Era Of Big Data Analytics Research Articles

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. We developed a decision-making tool based on OpenStreetMap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To illustrate how these VRPTW algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations

In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals.

THE FUTURE OF BIG DATA ANALYTICS AND ITS PROGRESS

Big data streams are now widespread because real-time applications that generate enormous amounts of data quickly have proliferated during the past 10 years. Big data is a treasure trove of vast quantities of data. Big data is commonly used in conjunction with other current buzzwords like data science, artificial intelligence, deep learning, and machine learning. Big data has a very promising future because of the demand for data across many technical industries. A massive amount of data has been produced as a result of the COVID-19 global epidemic. Big data analytics is helpful for evaluating, maintaining, and archiving information about COVID-19 patients for later use during the pandemic phase. Big Data Analytics (BDA), a growingly important technology, can significantly enhance sustainable tourism in terms of destination selection, traveller satisfaction, and the quality of travel experiences. Streaming Big Data is the term used to describe data that needs to be saved, processed, and analysed and that is continuously generated from various data sources, including Internet of Things (IoT) devices. The article provides information on - Big data analytics techniques in social media, COVID-19 Pandemic in the New Era of Big Data Analytics, Big data analytics and sustainable tourism, Predictive big data analytics for supply chain demand forecasting, Streaming Big Data Analytics, Big data stream analysis and Big Data Analytics in Crypto Currency. Keywords: Big Data, Streaming Big Data, Predictive Big Data, Sustainable Tourism

Monitoring and diagnostics of correlated quality variables of different types

As data acquisition and processing technologies continue to advance rapidly, new challenges emerge for statistical process monitoring. One such challenge, especially in the era of big data analytics, is monitoring multivariate processes involving a mixture of continuous, categorical, and discrete quality variables. The existing multivariate control charts focus mostly on monitoring correlated variables of the same type. We propose a new Phase II control chart that is based on a modified Holm’s step-down multiple testing procedure (Holm 1979) which achieves two important goals at the same time: (1) it simultaneously monitors correlated variables of different types, while keeping the probability of false alarm under desirable level, and (2) when the process is determined to be out of control, it further provides, without any additional efforts, diagnostics to pinpoint which parameters are out of control. The proposed chart is shown to outperform the existing charts particularly in its ability to provide more accurate diagnostics.

Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics

Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth’s land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.

Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote

When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the areas of econometrics and statistics, among other fields, these diagnostics are controversial concerning their “successfulness”. It has been remarked that they frequently fail to do proper model assessment due to information complexity, resulting in model misspecification. This work proposes and investigates a robust and easily interpretable methodology, termed Elastic Information Criterion, capable of capturing multicollinearity rather accurately and effectively and thus providing a proper model assessment. The performance is investigated via simulated and real data.

Hybrid design for sports data visualization using AI and big data analytics

In sports data analysis and visualization, understanding collective tactical behavior has become an integral part. Interactive and automatic data analysis is instrumental in making use of growing amounts of compound information. In professional team sports, gathering and analyzing sportsperson monitoring data are common practice, intending to evaluate fatigue and succeeding adaptation responses, analyze performance potential, and reduce injury and illness risk. Data visualization technology born in the era of big data analytics provides a good foundation for further developing fitness tools based on artificial intelligence (AI). Hence, this study proposed a video-based effective visualization framework (VEVF) based on artificial intelligence and big data analytics. This study uses the machine learning method to categorize the sports video by extracting both the videos' temporal and spatial features. Our system is based on convolutional neural networks united with temporal pooling layers. The experimental outcomes demonstrate that the recommended VEVF model enhances the accuracy ratio of 98.7%, recall ratio of 94.5%, F1-score ratio of 97.9%, the precision ratio of 96.7%, the error rate of 29.1%, the performance ratio of 95.2%, an efficiency ratio of 96.1% compared to other existing models.

COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

AbstractAlthough scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID‐19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study ‘black swan’ events such as the COVID‐19‐related global crisis and its aftermath's implications for managers and policymakers.

Enhancing the agro engineering system using game theory analytics

The advent of technologies and the impact of computer play an essential role in performing regular activities. The new era of Big Data analytics helps in analyzing the data through primary data and secondary data. By using modern tools to handle Distributed File systems enormous data can be handled and processed with high speed and accuracy in a stipulated time. The use of these emerging trends in all the fields gives better solution and strives the world in all aspects of digitization in a successful manner. To produce high yields in food crops, the agriculture adopted modern industrial systems. By using modern tools and techniques the agricultural production can be increased abundantly. The proposed system will help and attract the common people to invest their hard work and time in agricultural production, and make a huge contribution in the financial growth of the nation. The tractors and agro engineering tools for irrigation systems, are already used to improve the production of food crops. But the lack of advanced techniques such as smart system for monitoring the crop, to look for the strength of the soil through soil analyzer, checking for moisturisation in land and air, etc., are very much needed for proper yield. This system presents the novel framework and initial experimental results shows the feasibility of proposed system.

Information technology capability and firm performance in the era of Big Data analytics: Evidence from the United States

Research Question: This study investigates the relationship between information technology (IT) capability and firm performance in the 2010s, the era of big data analytics (BDA), in the context of US companies. Motivation: With the evolution of business intelligence and the proliferation of analytic tools that further improve IT capability, it is more important than ever to understand whether firms with stronger IT capabilities perform better. Idea: After categorizing firms into pairs of IT leaders and control groups, the performance of each pair of firms in each group was compared. Data: All data are publicly available from Compustat and InformationWeek (IW) 500. Tools: The Wilcoxon signed-rank test and regression analysis were used to examine how the performance of IT leaders and control groups changed during the 2010–2017 period. Findings: This results show no significant relationship between a firm’s IT capability and its performance in the sample of US companies during this period. Contribution: This study will help academicians and practitioners to better understand how the adoption and application of BDA derived from IT capability affects firm performance.

A DATA-DRIVEN MADM MODEL FOR PERSONNEL SELECTION AND IMPROVEMENT

Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.

Study of Analysing and Processing of Big Data by Organizations in Indian Context

Every business organization needs valuable data and insights for understanding audience intent and consumer’s likings. Big data in this acts as a significant part as it supports in precede the needs of customers for which the data needs to be well presented and appropriately analyzed. Big Data permits organization to segregate customers in broad way which permits a business to hold consumers in a real-time, as in this tough competitive time you need to treat customers how they want. In simplified term “Big Data is a mix of processes and tools by which huge data grid through various form with each other and enormous amount of heterogeneous and rationalized information is created which in addition, used to figure out the utmost valuable customers. It also provide assistance for businesses to innovate, create and pitch new experiences, services, and products. The availability of such information creates opportunities for organizations. The paper here discusses about big data elements, its maintenance, handling and storage of varieties of big data by organizations and gains of big data analytics to organizations. The paper also analyze about the velocity of data generated and analysis of big data by organizations to understand its impact on organization working and consumer decision respectively. The paper also gives avenues for future research by explaining the application and practices of organization in the era of big data analytics.

Automated data-driven profiling: threats for group privacy

PurposeUser profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge.Design/methodology/approachFocusing on a collective level, the authors conducted an extensive literature review related to information privacy and concepts of social identity. They also examined numerous automated data-driven profiling techniques analyzing at the same time the involved privacy issues for groups.FindingsThis paper identifies privacy threats for collective entities that stem from data-driven profiling, and it argues that privacy-preserving mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Moreover, this paper concludes that collective privacy threats may be different from threats for individuals when they are not members of a group.Originality/valueAlthough research evidence indicates that in the age of big data privacy as a collective issue is becoming increasingly important, the pluralist character of privacy has not yet been adequately explored. This paper contributes to filling this gap and provides new insights with regard to threats for group privacy and their impact on collective entities and society.

Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.

Big Data Analytics : Implications and Prospects for Accounting Professionals

Technology is constantly changing. Smartphones, tablets, wireless access, networks, portals and the cloud have changed how the accounting and finance department uses data. Big data and Big Data Analytics impact nearly every aspect of accounting, including audit, tax, and managerial accounting.It helps in identifying various opportunities for improvement, and aid in evaluating global opportunities. Accounting professionals will need cutting-edge data analytics skills to thrive in this changing landscape. Accounting professionals should try to enhance their own capabilities in terms of both technical and analytical/statistical skills & knowledge. The present paper is basically a theoretical paper covering various aspects of Big Data and Big Data Analytics apart from discussing the opportunities available to accounting professionals in the era of Big Data Analytics. The paper also throws light on various implications of Big Data Analytics in accounting and finance area.

Orthodontics in the era of big data analytics

The objective of this report was to provide an overview of the current landscape of big data analytics in the healthcare sector, introduce various approaches of machine learning and discuss potential implications in the field of orthodontics. With the increasing availability of data from various sources, the traditional analytical methods may not be conducive anymore for examining clinical outcomes. Machine-learning approaches, which are algorithms trained to identify patterns in large data sets, are ideally suited to facilitate data-driven decision making. The field of orthodontics is particularly ripe for embracing the big data analytics platform to improve decision making in clinical practice. The availability of omics data, state-of-the-art imaging and potential for establishing large clinical data repositories have favourably positioned the specialty of orthodontics to deliver personalized and precision orthodontic care. Specifically, we discuss about next-generation sequencing, radiomics in the context of CBCT imaging, and how centralized data repositories can enable real-time data pooling from multiple sources.

Planning, designing and conducting establishment-based freight surveys: A synthesis of the literature, case-study examples and recommendations for best practices in future surveys

The state-of-practice in planning, designing, and conducting freight surveys leave much to be desired, even in the era of big data analytics. This paper addresses this issue by providing a comprehensive, yet, inexpensive integrated data collection framework for conducting establishment-based freight survey (EBFS). The paper demonstrates the application of the proposed framework by implementing it in eight cities across two geographically dissimilar states of India. This is the first freight survey of its kind in terms of scale and scope in developing countries, where there is no established practice in freight data collection. Guidelines are suggested for overcoming challenges in EBFS such as: (a) efficient allocation of survey resources within budgetary constraints; (b) effective survey instrument design for reducing the respondent burden; (c) determination of sample size requirements and the expected number of sampling units to be contacted (d) development of sampling strategies using sampling frames with limited auxiliary information; and (e) data collection strategies to improve response rates. The heuristics for allocation of survey resources are mathematically formulated and predicted using the web-based survey responses obtained from planners of past surveys. Trade-off scenarios between different components of survey resources (money, time, and manpower) are presented to enable the planners to arrive at a suitable EBFS design for meeting research requirements within resource constraints. Analysis of results suggest that the response rates for EBFS are largely linked to the physical characteristics of commodities and city demography. The discussions provided on resource allocation, survey instrument design, sampling design, pilot surveys, interviewer training, response rate improvement strategies, and data processing are expected to guide for best practices in future surveys. In sum, the literature synthesis, case-study illustrations, and the proposed framework for EBFS design are expected to strengthen the state-of-practice of EBFS by making the rigorous random sample surveys less expensive, more systematic, and in turn, replacing the need to opt for convenience samples.

Privacy As Protection of the Incomputable Self: Agonistic Machine Learning

This paper takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy I expand previous work on the relational and ecological nature of privacy and the productive indeterminacy of human identity. Second, I will explain that this does not imply a rejection of machine learning, based on a more in-depth study of the assumptions, operations and implications of the practice of machine learning – highlighting its alignment with purpose limitation as core to its methodological integrity. Instead of rejecting machine learning, I advocate a practice of ‘agonistic machine learning’ as core to scientifically viable integration of data-driven applications into our environments while simultaneously bringing them under the Rule of Law. This should also provide the best means to achieve effective protection against overdetermination of individuals by machine inferences.

Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning

Abstract This Article takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy, I expand previous work on the relational nature of privacy, and the productive indeterminacy of human identity it implies, into an ecological understanding of privacy, taking into account the technological environment that mediates the constitution of human identity. Second, I will investigate how machine learning actually works, detecting a series of design choices that inform the accuracy of the outcome, each entailing trade-offs that determine the relevance, validity and reliability of the algorithm’s accuracy for real life problems. I argue that incomputability does not call for a rejection of machine learning per se but calls for a research design that enables those who will be affected by the algorithms to become involved and to learn how machines learn — resulting in a better understanding of their potential and limitations. A better understanding of the limitations that are inherent in machine learning will deflate some of the eschatological expectations, and provide for better decision-making about whether and if so how to implement machine learning in specific domains or contexts. I will highlight how a reliable research design aligns with purpose limitation as core to its methodological integrity. This Article, then, advocates a practice of “agonistic machine learning” that will contribute to responsible decisions about the integration of data-driven applications into our environments while simultaneously bringing them under the Rule of Law. This should also provide the best means to achieve effective protection against overdetermination of individuals by machine inferences.

Big Data and Advanced Analytics in Industry 4.0: a comparative analysis across the European Union

Digital economy and Factory (Industry) 4.0 are among the main challenges in the era of Big Data Analytics. A digital transformation is required since factories and enterprises need to face quick changes in the technological process and continuous flows of massive data, in order to improve the decision making process. First, we want to investigate the actual scenario in Europe. For instance, the data from the Digital Economy Society Index (DESI) by the European commission (available at https://ec.europa.eu/digital-single-market/en/countries-performance-digitisation) gives on overview on countries’ performance digitization. The report measures such factors as connectivity, digital skills, digital public services, and more. In particular, it is of interest to analyze the different incidence of employers with digital skills (Analytics, Cybersecurity, Cloud, for instance) on competitiveness and efficiency among countries and industrial sectors, with a privileged eye on Spain and Italy, and to investigate the profile of those industrial sector that are actually investing in digital skills and those that are not.