Big Data Analytics Tools Research Articles

Big Data Analytics-based life cycle sustainability assessment for sustainable manufacturing enterprises evaluation

Recently, governments and organizations have repeatedly pressed manufacturing enterprises to promote the ethical and transparent use of natural resources, lessen their negative effects on national and international ecosystems, and safeguard people and the environment. In this context, enhancing the various stages of the product/service life cycle to fulfill sustainability requirements and foster sustainable value creation is a key area of interest for researchers and professionals. This emphasis reflects the growing recognition of the importance of minimizing the environmental impact of products and services, while also maximizing their positive contributions to society, economy, and environment. To this end, this research work addresses how manufacturing enterprises benefit from life cycle sustainability assessment (LCSA) thinking to incorporate the environmental and social criteria into the product/service life cycle strategies. To do so, a novel approach based on environmental priority strategy (EPS) as an LCSA method for impacts monetization coupling with Big Data Analytics (BDA) techniques and tools is developed to evaluate and analyze the manufacturing enterprises’ impacts on the environment and society. Moreover, the developed approach evaluates manufacturing enterprises’ progress toward sustainable development goals (SDGs). Finally, to demonstrate the applicability of the developed approach, a case study from the corporate environmental impact database is used, and the obtained numerical results are analyzed showing its efficiency and added value.

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

빅데이터 분석을 활용한 중소기업, 소상공인 ESG 경영 이슈 분석

In the context of experiencing unexpected external factors like COVID-19, businesses are facing a significant demand for change. The management focus on ESG has emerged as a practical issue concerning the survival and sustainability of companies.
 Existing research has primarily concentrated on empirical analysis based on ESG evaluation systems, with relatively little attention given to analyzing key ESG-related keywords. This study aims to fill the gap in such research.
 To achieve this, the study utilized big data from news channels operating on major portal sites such as Naver, Daum, and Google. Through the application of big data analysis tools, the study performed the extraction and analysis of ESG-related keywords targeting small and medium-sized enterprises (SMEs) and small businesses.
 The ESG keywords extracted through the use of big data analysis tools contribute to the ongoing discussions about integrated approaches to ESG evaluation criteria. Additionally, they provide foundational data necessary for developing differentiated ESG strategies based on the size of the companies. As a result, this study holds significance in deriving both academic and practical insights.

SINIRLARIN ÖTESİNDEKİ DİJİTAL İZLER: GÖÇ KRİZİ ÜZERİNE SİSTEMATİK BİR DERLEME

This study aimed to systematically examine the studies conducted and published on immigrants, asylum seekers, and refugees by using big data written in English. Articles were searched on Scholar, The Web of Science, ProQuest, Science Direct, PubMed and Scopus databases. The concept set centered around the concepts of immigration and big data was used in the surveys. In accordance with the PRISMA protocol principles, 49 articles were examined according to the inclusion and exclusion criteria among 258 articles obtained from the relevant databases until the end of December 2022. The reviewed articles were categorized under the headings of “topics examined”, “dataset”, “analyses”, “software used” and “key findings”. The studies provide indications on how to obtain information about this population, which is difficult to reach group especially due to its massiveness, using big data tools. In the findings, it has been seen that studies based on big data on immigrants, asylum seekers and refugees contribute to facilitating the integration of these groups into the target country. Also, it has been revealed that these studies may lead to undesirable results in terms of violating the confidentiality of research groups, producing labeling, and increasing surveillance for these groups. In addition to these, it has been found that these studies have methodological handicaps in terms of representativeness, accuracy, excessive homogenization, and easy generalization. It is thought that the findings of the study will shed light on the international migration and refugee policies to be carried out using big data analysis tools.

Decision support model for big data analytics tools

Decision support model for big data analytics tools

An enhanced distributed framework for real-time performance testing of large scale IoT dataset using big data analytic tools

<p>The demand for analyzing enormous IoT datasets is rising in parallel with the popularity of the IoT. There are considerable obstacles to effective processing and analysis due to the amount, velocity, and variety of IoT data. In this research, we present a distributed system that makes use of big data analytic tools like Apache Hive, Spark, and Hadoop to efficiently test the performance of massive IoT datasets. The framework addresses the lack of a comprehensive solution by providing a scalable and fault-tolerant architecture. We discuss the motivation behind real-time performance testing in the context of big data analytics for IoT datasets and highlight the need for a distributed framework. A literature review is conducted to explore existing performance testing frameworks, big data analytic tools, and approaches for performance testing big data analytics. The proposed framework’s key components, including dataset generation, test scenario specification, cluster configuration, performance metrics collection, analysis and visualization modules, and implementation details, including tool choices, are discussed. An experimental evaluation is conducted to validate the framework’s performance, and it is suggested to incorporate blockchain technology. Overall, the proposed framework offers a comprehensive solution for real-time performance testing of large-scale IoT datasets, providing organizations and researchers with a valuable tool to ensure efficient and reliable IoT data processing and analysis.</p>

Exploiting organizations' innovation performance via big data analytics: an absorptive knowledge perspective

PurposeThe advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.Design/methodology/approachThis study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.FindingsThe authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.Originality/valueThese results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

A Reference Architecture for Supporting Multidimensional Big Data Analytics over Big Web Knowledge Bases: Definitions, Implementation, Case Studies

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over Big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.

Leveraging SMEs technologies adoption in the Covid-19 pandemic: a case study on Twitter-based user-generated content

The COVID-19 pandemic has caused many entrepreneurs and small and medium enterprises (SMEs) to adapt their business models and business strategies to the consequences caused by the pandemic. In order to identify the main innovations and technologies adopted by SMEs in the pandemic, in the present study, we used a database of 56,941 tweets related to the coronavirus to identify those that contained the hashtag #SMEs. The final sample was analyzed using several data-mining techniques such as sentiment analysis, topic modeling and textual analysis. The theoretical perspectives adopted in the present study were Computer-Aided Text Analysis, User-Generated Content and Natural Language Processing. The results of our analysis helped us to identify 15 topics (7 positive: Free support against Covid-19, Webinars tools, Time Optimizer and efficiency, Business solutions tools, Advisors tools, Software for process support and Back-up tools; 4 negative: Government support, Payment systems, Cybersecurity problems and Customers solutions in Cloud, and and 4 neutral: Social media and e-commerce, Specialized startups software, CRMs and Finance and Big data analysis tools). The results of the present study suggest that SMEs have used a variety of digital tools and strategies to adapt to the changing market conditions brought on by the pandemic, and have been proactive in adopting new technologies to continue to operate and reach customers in a connected era. Future research should be directed towards understanding the long-term effects of these technologies and strategies on entrepreneurial growth and value creation, as well as the sustainability of SMEs in the new era based on data-driven decisions.

Assisting small Caribbean islands in health sector planning post pandemic: A brief discussion

The Caribbean Region has been one the hardest hit by the COVID-19 pandemic due to vaccine inequity, human resource constraints, and pre-existing infrastructural constraints, which led to countries taking viral mitigation and prevention measures for instance border lockdown and states of emergency. While at that phase, treating COVID-19 patients has been the number one priority, several other health services have been neglected, threatening public health. During that period there was significant disruption of healthcare delivery to patients with Chronic non-communicable Diseases in the region which deteriorated capacity issues in the health system, for example Human Resource Deficiencies, Financing of the Health Sector, Governance, and a lack of Health Information Systems. This paper provides an overview of how pandemic insurance claims and big data analytics tools can assist in gaining insights into the current state of the population’s health. Big data and analytical approaches provide a variety of solutions, including the detection of current COVID-19 cases and the forecasting of future outbreaks which can aid in obtaining some insight into the present state of the health of the population.

Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research

AbstractData science and big data analytics (DS &BDA) methodologies and tools are used extensively in supply chains and logistics (SC &L). However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise DS &BDA application areas in the SC &L comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose an unique systematic review methodology for the field of DS &BDA in SC &L. Second, we use the methodology proposed for a systematic literature review on DS &BDA techniques in the SC &L fields aiming at classifying the existing DS &BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. We analyse 364 publications which use a variety of DS &BDA-driven modelling methods for SC &L processes across different decision-making levels. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of DS &BDA in SC &L.

Precipitation forecasting using machine learning in the region of Beni Mellal-Khenifra

Agriculture in the region of Beni Mellal-Khenifra, Morocco relies on irrigation from rain and dams, but recently there has been a lack of precipitation which may negatively affect crop growth. This has made accurate precipitation forecasts even more important for farmers, as they need this information to make informed decisions about their crops. However, a lack of data-driven research utilizing past data presents a challenge for the development of such research and leaves farmers relying solely on weather forecasts from TV, which cannot relied upon in systems such as irrigation. The objective of this paper is to propose various approaches for forecasting precipitation in the region of Beni Mellal-Khenifra using big data analytics and machine learning techniques. The study made use of Apache Spark, a big data analytics tool, and five machine-learning algorithms: Lasso regression, ridge regression, elastic net, auto regressive integrated moving average, and random forest. These algorithms were applied on dataset of daily rainfall from 2000 to 2015 to forecast the amount of precipitation in the region. The results of the study showed that the random forest algorithm had the lowest mean absolute error, making it the most effective at forecasting precipitation in the region.

Hybrid Device Fabrication Using Roll-to-Roll Printing for Personal Environmental Monitoring.

Roll-to-roll (R2R) printing methods are well known as additive, cost-effective, and ecologically friendly mass-production methods for processing functional materials and fabricating devices. However, implementing R2R printing to fabricate sophisticated devices is challenging because of the efficiency of material processing, the alignment, and the vulnerability of the polymeric substrate during printing. Therefore, this study proposes the fabrication process of a hybrid device to solve the problems. The device was created so that four layers, composed of polymer insulating layers and conductive circuit layers, are entirely screen-printed layer by layer onto a roll of polyethylene terephthalate (PET) film to produce the circuit. Registration control methods were presented to deal with the PET substrate during printing, and then solid-state components and sensors were assembled and soldered to the printed circuits of the completed devices. In this way, the quality of the devices could be ensured, and the devices could be massively used for specific purposes. Specifically, a hybrid device for personal environmental monitoring was fabricated in this study. The importance of environmental challenges to human welfare and sustainable development is growing. As a result, environmental monitoring is essential to protect public health and serve as a basis for policymaking. In addition to the fabrication of the monitoring devices, a whole monitoring system was also developed to collect and process the data. Here, the monitored data from the fabricated device were personally collected via a mobile phone and uploaded to a cloud server for additional processing. The information could then be utilized for local or global monitoring purposes, moving one step toward creating tools for big data analysis and forecasting. The successful deployment of this system could be a foundation for creating and developing systems for other prospective uses.

Towards data fusion-based big data analytics for intrusion detection

ABSTRACT Intrusion detection is seen as the most promising way for computer security. It is used to protect computer networks against different types of attacks. The major problem in the literature is the classification of data into two main classes: normal and intrusion. To solve this problem, several approaches have been proposed but the problem of false alarms is still present. To provide a solution to this problem, we have proposed a new intrusion detection approach based on data fusion. The main objective of this work is to suggest an approach of data fusion-based Big Data analytics to detect intrusions; It is to build one dataset which combines various datasets and contains all the attack types. This research consists in merging the heterogeneous datasets and removing redundancy information using Big Data analytics tools: Hadoop/MapReduce and Neo4j. In the next step, machine learning algorithms are implemented for learning. The first algorithm, called SSDM (Semantically Similar Data Miner), uses fuzzy logic to generate association rules between the different item sets. The second algorithm, called K2, is a score-based greedy search algorithm for learning Bayesian networks from data. Experimentation results prove that – in both cases – data fusion contributes to having very good results.

Governance and sustainability of distributed continuum systems: a big data approach

Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body’s self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge; second, it can leverage the knowledge to monitor and predict future conditions; and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system’s downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through Bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient.

Exploring the Intersection of Big Data and Legal Analytics: A Survey of its Application in the Legal Industry

Over the last decade, we have witnessed exponential growth in the usage of Big Data in a wide range of domains such as the entertainment industry, sports industry, financial industry etc. The legal industry is starting to take notice of Big Data analytics in their field. Lawyers have been using big data analytics tools for their daily purposes which include billing, marketing and customer relations functions. For the survey, "Legal Analytics" was defined as software that uses artificial intelligence to search through massive amounts of data and identify trends that are useful to lawyers. The connection between Big Data (BD) and law can be visualised in several ways. More and more law firms are turning to Big Data to identify which cases are likely to be straightforward and which cases will have hidden hurdles to overcome. In this paper, we have attempted to compare and take a deeper look at some of the algorithms and platforms built and developed for data analytics, namely Auto-Regressive Integrated Moving Average (ARIMA), Structural Equation Modelling (SEM), Long Short – Term Memory (LSTM) and a platform known as many Laws which is used for fetching queries and specific laws through a vast database of European laws and regulations. We have further studied how different parameters have affected the results in the case of models such as ARIMA and have taken a look into a text extractor architecture that incorporates the use of LSTM in one of its layers to extract relevant information.

Cultural Content in the Youth’s Social Media on Likupang Super Priority Tourism Destination

There are 1340 ethnic groups comprising Indonesia, a country with a rich culture and has a lot to offer to the tourism industry. This cultural wealth needs to be communicated to the community, including the younger generation, as the younger generation and the largest group of users of social media, as well as a potential market for the tourism business. Experience sharing activities carried out through social media have a positive and significant influence on travel intentions. Millennials and Generation Z who are fluent in social media, have a role in increasing tourism activities. Ideally, these cultural contents are shared abundantly on social media. Then, how is cultural content shared on social media about the super priority destination of Likupang. The research aims to analyze the cultural content in the social media of Likupang super priority tourist destination among the younger generation. The research is conducted in a qualitative case study method, where data collection was carried out through social media observations using big data analysis tools, and forum group discussion with the younger generation, academics, and tourism practitioners in the Likupang area. This research found variations in cultural content in Likupang tourism include language, clothing, arts and culture, local wisdom, culinary, leisure activities. Secondly, regarding narration and storytelling. Of the many kinds of uploaded content, narrative content has its own charm, even above it has been found that the content has the 7th highest engagement among 1000 uploads with the hashtag #Likupang. Thirdly, the selection of the method in conveying the narrative and storytelling, which micro-influencers' content aims to increase consumer travel conversion, more than brand recognition among a larger audience.

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

AbstractMicro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.

Research on the integrated and innovative teaching mode of probability and statistics courses under the background of big data

Big data has become a buzzword in many industries, forums and economic exchanges, and its development is interrelated and permeated with mathematics, computers and mathematical statistics courses. Probability theory and statistics, as tools for big data analysis, often belong to different teaching departments, and teaching and research are separated, this paper is based on the Department of Economics and Statistics doing innovative reforms in the discipline foundation, course class connotation and talent training direction to achieve two This paper is based on an innovative reform of the Department of Economics and Statistics in terms of the foundations of the subject, the content of the course and the direction of personnel training. Vertically, realize the theoretical content integration of "high school probability statistics university probability theory and mathematical statistics university statistics big data and statistics"; In the horizontal expansion of the teaching process, grasp the context of "theoretical system interesting cases mathematical modeling ideas project-based embedded teaching competition participation guidance theory and practice review and strengthening"; Form a student-centered and output-oriented integrated and innovative teaching model.

Customer-centered data power: Sensing and responding capability in big data analytics

In recent years, data-driven strategy formulation has become a trend in business to gain competitive advantages. Therefore, a company’s customer perception and responsiveness play an important role. Previous research has not explored how to effectively use big data to quantitatively understand customers. Thus, this study collects top managers’ opinions from different companies and applies a quantitative method to empirically examine the proposed model to enhance operations. This finding suggests that using big data analysis tools effectively enhances customer sensing and response capabilities. Furthermore, customer sensing and responding capabilities significantly lead to new product success. The implications are discussed considering both theoretical and practical perspectives.