Area Of Big Data Analytics Research Articles

A Review: Application of Big Data Analytics in Airlines Industry

Technological advancements have led to the generation of massive amounts of data across industries, including the airline sector. Leveraging big data analytics allows valuable insights to be derived from these vast datasets. A total of twenty research papers were reviewed, and electronic databases such as Web of Science, ScienceDirect, Google Scholars, and IEEE Xplore were utilized to gather relevant studies. The search keywords included "big data analytics," "airlines industry," "data mining," "predictive analytics," and "machine learning." The findings highlight the diverse application areas of Big Data Analytics (BDA) in the airline industry, including airline operation and optimization, service quality and customer satisfaction, risk management and safety, and aircraft maintenance. The review also identifies key challenges associated with BDA implementation in the airline industry: data integration, regulatory compliance, and data privacy and security concerns. Overall, this review paper provides valuable insights into the application of BDA in the airline industry and the associated challenges. It contributes to the existing knowledge base and offers a foundation for future research and practical implementations in this domain.

An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management

Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern business intelligence systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect and process vast amounts of data, falling under the category of big data. Managing and analyzing the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s information can generate meaningful insights into business tactics. In this regard, two of the popular data management systems in the area of big data analytics (i.e., data warehouse and data lake) act as platforms to accumulate the big data generated and used by organizations. Although seemingly similar, both of them differ in terms of their characteristics and applications. This article presents a detailed overview of the roles of data warehouses and data lakes in modern enterprise data management. We detail the definitions, characteristics and related works for the respective data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art. Finally, we provide a perspective on the challenges and promising research directions for the future.

SMARTEN—A Sample-Based Approach towards Privacy-Friendly Data Refinement

Two factors are crucial for the effective operation of modern-day smart services: Initially, IoT-enabled technologies have to capture and combine huge amounts of data on data subjects. Then, all these data have to be processed exhaustively by means of techniques from the area of big data analytics. With regard to the latter, thorough data refinement in terms of data cleansing and data transformation is the decisive cornerstone. Studies show that data refinement reaches its full potential only by involving domain experts in the process. However, this means that these experts need full insight into the data in order to be able to identify and resolve any issues therein, e.g., by correcting or removing inaccurate, incorrect, or irrelevant data records. In particular for sensitive data (e.g., private data or confidential data), this poses a problem, since these data are thereby disclosed to third parties such as domain experts. To this end, we introduce SMARTEN, a sample-based approach towards privacy-friendly data refinement to smarten up big data analytics and smart services. SMARTEN applies a revised data refinement process that fully involves domain experts in data pre-processing but does not expose any sensitive data to them or any other third-party. To achieve this, domain experts obtain a representative sample of the entire data set that meets all privacy policies and confidentiality guidelines. Based on this sample, domain experts define data cleaning and transformation steps. Subsequently, these steps are converted into executable data refinement rules and applied to the entire data set. Domain experts can request further samples and define further rules until the data quality required for the intended use case is reached. Evaluation results confirm that our approach is effective in terms of both data quality and data privacy.

Big data analytics in supply chain management: a systematic literature review

PurposeThe purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.Design/methodology/approachThis paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.FindingsThis paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.Research limitations/implicationsThe scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.Originality/valueThis paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

Impacts of big data on accounting

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

Big Data Intelligent Networking

The thirteen articles in this special section focus on big data analytics for intelligent networking. The Internet of Things (IoT) is likely to have a significant impact on human lives as new services and applications are developed through integration of the physical and digital worlds. IoT is an umbrella term referring to a large number of sensing and actuation devices connected to the Internet. The vast amounts of data will be generated from those devices and form big data to provide smarter living and/or improve production efficiency. The huge amount of data opens new challenges in the era of new data-driven solutions, which also have significant influence on communication networks. Current networks are often designed based on static end-to-end design principles hindering the efficient and intelligent provisioning of big data. This special issue features recent and emerging advances in the areas of big data analytics in networking applications and networking for big data

The origins of business analytics and implications for the information systems field

ABSTRACTLike many other disciplines, the information systems (IS) community has embraced big data analytics and data science. However, in the rush to exploit the popularity of this latest trend, the areas of big data analytics and data science that are most relevant to the IS field are not made clear. While many consider data analytics as an evolution of decision support systems (DSS), that is, as a technology that needs to be managed or enhanced, this essay traces the complex origins and philosophy of analytics instead back to Luhn’s text analytics in the late 1950s, Naur’s Computing as a Human Activity and his datalogy, Tukey’s Future of Data Analysis of the 1960s, and Codd’s relational database schema in the 1970s, well before big data analytics and data science became industry buzzwords. Many of what is now considered mainstream thinking in big data analytics and data science can be traced back to these visionaries. This essay examines the implications of the complex origins of data analytics and data science for the IS field, specifically on how those different discourses impact future research and practice.

Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real‐world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state‐of‐the‐art discipline. An ignorance of observing the progression of this fast‐growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics.This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining

Big data analytics in financial reporting and accounting

Purpose- This paper aims to discuss the impact of big data analytics on financial reporting and accounting within the institutional framework. Methodology- The paper’s design is a qualitative study based on in-depth interviews with accounting professionals in different industries to determine and analyze potential uses and impacts of big data analytics in financial reporting and accounting. Findings- The nature of accounting and financial reporting do not alter but traditional methods of recording, collecting and analyzing accounting information change. The revenue recognition in many industries including airways and telecommunication companies needs to process a large volume of data because of the complex billing systems. Furthermore, the big data and analytics present a pressing issue and in the same time a real opportunity for accountants working in forensic and valuation areas. Conclusion- We concluded that the companies are tracking the policies and procedures of BDA adopted by other firms in trying to assure the legitimacy of their firm and to maintain and enhance their competitive advantages. In addition, financial reporting and accounting professionals try to adapt to the challenges regarding using and mastering new technologies and applications, they need to improve their capabilities and skills in the areas of big data analytics.

Emergent models, frameworks, and hardware technologies for Big data analytics

Today’s state-of-the-art Big data analytics engines handle masses of data, but will reach to their limits, as the future Big data flood is predicted to still grow with an increasing speed. Hence we need to think about the next development phase and future features of Big data analytics engines. In this paper, we discuss possible future enhancements in the area of Big data analytics with focus on emergent models, frameworks, and hardware technologies. We point out a selection of new challenges and open research questions.

Big data analytics for network and service management

Big data analytics for network and service management

Scalable real-time classification of data streams with concept drift

Inducing adaptive predictive models in real-time from high throughput data streams is one of the most challenging areas of Big Data Analytics. The fact that data streams may contain concept drifts (changes of the pattern encoded in the stream over time) and are unbounded, imposes unique challenges in comparison with predictive data mining from batch data. Several real-time predictive data stream algorithms exist, however, most approaches are not naturally parallel and thus limited in their scalability. This paper highlights the Micro-Cluster Nearest Neighbour (MC-NN) data stream classifier. MC-NN is based on statistical summaries of the data stream and a nearest neighbour approach, which makes MC-NN naturally parallel. In its serial version MC-NN is able to handle data streams, the data does not need to reside in memory and is processed incrementally. MC-NN is also able to adapt to concept drifts. This paper provides an empirical study on the serial algorithm’s speed, adaptivity and accuracy. Furthermore, this paper discusses the new parallel implementation of MC-NN, its parallel properties and provides an empirical scalability study.

Big Data Analytics for Medication Management in Diabetes Mellitus

Medication development plays a prominent role in the fight against chronic illness such as hypertension, diabetes mellitus, asthma, etc. Without proper testing and methods for management of drug data, the disease management would fail. Providers rely on pharmaceutical companies to provide research data in widespread formats and pharmaceutical companies rely on hospitals for electronic medical record data (EHR) and for pharmacy refill records from insurance companies. Big Data Analytics (BDA) provides an excellent basis to examine and manage terabytes of data that comprises drug data and can manage all aspects of drug development. This survey paper examines the current literature to determine what is current practice in the area of Big Data analytics and medication management.

Survey of Object-Based Data Reduction Techniques in Observational Astronomy

Abstract Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.

Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector

High-cardinality attributes are categorical attributes that contain a very large number of distinct values, like for example: family names, ZIP codes or bank account numbers. Within a predictive modeling setting, such features could be highly informative as it might be useful to know that people live in the same village or pay with the same bank account number. Despite this notable and intuitive advantage, high-cardinality attributes are rarely used in predictive modeling. The main reason for this is that including these attributes by using traditional transformation methods is either impossible due to anonymization of the data (when using semantic grouping of the values) or will vastly increase the dimensionality of the data set (when using dummy encoding), thereby making it difficult or even impossible for most classification techniques to build prediction models. The main contributions of this work are (1) the introduction of several possible transformation functions coming from different domains and contexts, that allow the inclusion of high-cardinality features in predictive models. (2) Using a unique data set of a large energy company with more than 1 million customers, we show that adding such features indeed improves the predictive performance of the model significantly. Moreover, (3) we empirically demonstrate that having more data leads to better prediction models, which is not observed for “traditional” data. As such, we also contribute to the area of big data analytics.