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

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

The healthcare industry generates vast amounts of data that are crucial for improving patient outcomes and advancing medical research. However, traditional on premise solutions for data storage and analysis can become inadequate to handle the increasing volume, variety and velocity of healthcare data. The study aims to investigate the potential benefits and challenges of using cloud-based solutions for data analytics in healthcare. This paper reports about latest development and detailed role of using Artificial intelligence and capabilities of cloud Computing in health care sector/industry to foster innovative thinking, optimum wellbeing of the patient, focused medicinal support. This paper discusses various applications, algorithms and future of big data analytics with a focus on architecture, application and applicability of big data analytics using Hadoop and Cloud Computing in healthcare industry such as monitoring, prediction, performance, management etc including intensive care unit. many cloud platforms, like MMAP, are working in this field to provide a fast, reliable cost effective, efficient, and patient centric and solution to community health issues with capability of forecasting the health impact of various diseases on community for a given region or nation. Cloud computing framework, along with Artificial intelligence and Hadoop, aids healthcare management in completing analytical computations to identify logical, pertinent, and factual trends essential to strategize and enhanced readiness in event of catastrophes by facilitating data exchange among all stake holders.

Enterprise economic efficiency is an important indicator for measuring the input and output of enterprises, and it has a certain guiding role in the formulation of enterprise management decisions. This paper constructs an enterprise economic efficiency assessment model using big data analysis and explores the application of big data analysis in enterprise management decision-making. First, we extract the principal component factors for measuring enterprise economic efficiency indexes using principal component analysis, and then construct the comprehensive evaluation function of enterprise economic efficiency based on this. The particle swarm algorithm is used to optimize the BP neural network, and the PSO-BP neural network model is constructed to intelligently evaluate the economic efficiency of the enterprise. This paper extracts three principal components of enterprise economic efficiency, namely “enterprise solvency and profitability,” “enterprise operating ability,” and “net asset growth ability,” and achieves a cumulative variance explanation rate of 84.431%. The PSO-BP neural network model and the standard BP neural network model satisfy the error criterion of MAPE<0.001 when they are trained about 170 times and 820 times, respectively, and the number of operations of the former is only about 20.73% of that of the latter, which greatly shortens the running time of the model and indicates that the PSO-BP-based enterprise economic efficiency assessment model in this paper is practical. Practicality. This paper serves as a reference for the application of big data analysis in enterprise economic benefit assessment and management decision formulation.

Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model. BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price. To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution. We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.

As we come to terms with various big data challenges, one vital issue remains largely untouched. That is service level agreement (SLA) management to deliver strong Quality of Service (QoS) guarantees for big data analytics applications (BDAA) sharing the same underlying infrastructure, for example, a public cloud platform. Although SLA and QoS are not new concepts as they originated much before the cloud computing and big data era, its importance is amplified and complexity is aggravated by the emergence of time-sensitive BDAAs such as social network-based stock recommendation and environmental monitoring. These applications require strong QoS guarantees and dependability from the underlying cloud computing platform to accommodate real-time responses while handling ever-increasing complexities and uncertainties. Hence, the over-reaching goal of this PhD research is to develop novel simulation, modelling and benchmarking tools and techniques that can aid researchers and practitioners in studying the impact of uncertainties (contention, failures, anomalies, etc.) on the final SLA and QoS of a cloud-hosted BDAA.

This paper is a research on using a big data analysis for lawyers, attorneys and law firms. Big data is a new gold — has been noticed in media and many conferences. Today big data is driven by digital transformation. It helps to analyze a huge amount of data, creates opportunities for increasing performance and gets competitive advantage for private and public sector. A legal regulation of big data is still under the question in many countries. The main aspect and confusion are privacy concerns. Meanwhile, big data analysis actively used by governments, private and public market players. The market offers different legal tech innovations, software for analyzing big data and methods that we review in the article. Interestingly, there are different tools and application of big data for lawyers; and this what each legal professional must know. In this paper we are observing some aspects of using big data analysis for legal ndustry and discussing issues of legal regulation. We provide some practical ways on application of big data analysis for legal professionals. Some insights in this article adopted from author’s previous research made during the study in the Maastricht School of Management based in the Netherlands. The research is partly published in the Second Edition of Global Legal Insights AI, Machine Learning & Big Data in 2020.

Technology has revolutionized business operations around the world. There is an ever-increasing trend to digitize business operations around the world. It is not uncommon to see the application of Big Data Analytics (BDA) throughout a vast range of industries and organizations in today's extremely competitive world. However, BDA adoption in supply chain (SC) is less accepted and research in this area remains at its infancy. BDA is extremely useful throughout supply chain functions like manufacturing, distribution, procurement, and marketing. However, a sizable portion of sectors continues to hold divergent opinions regarding big data's alleged benefits. Moreover, there is not a lot of empirical studies that have been published that addresses the adoption of such a tool or better yet, that investigates what factors affect the decision to adopt BDA in supply chain management (SCM). This study aims to develop and extend technology acceptance model (TAM) that considers the critical factors that may affect BDA adoption in SCM by critically analyzing the existing literature. Upon reviewing the literature, it became evident that the main categories of factors that have been thoroughly examined and recognized to be crucial in comprehending BDA adoption and acceptability in SCM are the organizational- and individual-related factors, such as top management and computer self-efficacy.

ObjectivesWe reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea.MethodsWe reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0). We formulated a conceptual framework of big data in the healthcare and social service sectors at the national level. As a specific case, we designed a process and method of social big data analysis on suicide buzz.ResultsDeveloped countries (e.g., the United States, the UK, Singapore, Australia, and even OECD and EU) are emphasizing the potential of big data, and using it as a tool to solve their long-standing problems. Big data strategies for the healthcare and social service sectors were formulated based on an ICT-based policy of current government and the strategic goals of the Ministry of Health and Welfare. We suggest a framework of big data analysis in the healthcare and welfare service sectors separately and assigned them tentative names: 'health risk analysis center' and 'integrated social welfare service network'. A framework of social big data analysis is presented by applying it to the prevention and proactive detection of suicide in Korea.ConclusionsThere are some concerns with the utilization of big data in the healthcare and social welfare sectors. Thus, research on these issues must be conducted so that sophisticated and practical solutions can be reached.

This study aims to explore the relationship between data science or what is known as big data and the information specialists who deal with this data and their role in analyzing it to support the decision-making process. It also seeks to introduce modern trends in big data and the skills necessary to benefit from it professionally. This study is a qualitative study based on a systematic review of previous literature. Previous studies were reviewed to identify visible and hidden opportunities due to the foresight future models to use big data in favor of planning for future. The study adopted the descriptive approach in terms of production analysis. Explain the role of data science “big data” in strategic planning and decision-making at professional institutions and companies. The study results indicate the essential and conflicting part of big data in guiding the decisions of large companies and institutions and its positive role in developing products and increasing the efficiency and profits of these companies. In order to make the most of this data, it is necessary to seek the assistance of qualified data experts in different fields who can formulate this data in a way that helps to make logical and accurate decisions and develop strategic plans. The importance of big data in the process of strategic planning and decision-making. Correspondingly, propose the practical foresight approaches to using big data to produce significant strategic planning.

Big data is a new ubiquitous term for massive data sets having large, more varied and complex structure with the complexities and difficulties of storing, analyzing and visualizing for further processes or results. The use of Big Data in health is a new and exciting field. A wide range of use cases for Big Data and analytics in healthcare will benefit best practice development, outcomes analysis, prediction, and surveillance. Consequently, the aim of this chapter is to provide an overview of Big Data in Healthcare systems including two applications of Big Data analysis in healthcare. The first one is understanding disease outcomes through analyzing Big Data, and the second one is the application of Big Data in genetics, biological, and molecular fields. Moreover, characteristics and challenges of healthcare Big Data analysis as well as technologies and software used for Big Data analysis are reviewed.

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

The era of Big Data analytics has begun in most industries within developed countries. This new analytics tool has raised motivation for experts and researchers to study its impacts to business values and challenges. However, studies which help to understand customers’ views and their behavior towards the applications of Big Data analytics are lacking. This research aims to explore and determine the pros and cons of applying Big Data analytics that affects customers’ responses in an e-commerce environment. Data analyses were conducted in a sample of 273 respondents from Vietnam. The findings found that information search, recommendation system, dynamic pricing, and customer services had significant positive effects on customers’ responses. Privacy and security, shopping addiction, and group influences were found to have significant negative effects on customers’ responses. Customers’ responses were measured at intention and behavior stages. Moreover, positive and negative effects simultaneously presented significant effect on customers’ responses. Each dimension of positive and negative factors had different significant impacts on customers’ intention and behavior. Specifically, information search had a significant influence on customers’ intention and improved customers’ behavior. Shopping addiction had a drastic change from intention to behavior compared to group influences and privacy and security. This study contributes to improve understanding of customers’ responses under big data era. This could play an important role to develop sustainable consumers market. E-vendors can rely on Big Data analytics but over usage may have some negative applications.

Big data analytics is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.

BackgroundThe field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.MethodsWe retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.ResultsA total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.ConclusionThis study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.