Examining the Dimensions of Adopting Natural Language Processing and Big Data Analytics Applications in Firms

These days, Big Data (BD) and Big Data Analytics (BDA) applications have increased intensively among public and private organisations. Most organisations are aware that BDA has an enormous potential in aiding them to better understand their business environments and their customers’ needs. Nevertheless, many organisations have yet to implement BD as they are concerned that poor quality of data will have an adverse impact on establishing worthful insight, and leading to severe mistakes during their decision-making process. In addition, the different BD characteristics or traits could affect data quality. Therefore, to determine the value of data generated from BD, the collected data must be analysed for accuracy and quality. This paper aims to present findings to better understand quality requirements for BDA implementation in the public sector, specifically in Malaysia. This study explored the influence of Data Quality Dimensions (DQD) on BDA application, identified the influence of Big Data Traits (BDT) on DQD, and evaluated the integration of BDT and DQD in BDA applications using expert validation approach. A conceptual model that incorporates DQD and BDT for BDA application in the public sector was proposed as the study outcome. The conceptual model was developed based on eight BDT (variety, velocity, veracity, validity, volume, value, volatility, and variability) and four data quality categories (intrinsic, contextual, representational, and accessibility). The expert validation results showed that five out of eight BDT are important. The outcomes from this study would deliver important knowledge to the current body of studies that may prove useful for potential use in the future.

Concurrence of big data analytics and healthcare: A systematic review

Big Data are growing at an exponential rate and it becomes necessary the use of tools and technologies to manage, process and visualize them in order to extract value. In this paper a micro-service based platform is presented for the composition, deployment and execution of Big Data Analytics (BDA) application workflows in several domains and scenarios is presented. ALIDA is a result coming from previous research activities by ENGINEERING. It aims to achieve a unified platform that allows both BDA application developers and data analysts to interact with it. Developers will be able to register new BDA applications through the exposed API and/or through the web user interface. Data analysts will be able to use the BDA applications provided to create batch/stream workflows through a dashboard user interface to manipulate and subsequently visualize results from one or more sources. The platform also supports the auto-tuning of Big Data frameworks deployment properties to improve metrics for analytics application. ALIDA has been properly extended and integrated into a software solution for the analysis of large amounts of data from the avionic industries. A use case within this context is then presented.

Big data analytics (BDA) applications use advanced analysis algorithms to extract valuable insights from large, fast, and heterogeneous data sources. These complex BDA applications require software design, development, and deployment strategies to deal with volume, velocity, and variety (3vs) while sustaining expected performance levels. BDA software complexity frequently leads to delayed deployments, longer development cycles and challenging performance monitoring. This paper proposes a DevOps and Domain Specific Model (DSM) approach to design, deploy, and monitor performance Quality Scenarios (QS) in BDA applications. This approach uses high-level abstractions to describe deployment strategies and QS enabling performance monitoring. Our experimentation compares the effort of development, deployment and QS monitoring of BDA applications with two use cases of near mid-air collisions (NMAC) detection. The use cases include different performance QS, processing models, and deployment strategies. Our results show shorter (re)deployment cycles and the fulfillment of latency and deadline QS for micro-batch and batch processing.

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on Twitter and deemed to be within the confines of Data Mining. It is concerned with Natural Language Processing (NLP)-based sentiment analysis for Twitter’s opinion mining. As such, the objective of this work is to use a data mining approach of text-feature extraction, classification, and dimensionality reduction, using sentiment analysis to analyze and visualize Twitter users’ opinion. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to get word scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using BNN. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to extract meaningful data from tweets in real-time and store them into a relational model for analysis. And then use our dimension reduction method. This will help people discover the correlation of the leading role between them, which also reflects news media’s focuses and people’s interests. This system has proved better results with respect to accuracy and efficiency in comparison with some other similar works. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.

When changes happen to big data analytics (BDA) applications in the Cloud at runtime, the affected BDA applications have to be re-deployed to accommodate the changes. Deciding the most suitable deployment is critical and complicated. Although there have been various research studies working on BDA application management, autonomic deployment decision making is still an open research issue. This paper proposes a deployment decision making solution for BDA applications in the Cloud: first, we propose a novel language, named DepPolicy, to specify runtime deployment information as policies; second, we model the deployment decision making problem as a constraint programming problem using MiniZinc; third, we propose a decision making algorithm that can make different deployment decisions for different jobs in a way that maximises overall utility while satisfying all given constraints (e.g., cost limit); fourth, we design and implement a decision making middleware, named DepWare, for BDA application deployment in the Cloud. The proposed solution is evaluated in terms of feasibility, functional correctness, performance and scalability.

Since the computer has been used in accounting work and with the current economic development, the enterprise accounting system has formed a large amount of data in the accumulation. The application of big data technology in enterprises is mainly in the R&D department, and the application rate of the finance department is not very high. In recent years, the development of big data technology and cloud computing technology has become increasingly mature, and the function of financial accounting has also gradually shifted to manage accounting. Previously under the traditional accounting method, the data that is not fully utilized by the technology can be fully explored today by relying on big data analysis. This article expounds the application of big data analysis method in modern business accounting data, and compares a series of advantages of big data analysis method with traditional analysis method. Finally, it is mentioned that in the era of highly developed Internet, there may be problems in applying big data analysis.

Big Data Analytics (BDA) has emerged as a transformative force in healthcare, offering innovative solutions to analyze large and complex datasets for actionable insights. This systematic review, encompassing 142 peer-reviewed studies published between 2010 and 2024, explores the tools, techniques, and applications of BDA in healthcare. The findings reveal the critical role of BDA in enhancing clinical decision-making, optimizing hospital workflows, and advancing medical research. Key applications such as predictive analytics for disease prevention, real-time monitoring through IoT integration, and precision medicine through genomic analysis are highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with advanced techniques such as machine learning and natural language processing, have been pivotal in transforming healthcare data into actionable knowledge. However, the review also identifies significant challenges, including data integration issues, algorithmic bias, and ethical concerns related to patient privacy and data security. By addressing these barriers, BDA has the potential to revolutionize healthcare delivery, providing more personalized, efficient, and equitable care. This study provides a comprehensive understanding of the current state of BDA in healthcare, its limitations, and its promising future applications, offering valuable insights for researchers, policymakers, and healthcare practitioners.

Big data analytics (BDA) applications use machine learning to extract valuable insights from large, fast, and heterogeneous data sources. The architectural design and evaluation of BDA applications entail new challenges to integrate emerging machine learning algorithms with cutting-edge practices whilst ensuring performance levels even in the presence of large data volume, velocity, and variety (3Vs). This paper presents a design process approach based on the Attribute-Driven Design (ADD) method and Architecture tradeoff analysis method (ATAM) to specify, deploy, and monitor performance metrics in BDA applications supported by domain-specific modeling and DevOps. Our design process starts with the definition of architectural drivers, followed by functional and deployment specification through integrated high-level modeling which enables quality scenarios monitoring. We used two use cases from avionics to evaluate this proposal, and the preliminary results suggest advantages by integrating multiple views, automating deployment and monitoring compared to similar approaches.

With the introduction of information technology, the construction and effective operation of University intelligent campus is a stage that all universities must go through. Based on digital campus, intelligent campus comprehensively utilizes next-generation information technologies such as Internet of things, cloud computing, big data, social network and artificial intelligence to fully perceive the campus physical environment. It is an advanced form of university information development, the promotion and extension of digital campus, provides extensive IT services for teachers and students, establishes an intelligent application information system for data sharing throughout the University, and carries out campus education and scientific research. Promote the all-round innovative development of management and campus services. High school is different from traditional project construction. It is a complex system engineering integrating the next generation information technology and education management. This is mainly based on the development of Internet communication technology and big data analysis technology based on intelligent algorithms. Its construction pays more attention to the later operation, maintenance and management, which is mainly reflected in safety, applicability and sustainable development innovation. This is an information system project with a long construction cycle. We study the application of big data analysis technology based on Intelligent Algorithm in the construction of intelligent campus.KeywordsIntelligent algorithmBig dataSmart campus

The amount of data generated by the global economy is enormous. Therefore, there is a need to approach the analysis of data flowing in from different parts of the functioning of the economy in a completely different way. This is where Big Data analytics solutions come to our rescue, offering tremendous opportunities for businesses, whether they are used alone or in conjunction with existing traditional data. Researchers, analysts and business users can leverage these new data sources for advanced analytics that provide deeper insights into data and to power innovative applications. Some popular techniques include data mining, text analytics, predictive analytics, data visualization, AI (Artificial Intelligence), machine learning, statistics, and natural language processing, which have been successfully applied to cutting-edge technologies used in logistics. Taking into account the above conditions, the authors present the latest organizational and technological solutions in the aspect of using Big Data analysis to modify and improve supply chain processes. The author points out the challenges Big Data poses and what should be paid special attention to in order to effectively use its potential. Several examples of the application of Big Data analyses were presented, focusing on the most important areas of supply chains. The whole article ends with conclusions including the effects of using Big Data analyses.

Bibliometric analysis is a widely used technique for investigating and studying scientific information. There is no previous research that explains bibliometric analysis related to the adoption of big data analytics in auditing. Thus, this research will fill the gap in previous research to examine bibliometric analysis related to the adoption of big data analytics in auditing. This paper employs bibliometric analysis on Scopus-indexed journals to examine the topic of big data analytics in audits, utilizing the VOSviewer tool. The objective of utilizing bibliometric analysis in this research is to ascertain the progression of articles concerning the application of big data analytics in the field of auditing. This article discusses the development of the number of publications and citations, the trend of publication researchers, the country of publication articles, the relationship between researchers, and the relationship between words with the topic of big data analytics in the period 2010–2022. This research reveals areas of application of big data analysis adoption in auditing. Qualitative research, especially library research, is the best method widely used among writers. This study provides several useful insights into the meaning of big data and data analysis, the benefits of using big data analysis in the audit process, and how the audit process can be made easier with big data analysis. Among the most interesting insights, the results suggest that big data implies vast amounts of data that exceed the limits of what can be stored and processed. Thus, the use of data analytics helps auditors reduce cognitive errors arising from large and diverse data sets. This bibliometric research presents the number of articles and citations of research publications, which authors and countries have the most research on this topic, and the keywords/terminologies that appear most frequently as well as the meaning of these keywords/terminologies.

ACCORDANT: A domain specific-model and DevOps approach for big data analytics architectures