Big Data Issues Research Articles

Fuzzy based Decision-Making Algorithm for Solving Big Data Issues in Smart Cities

To better provide urban services and build an increasingly sustainable architecture, big data can be used to make more efficient use of current assets while enhancing the caliber of services offered to local inhabitants. However, there are several challenges to incorporating big data into existing infrastructure. Therefore, this research aims to determine the problems associated with Big Data’s effectiveness in developing intelligent towns and to investigate the connections between those difficulties. The 14 issues with Big Data were found through a literature study, and the precision was checked by feedback from professionals. Next, we employ a combined approach based on fuzzy interpretation, Structured simulation, and the Fuzzy Making Decisions Trial and Assessment Laboratories to decipher the connections between our identified problems incorporating Big Data into the development of smart cities is hampered, as shown by the analysis of links between challenges, primarily by the heterogeneous inhabitants in developed cities and the lack of connectivity. The findings of this study will provide creative city practitioners and policy planners with the information they need to successfully tackle these obstacles, clearing the way for the widespread adoption of smart city technologies. This research is a first step towards creating an interpretive structural model of the difficulties brought on by Big Data in cutting-edge urban planning. The study attempts, in part, to use this paradigm to better understand the relationship among the highlighted issues.

A framework of Polar CanisFel optimization-based deep ensemble classifier with graph embedding for imbalanced data classification

Imbalanced data classification (IDC) presents a significant challenge in data mining (DM), as it frequently occurs in various real-world areas with profound implications for highly skewed databases. IDC revolves around the task of learning from data characterized by a substantial imbalance in the number of samples across its different classes. Hence the Polar-CanisFel (PCF) Optimization-deep ensemble model is designed to address imbalanced big data issues, incorporating the SMOTE technique for rebalancing the dataset. This ensemble classifier leverages a deep convolutional neural network (DCNN), Long Short-Term Memory (LSTM), and Gated Recurrent Neural Network (GRNN) architectures for effective data classification. For the Heart Failure Prediction Dataset, the model reaches an accuracy of 96.35%, sensitivity of 94.54%, and specificity of 96.11%. Further, the accuracy of 95.91%, sensitivity of 95.87%, and specificity of 94.79% are obtained concerning the Stroke Prediction dataset. Finally, when applied to the Hepatitis-C prediction dataset, the model attains an accuracy of 92.79%, sensitivity of 92.90%, and specificity of 92.63% during 90% of training.

BIG DATA, ARTIFICIAL INTELLIGENCE, AND MANAGEMENT ACCOUNTANT: A GLOBAL PERSPECTIVE

This study aims to objectively explored the relevance of big data issues that have developed in the professional world to the best practices of the management accounting profession. The conceptual framework was developed to become the frame for consideration of making structured designs on artificial intelligence issues. Using data sources derived from literature studies and conducting various reviews of articles related to this interesting topic, conclusions are generated that refer to the implications of management accountant best practices. This study finds that the concept of management accountants is strongly influenced by the adoption of Big Data in the companies. Furthermore, we specifically define and present strategic steps that can suggest management accountants can carry out best practices in accordance with professional programs that have become an important part of practice. This study contributes to the development of the best practice of management accountants where Big Data is the center of attention that cannot be separated from their professional practice so that it is possible to adjust the practice of management accountants that generate added value. To the best authors knowledge, this is the first study to seeks and explores the Big Data and Artificial Intellegence in the management accountant profession from global perspectives. The study provides some deep insight to the accountant management global more take care for their sustainable profession in the long wave of digitalisation.

A Study of Cognitive Justice Issues in Big Data Mining Behaviour

随着大数据时代的降临，算法技术的正当性问题引发人们关注。区别于算法正义和数据正义，认知正义以认知主体为视角并具有法律意义，要求认知主体的认知水平与认知客体的可信度相匹配。实践中存在对大数据挖掘所获证据的证明力进行天然推定，证明标准难以对法官心证形成控制的现状。这是由于大数据挖掘基于相关关系的思维冲击了人们的认知惯性，将解释权交给了算法，降低了认知水平，算法黑箱的不透明性和难以检验性导致认知客体本不应当赋予过高的可信度，算法歧视导致难以对二者匹配的过程进行矫正。需要将内心确信符合信念标准、偏见性算法信息应仅发挥补强性作用、将大数据挖掘所获信息在辩护人阅卷中进行披露、破除相互印证的因果逻辑桎梏，从而回归于认知正义，走向法治而非“算法治”。 Abstract: With the emergence of the big data era, questions about the legitimacy of algorithmic technologies have arisen. Distinguished from algorithmic justice and data justice, cognitive justice takes the perspective of the cognitive subject and has legal significance, requiring the cognitive level of the cognitive subject to match the credibility of the cognitive object. In practice, there is a natural presumption of the probative power of evidence obtained by big data mining, and the standard of proof is difficult to control the judge’s mental evidence. This is due to the big data mining based on the correlation of thinking impacts the cognitive inertia of people, the interpretation of the right to the algorithm, reducing the level of cognition, the opacity of the black box of the algorithm and difficult to test leads to the cognitive object should not have been given too much credibility, the algorithmic discrimination leads to the difficulty of the process of correcting the matching of the two. There is a need to return to cognitive justice, towards the rule of law rather than the “rule of law”, by bringing inner certainty in line with the standard of conviction, biased algorithmic information should play only a reinforcing role, disclosure of the information obtained from big data mining in the reading of the defense case file and breaking the shackles of the causal logic of mutual corroboration.

Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

One of the factors hindering the scaled application of lithium-ion batteries is their thermal safety issues. Real-time monitoring of core temperature holds promise in alleviating this concern. However, calibrating individual models for thousands of battery cells poses challenges and leads to big data issues. The maturing and rapidly advancing artificial intelligence technologies hold promise in addressing the task of core temperature prediction. In this study, we conduct a benchmark test using typical recurrent neural networks for core temperature prediction. The test encompasses three main aspects: data acquisition, data processing, and data analysis. Firstly, we conduct dynamic battery tests across a wide temperature range on a custom-built experimental platform. Subsequently, we employ big data techniques for data pre-processing, including filtering, normalization, and sliding window methods. Next, hyperparameter optimization is then performed using the Bayesian optimization with Tree-structured Parzen Estimator, followed by K-fold cross validation. Consequently, we obtain three optimal performing models based on typical recurrent neural networks. Finally, we extensively compare these models in terms of performance and complexity. According to the results, all data-driven methods achieve satisfactory model performance, with RMSE within 0.17 °C and MAE within 0.15 °C. Among the models with similar predictive performance conditions, gated recurrent unit exhibits the lowest model complexity and faster training speed. Meanwhile, we also validate the portability and generalizability of the forecasting models. This study lays the foundation for future research in this area and can provide guidance for engineering practical applications to ensure the safe operation of battery energy storage systems.

Open Research Issues and Tools for Visualization and Big Data Analytics

Open Research Issues and Tools for Visualization and Big Data Analytics

A ferroelectric fin diode for robust non-volatile memory

Among today’s nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 1010 cycles, an ON/OFF ratio of ~102, a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 104 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing.

Three Philosophical Issues of Big Data

Three Philosophical Issues of Big Data

Research on the Legal Regulation Issues of Big Data’s “Killing Familiarity” Behavior in Electronic Commerce

Research on the Legal Regulation Issues of Big Data’s “Killing Familiarity” Behavior in Electronic Commerce

A Comparison of LSTM, GRU, and XGBoost for forecasting Morocco's yield curve

The field of time series forecasting has grown significantly over the past several years and is now highly active. In numerous application domains, deep neural networks are exact and powerful. They are among the most popular machine learning techniques for resolving big data issues because of these factors. Historically, there have been numerous methods for accurately predicting the subsequent change in time series data. The time series forecasting problem and its mathematical underpinnings are first articulated in this study. Following that, a description of the most popular deep learning architectures used to date with success in time series forecasting is provided, emphasizing both their benefits and drawbacks. Feedforward networks, recurrent neural networks (such as Elman networks), long- and short-term memory (LSTM), and gated recurrent units (GRU) are given special consideration. Furthermore, the advantages of the XGBoost boosting tree method have shown its superiority in numerous data mining competitions in recent years. The high coefficients of the metric measures indicate that the proposed XGBoost model provides good predictive performance, according to the results.

K-POP 아이돌 그룹의 세대별 이슈 변화 분석 고찰

This study aims to identify the major issues in the news big data related to generational idols formed by the growth and development of idols amid the current global spread of K-pop, and to suggest ways to strengthen the competitiveness of the Korean music industry by identifying the major issues that are being formed through the media for each generation of K-pop idols and how they have changed. For this purpose, news big data LDA technique was used as a topic modeling analysis method for 55,660 cases, and as a result of the topic modeling analysis, common issue types of six dimensions were derived. The main issue types are activity dimension, marketing dimension, global dimension, social contribution dimension, dispute dimension, and fandom dimension, and policy, industrial, and academic implications are proposed based on the results of this study.

Efficient integration of big data with blockchain: Challenges, opportunity and future

<p>Big data has become more and more popular, piquing the interest of both researchers and technologists as well as business executives. Despite its advantages, big data has a number of problems that necessitate a one-stop shop. Blockchain technology has seen a considerable increase in usage, which has had a substantial impact on its applications and led to a variety of useful outcomes. In the areas of identity, trust, decentralization, data-driven decisions, data ownerships, etc., there are notable game-changers. As a result, Blockchain is frequently acknowledged as an effective fix for big data issues. Among the solutions it suggests are decentralized private data management and digital property resolution. Together, two these technologies can develop beneficial solutions.<em></em></p>

Context-aware Big Data Quality Assessment: A Scoping Review

The term data quality refers to measuring the fitness of data regarding the intended usage. Poor data quality leads to inadequate, inconsistent, and erroneous decisions that could escalate the computational cost, cause a decline in profits, and cause customer churn. Thus, data quality is crucial for researchers and industry practitioners. Different factors drive the assessment of data quality. Data context is deemed one of the key factors due to the contextual diversity of real-world use cases of various entities such as people and organizations. Data used in a specific context (e.g., an organization policy) may need to be more efficacious for another context. Hence, implementing a data quality assessment solution in different contexts is challenging. Traditional technologies for data quality assessment reached the pinnacle of maturity. Existing solutions can solve most of the quality issues. The data context in these solutions is defined as validation rules applied within the ETL (extract, transform, load) process, i.e., the data warehousing process. In contrast to traditional data quality management, it is impossible to specify all the data semantics beforehand for big data. We need context-aware data quality rules to detect semantic errors in a massive amount of heterogeneous data generated at high speed. While many researchers tackle the quality issues of big data, they define the data context from a specific standpoint. Although data quality is a longstanding research issue in academia and industries, it remains an open issue, especially with the advent of big data, which has fostered the challenge of data quality assessment more than ever. This article provides a scoping review to study the existing context-aware data quality assessment solutions, starting with the existing big data quality solutions in general and then covering context-aware solutions. The strength and weaknesses of such solutions are outlined and discussed. The survey showed that none of the existing data quality assessment solutions could guarantee context awareness with the ability to handle big data. Notably, each solution dealt only with a partial view of the context. We compared the existing quality models and solutions to reach a comprehensive view covering the aspects of context awareness when assessing data quality. This led us to a set of recommendations framed in a methodological framework shaping the design and implementation of any context-aware data quality service for big data. Open challenges are then identified and discussed.

A Novel Clustering and Matrix Based Computation for Big Data Dimensionality Reduction and Classification

For higher dimensional or "Big Data (BD)" clustering and classification, the dimensions of documents have to be considered. The overhead of classifying methods might also be reduced by resolving the volumetric issue of documents. However, the dimensions of the shortened collection of documents might potentially generate noise and abnormalities. Previous noise and abnormality information removal strategies include several different approaches that have already been established throughout time. To increase classification accuracy, current classifications or new classification methods that has created to conduct classification, must deal with some of the most difficult issues in BD document categorization and clustering. Hence, the goals of this research are derived from the issues that can be solved only by expanding classification accuracy of classifiers. Superior clusters may also be achieved by using effective "Dimensionality Reduction (DR)". As the first step in this research, we introduce a unique DR approach that preserves word frequency in the document collection, allowing the classification algorithm to obtain improved (or) at least equal classification levels of accuracy with a lower dimensionality set of documents. When clustering "Word Patterns (WPs)" during "WP Clustering (WPC)", we imply a new WP "Similarity Function (SF)" for "Similarity Computation (SC)" to be used as part of WPC. DR of the document collection is accomplished with the use of information gained from various WP clusters. Finally, we provide "Similarity Measures" for SC of high dimensional texts and deliver SF for document classification and deliver SF for document classification. With assessment criteria like "Information-Ratio for Dimension-Reduction", "Accuracy", and "Recall", we discovered that the proposed method WP paired with SC (WP-SC) scaled extremely effectively to higher dimensional "Dataset’s (DS)" and surpasses the current technique AFO-MKSVM. According to the findings, the WP-SC approach produced more favorable outcomes than the LDA-SVM and AFO-MKSVM approaches.

Adaptive Energy Management of Big Data Analytics in Smart Grids

The smart grid (SG) ensures the flow of electricity and data between suppliers and consumers. The reliability and security of data also play an important role in the overall management. This can be achieved with the help of adaptive energy management (AEM). This research aims to highlight the big data issues and challenges faced by AEM employed in SG networks. In this paper, we will discuss the most commonly used data processing methods and will give a detailed comparison between the outputs of some of these methods. We consider a dataset of 50,000 instances from consumer smart meters and 10,000 attributes from previous fault data and 12 attributes. The comparison will tell us about the reliability, stability, and accuracy of the system by comparing the output of the various graphical plots of these methods. The accuracy percentage of the linear regression method is 98%; for the logistic regression method, it is 96%; and for K-Nearest Neighbors, it is 92%. The results show that the linear regression method applied gives the highest accuracy compared to logistic regression and K-Nearest Neighbors methods for prediction analysis of big data in SGs. This will ensure their use in future research in this field.

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

A Review Of Blockchain Technology In Big Data Paradigm

Various organizations have plunged towards cloud computing solutions because of high cost, storage space and maintenance issues from big data. Big data is utilized by institutions in decision making process to achieve valuable insights in real time. Nevertheless big data has indeed helped organizations, andeven small to medium-sized businesses, but it characteristics has posed great challenges at same time. The cloud platform has facilitated in handling computational, networking and storage capacity related issues but still these mechanisms are inadequate to deal with the rapid data explosion. Moreover data is not secure in Cloud Environment as the servers are governed by third party. In recent years fusion of big data and blockchain technology has pushed big data beyond imagination and imparted great prospective in better-quality big data services and applications. Organizations certainly will adopt these two emerging technologies to transform their business needs. In this paper we present an overview of security related issue of big sensitive data on cloud platform along with motivation behind paradigm shift i.e. Integration of big data and blockchain technology.

The Interplay Between the Essential Facility Doctrine and the Digital Markets Act: Implications to Big Data

Today many companies are collecting and extracting data from different sources to help them with their strategic decision-making. Big data is the basis of data-driven economy, bringing significant competitive advantage and market power to companies who are able to harness and exploit its potential. Digital transformation of markets and economy challenges the existing structures of consumer protection, data protection and competition law. Data is a commodity as well as a strategic asset. The term Big data refers to the amount of data that cannot be processed in a short time by traditional informatics devices. Undertakings possessing a large scale of different data have a competitive advantage. Possible application of the essential facility doctrine to Big data issues has not attracted much attention in competition assessment. This paper will try to fill the gap by providing some insights into competition and data issues. Also, the question whether data can be considered under the essential facility doctrine will be analysed. Furthermore, it will be shown that essential facility criteria are applicable, although there is room for some adjustments to data markets. The last part of the paper will scrutinize the Digital Markets Act that tries to shed some light and clear some possible problematic behaviour of the so-called gatekeepers. The regulation leaves the conventional approach and shortens the process of tackling possible anti-competition concerns. It regulates only those undertakings that have significant impact on market and the possibility to become an important gateway in the future. When the status of a gatekeeper is established in accordance with all prescribed criteria, there will be no need to show that the elements of the essential facility doctrine are fulfilled. The essential facility doctrine will still be relevant to undertakings that are not designated as gatekeepers.

Review of Literature on Improving the KNN Algorithm

K-Nearest Neighbors (KNN) is a classification algorithm that has been widely used in the world of machine learning. The KNN algorithm classifies objects with learning data that are closest to the object. The special case where the classification is predicted based on the closest learning data is called the Nearest Neighbors algorithm. Classification is an important issue in processing big data, data science, and machine learning. KNN is one of the oldest, simplest, and also accurate algorithms for pattern classification and regression models. Many researchers have also improvised KNNs and the results obtained by these studies have changed a lot in terms of the accuracy of the results. The purpose of this paper is to see the improvement of several types of development of the KNN algorithm. &nbsp

Millennials looking for their place in epidemiology.

The "millennial" epidemiologists, born between the beginning of the 80s and the end of the 90s, are the generation that most of all, today, lives between the present and the future of this discipline. This issue of Recenti Progressi in Medicina aims to talk about what young (and no longer young) epidemiologists and public health researchers are dealing with and to reflect on the most relevant topics in our field, with an eye to the future. Starting from the profile of the "millennial" epidemiologists in Italy and the topics on which they work, the issue develops through three parts dealing with relevant topics for the present and the future of Public health. The first part deals with the important issue of finding a balance between the protection of personal data and the protection of health through a dialogue between researchers, jurists and citizens. The second part aims to clarify the issue of big data and its implications for producing health. The third part touches on four relevant topics for the perspectives of epidemiology through reflections and application examples of machine learning, integration between pharmacoepidemiology and environmental epidemiology, health prevention and promotion involving citizens and other stakeholders, and epidemiology of mental health. In a constantly changing world, challenges for those who work to produce health are not lacking, as is the determination to face them. With this issue, we hope to contribute to the awareness of who we are and our potential, to help millennials (but not only) find their place in epidemiology, today and tomorrow.