyte’s of data every month in the form of texts, emails,phone calls, videos, photos, searches, music, etc., if this number is multiplied by 5 billionsmartphone users, that is a large amount. Traditional computing systems cannot handle thislarge amount of data. You have no idea about how much data you are generating in eachminute.Butthechallengingparthereisthatthedataisnotpresentinastructuredmannerand it is huge in size. Data is being generated in millions of ways and it is one of the biggestfactors for the evolution of Big Data. With the exponential growth of the data, people startedto store it in relational database systems. But with the advancements in the internet anddigitalization, they are insufficient. In order to overcome this, big data came into the picture.This Big Data.Provides a new set of tools and technologies to store a large amount ofunstructured data. Industryinfluencers,academicians,and other prominent stakeholders agree that Big Datahas become a big game-changer in most industries. The primary goal for most organizationsis to enhance customer experience, cost reduction, better-targeted marketing, and makingexistingprocessesmoreefficient.Inthispaper,welookintothevariousapplicationsthatBig Data offers to the industries, Industry-specific challenges that these industries face, andhow Big Data solves these challenges.

Applications of geospatial big data in the Internet of Things

AbstractThe Internet of Things (IoT) paradigm represents networks of objects that, without requiring human action, sense and interact with the environment, collect data, and transmit these data for users and applications via the internet. IoT applications can generate a massive amount of data, with a significant part containing geospatial aspects and requiring applications to deal with the objects' geospatial relationships and extract information and knowledge. This article describes a systematic literature mapping investigating the geospatial evolution of big data in the IoT. For the mapping, 116 documents were selected from four academic databases—Scopus, IEEE Xplore, ACM Digital Library, and Web of Science—and classified according to their main contributions. The analysis of these works allowed us to view what domains have applied geospatial big data from IoT, the artifacts that support this application, and the most prolific authors and venues involved in this research area.

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

The Research Hotspot and Path Evolution of Big Data in Healthcare Industry: Visualized Analysis Based on Cite Space

Along with the swift development of big data, the application fields of big data have become more and more broad, as well as the fusing of big data and healthcare. In this paper, CiteSpace, the bibliometrics software, was used for analyzing the research status and hotspots of big data in healthcare fields. Taking big data and healthcare as the keywords, the core database of Web of Science from 2011 to 2020 has been retrieved. According to author/country/organization co-authorship analysis and author/journal/reference co-cited analysis, the research status of big data in healthcare was discussed. Through the keywords co-occurrence network and cluster analysis, research hotspot and path evolution of big data in healthcare were also studied.