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Abstract Background Our current understanding of the pathophysiology and management of sepsis is associated with a lack of progress in clinical trials, which partly reflects insufficient appreciation of the heterogeneity of this syndrome. Consequently, more patient-specific approaches to treatment should be explored. Aims To summarize the current evidence on precision medicine in sepsis, with an emphasis on translation from theory to clinical practice. A secondary objective is to develop a framework enclosing recommendations on management and priorities for further research. Sources A global search strategy was performed in the MEDLINE database through the PubMed search engine (last search December 2017). No restrictions of study design, time, or language were imposed. Content The focus of this Position Paper is on the interplay between therapies, pathogens, and the host. Regarding the pathogen, microbiologic diagnostic approaches (such as blood cultures (BCs) and rapid diagnostic tests (RDTs)) are discussed, as well as targeted antibiotic treatment. Other topics include the disruption of host immune system and the use of biomarkers in sepsis management, patient stratification, and future clinical trial design. Lastly, personalized antibiotic treatment and stewardship are addressed (Fig. 1). Implications A road map provides recommendations and future perspectives. RDTs and identifying drug-response phenotypes are clear challenges. The next step will be the implementation of precision medicine to sepsis management, based on theranostic methodology. This highly individualized approach will be essential for the design of novel clinical trials and improvement of care pathways. [ABSTRACT FROM AUTHOR]

At present, universities are facing a critical period of transition from digital campus to smart campus. Because cloud service data has the characteristics of diversity, in addition to structured data, there are also unstructured data, which play an important role in the study and life of the majority of students. Therefore, it is very important to improve the construction level of smart campus cloud service platform. At this stage, there are great differences between the service platform under the traditional model and the smart campus platform under the big data computer system. Under the traditional mode, the construction method of smart campus cloud service platform in the city has some shortcomings. Therefore, it is urgent to build a smart campus cloud service platform. Based on this, this paper compares the smart campus cloud service platform of the traditional service platform and the big data computer system. The results show that the latter has a timely response speed and can bring high-quality and efficient data services to users. At the same time, it also analyzes the ideas and goals of building an intelligent cloud service platform, and puts forward corresponding construction methods. [ABSTRACT FROM AUTHOR]

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI +3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI +3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology with Big data, Artificial Intelligence, and Machine Learning. It disrupted industries such as health, education, manufacturing, and banking. However, the increasing popularity of Blockchain ex- poses the scalability issues of major public blockchain platforms (e.g., Bitcoin and Ethereum) and dramatically affects its development. The scalability problem manifests in terms of Low throughput, high transaction latency, and massive energy consumption. Several reviews and studies cover these factors and their potential solutions, yet these studies need to highlight more information through actual application to natural systems or projects. This study investigates all relevant papers on current research solutions for public blockchain scalability issues. The scope of this paper is to explore the implementation of different state-of-the-art scalability solutions to natural systems and projects while simultaneously highlighting the results. This study discusses the methods and techniques used and the challenges encountered that have yet to future researchers must explore. [ABSTRACT FROM AUTHOR]

As technology trends, society must adapt to the changing environment over time. Many companies have been utilizing Big Data to improve decision-making capabilities and develop strategies for launching new products and services, as they understand the customers' needs while exploring new markets. Trends in the automation of tasks are inviting more users to further their interest in the noticeable technology development. This paper studies the impacts of Big Data. The paper analyzes the potential benefits, risks, and challenges companies may face in adopting Big Data. With the advancement of technology, audit technology has also improved the utilization of Big Data. [ABSTRACT FROM AUTHOR]

Diabetes mellitus remains a global health challenge, requiring innovative solutions for effective disease management. This paper offers a thorough analysis of diabetes technologies, highlighting their various roles in diabetes care. Through a thorough review of the literature and analysis of emerging trends, we explore the multifaceted impact of technology on diabetes care. We investigate the key role of continuous glucose monitoring systems, insulin pumps and smart insulin pens in achieving optimal glycaemic control. The paper also evaluates the integration of artificial intelligence and machine learning algorithms in predictive modelling for early detection of glucose fluctuations, ultimately preventing diabetes-related complications. Additionally, studies the potential of telemedicine and mobile applications in enhancing patient engagement and self-management. Moreover, the review covers advancements in closed-loop insulin delivery systems, offering insights into their clinical effectiveness and potential to revolutionize diabetes care. Ethical and privacy considerations related to the use of patient data in these technologies are discussed, emphasizing the importance of striking a balance between technological innovation and patient security. This paper's evidence synthesis underscores the increasing influence of diabetes technologies on patient outcomes, quality of life, and healthcare systems. It underscores the need for multidisciplinary collaboration between healthcare professionals, researchers and technology developers to ensure the seamless integration and accessibility of these tools to patients living with diabetes. This study serves as a valuable resource for clinicians, researchers, and policymakers, providing a comprehensive view of evolving diabetes technologies and their potential in the field. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. The online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. Through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. [ABSTRACT FROM AUTHOR]

Based on the rapid development of Internet new generation information technology, campus culture has become an important force to promote educational innovation. However, the uneven quality of campus culture teaching is also increasingly exposed. Therefore, we propose a set of campus culture intelligent quality assessment feedback system driven by data and knowledge. Based on real multi-modal campus culture data, the evaluation system covers different online teaching forms, and can comprehensively consider and measure the core elements of quality evaluation from the perspective of teachers and students, so as to achieve accurate evaluation and feedback of campus culture. This paper proposes to combine emotion computing with big data analysis, construct a theoretical framework of multi-modal emotion analysis from the perspective of multi-spatial fusion, and explore the students' learning emotion recognition and intelligent assessment based on multi-modal data fusion. By studying the key issues, logical path and implementation path of multimodal learning emotion analysis in campus culture, this paper reveals the mechanism of student emotion development from the perspective of campus culture, expands the intervention practice of student group emotion, and provides technical support and action guidance for after-school teaching and research on campus culture. [ABSTRACT FROM AUTHOR]

As Global Navigation Satellite Systems (GNSS) infrastructure expands and more satellites are launched, the number of signals and measurements available to users increases, resulting in a corresponding rise in data generation rates. GNSS receivers with the ability to process triple frequency measurements and multi-constellation reception, have been complemented with the massive deployment of connected, GNSS-enabled devices such as smartphones, wearables, and IoT sensors, triggering the growth of a highly dispersed cloud of GNSS data. This trend observed in other domains presents both challenges and opportunities for applications of GNSS technology. The scientific exploitation of GNSS data involves developing new concepts through the analysis of the aforementioned sources of information. Traditionally, the extraction of insights from GNSS data has relied on searching and downloading datasets from multiple repositories. These datasets provided by numerous organisations are scattered worldwide, containing gems of information difficult to track and discover. This approach hinders research productivity, as scientists are forced to shift their focus from data exploration, analysis, modelling, visualization, and realisation of conclusions to a long list of repetitive and manual tasks of little added value. The GNSS Science Support Centre (GSSC) hosts ESA's innovative Digital Platform for GNSS, GSSC Now, which integrates a large GNSS repository with cutting-edge features for data exploration and analysis. The paper presents key functionalities of GSSC Now and highlights its value through case studies of real-world GNSS applications. This platform expands established computational limits, placing the data source side-by-side with cloud computing capabilities. The change of paradigm provides scientists with seamless execution of Machine Learning models, interactive algorithms and visualisation tools on a vast repository of GNSS data, removing barriers that hinder research productivity. [ABSTRACT FROM AUTHOR]

Many studies provide ways to monitor and manage the aquaponics system, a closed-loop ecosystem that combines hydroponics—growing plants without soil—and aquaculture—raising fish—using cloud computing, the Internet of Things (IoT), and smart sensing technologies. A system operating at peak efficiency yields organic, healthful food with little water and no chemical fertilizer usage, therefore contributing to food security and sustainability. In order to develop our proposed aquaponic system based on Internet of Things technologies, this paper will centralize our endeavor to integrate big data tools, namely Apache Spark and Kafka, into the previous architecture for data collection and processing, as well as for the prediction of any anomalies in the system. [ABSTRACT FROM AUTHOR]

The operation and maintenance expenses of power transmission and transformation projects, as a significant power supply carrier of the nation, continue to rise as a result of the sustained and quick expansion of China's social economy and the quick growth of the country's power demand. Power grid businesses are under a lot of market pressure. To increase the level of lean management of the operation and maintenance costs of power transmission and transformation projects, power grid enterprises must significantly enhance their capacity to estimate the operation and maintenance costs of their organizations in advance. Machine learning algorithms are gradually applied to the operation and maintenance cost prediction of power transmission and transformation projects of power grid enterprises as a result of the ongoing development of big data technology, effectively increasing the accuracy of operation and maintenance cost prediction. In this paper, by analyzing the variables affecting the differential operation and maintenance cost of power transmission and transformation projects, a scientific and reasonable investment analysis model for the differential operation and maintenance cost of power transmission and transformation projects is constructed using the stochastic forest algorithm of particle swarm optimization, and the variables affecting the differential operation and maintenance cost of substations and transmission lines are obtained, which proves that the trend of the prediction model in this paper is more consistent with the actual situation. [ABSTRACT FROM AUTHOR]

In today's era, science and technology have undergone tremendous development, and the research on big data technology has become more and more mature. Many areas of human life have begun to apply big data technology, and big data technology has indeed brought more convenience to human life. With innovation, human life has also become better because of the development of big data. With the increase of people's awareness of environmental protection and sustainable development, more and more scholars have begun to pay attention to the research of multi-channel closed-loop supply chain. The return products caused by reverse logistics greatly affect the supply chain inventory system and the butterfly effect of the supply chain. This paper uses the Agent technology in big data to construct a multi-channel closed-loop supply chain decision model, and applies the Agent technology to the research of multi-channel closed-loop supply chain inventory and butterfly effect. This paper analyzes the functional agents included in each structural agent in the closed-loop supply chain, designs the simulation process, and designs the final data collection and analysis. Through the analysis of the simulation results, find out the influence of the reverse logistics recycling and return links and the changes in inventory control parameters on the butterfly effect of the members of the closed-loop supply chain at all levels, and provide guidance for the members of the closed-loop supply chain to formulate reasonable and effective inventory control strategies Sexual opinions. [ABSTRACT FROM AUTHOR]

Big data has become the mainstream of information dissemination today. Big data plays an important role in the field of intelligent network monitoring. How to improve the hidden danger identification of intelligent network monitoring system and improve its accuracy is a hot issue to be solved. Therefore, in the context of big data, this paper uses intelligent analysis algorithm to identify and analyze the hidden dangers of intelligent network monitoring system. This paper mainly applies the methods of experimental setup, fuzzy analysis and data comparison to carry out relevant research on intelligent analysis of hidden danger identification of intelligent network monitoring system. The experimental data shows that the accuracy of the data obtained by the proposed method in the hidden danger identification is more than 50%, but less than 80%. Therefore, the application of background difference method needs further improvement. [ABSTRACT FROM AUTHOR]

The use of Big Data (BD) is considered to have the potential to provide significant benefits for hospitals because it can improve hospital services and can save treatment costs. This study aims to identify the effects of using BD Analytics (BDA) in improving hospital performance, including the key factors to determine the effectiveness of BD in improving the effectiveness of hospital performance, the data sources needed, the utilization of BD in improving patient satisfaction, and the challenges that can hinder the use of BD as the factors to determine the capabilities of BD. Implementing the PRISMA concept to conduct a Systematic Literature Review (SLR) of relevant literature by identifying the selection of relevant data sources, determining inclusion and exclusion indicators, extracting data from selected papers, evaluating the quality of the papers, and analyzing data using the meta-analysis method. Through this process, the authors examined 111 papers from various database sources and found 24 papers for further research. The results of this study indicate that from 24 papers, the data source to conduct data analysis is the main key factor to determine the capabilities of BD for hospitals which affect the further analysis of BDA. It was also found that several challenges hindering the use of BD need to be overcome to maximize the use of BD. However, there were limitations in the data sources used in the study. Therefore, it is necessary to review policies and infrastructure and utilize more varied data sources to maximize the use of BD and decision support systems in hospitals. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate the use of Rough Set Theory to identify information that can be used in project management, namely the knowledge about the appropriate prioritization of activities and the use of resources directed towards the adopted project objectives. Rough Set Theory operates on large data sets and permits the reduction of irrelevant data and the induction of decision rules that discover recursion and relationships in the data. The discovered rules can prove valuable for decision-making project managers. All the rules found in course of the proposed rule-finding process have been considered interesting and are discussed in the paper. The paper also provides an example of the application of Rough Set Theory in the field mentioned above. [ABSTRACT FROM AUTHOR]

Small data and big data are distinct approaches to data analysis and utilization in various applications. While big data has been the focus of many research and business efforts for more than ten years, small data is increasingly being recognized as having potential value in certain settings. We systematically review literature and conclude that small data can be indeed valuable in certain scenarios. This paper incorporates the data value perspective of small data within various application areas. For this, we apply the data-information-knowledge-wisdom (DIKW) hierarchy to categorize papers and findings, and discuss the papers from the view point of "data value". Our review identifies various contexts where small data can be used to create value, such as data pre-processing, classification tasks, anomaly detection, forecasting and decision support. We also highlight industries that may be particularly promising areas for practitioners and researchers focused on small data. In addition, we provide an overview of methods and tools for small data analysis, including statistical techniques, visualization, and machine learning algorithms. Finally, based on our results, we suggest, that further research should focus on small data analysis. [ABSTRACT FROM AUTHOR]

The development of information technology proves the possibility of acquiring large samples of policy text information and promotes innovative research based on the policy informatics approach of fusing data from multiple sources. This paper constructs a new public policy model based on existing data on economic system reform, and uses multi-types of textual big data such as government website, official weblog, and social media as the object of collection, to conduct a big data measurement on policy concerns related to the theory and practice of economic system reform since the reform and opening up. And this paper constructs a fine-grained measurement and portray the evolutionary trend of policy concerns and policy implementation. Based on the database, this paper helps to analyse the economic development trend since the reform and opening up, evaluate and monitor the implementation effect of different incentive policies and establish an iterative feedback mechanism, and provides a reference for relevant decision-making departments. [ABSTRACT FROM AUTHOR]

Big data analytics uncovers hidden patterns through classification, prediction and reinforcement of big datasets. In these datasets, some features have a negligible connection with other features and some may be insignificant as their presence does not impact the results of big data analytics. The algorithms of big data analytics generate better classification models when supplied with a dataset consisting of relevant, important and informative features. These features can be classified as important and unimportant. For the selection of important features, different filtrations techniques are used. These techniques filter features on different basis like information gain, information dispersion, Gini index, etc. and have a few drawbacks reviewed in this paper. The first contribution of this paper is to propose a new feature selection technique named "Relevance-diversity algorithm" for selecting important features based on two measures i.e. relevance and diversity for optimizing features as low as possible and reducing the search time used in feature selection. The second contribution of the paper is that it proposes a new supervised classification algorithm based on Naive Bayes classification. The assumption of naive i.e. feature independence is discarded from the algorithm of Naive Bayes classification. The features are considered to be dependent on each other and their combined impact on the class value is evaluated. The newly proposed classification algorithm is then applied to the features selected through the relevance-diversity based feature selection technique. The datasets of Weather, Tic-Tac-Toe, Lenses, Balance-scale and CarEval are used for the evaluation of both the techniques. The results of the proposed feature selection method are compared with the existing methods and the results of Modified-Bayes are compared with the existing Naive Bayes algorithm. Analysis revealed that the proposed method performed better in terms of the number of features, accuracy and time complexity. [ABSTRACT FROM AUTHOR]

Unintentional injuries are common among children below the age of 14 years. Although the concern about the need for injury prevention is increasing among caregivers, preventable injuries are still one of the leading causes of death among children aged 0-14 years. Hence, there is a need for an effective system that can empower caregivers, parents and nursery teachers in making better and faster decisions to protect the lives of children through situational design of safe environments. In this paper, as a new situational design tool, we propose a situation-aware system for unintentional injury prevention with the integration of image processing using an RGB-D camera and spatial knowledge graphs. This paper describes the identification of dangerous situations based on the results obtained from an R-Map analysis of a large amount of data on injury situations. This paper also describes an implementation of the system and the advantages that it can offer in understanding possible dangerous situations in indoor environments like classrooms or homes and providing alternative suggestions on what kind of preventive measures can be taken to minimize such injuries. The feasibility of the proposed system was proved by applying it into nursery injury situational data. [ABSTRACT FROM AUTHOR]

High-performance sparse matrix multipliers are essential for deep learning applications, and as big data analytics continues to evolve, specialized accelerators are also needed to efficiently handle sparse matrix operations. This paper proposes a modified, configurable, outer product based architecture for sparse matrix multiplication, and explores design space of the proposed architecture. The performance of various architecture configurations has been examined for input samples with similar characteristics. The proposed architecture has been implemented on Xilinx Kintex-7 FPGA using 32-bit single precision floating-point arithmetic and also in 8-bit, 16-bit and 32-bit fixed-point arithmetic formats. The effect of quantization in the proposed architecture has been analyzed extensively and the results have been reported. The performance of the proposed architecture has been compared with state-of-the-art implementations, and an improvement of 9.21% has been observed in the performance. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis. [ABSTRACT FROM AUTHOR]

One of the best forms of entertainment today is online multimedia with movies and various other entertainment sources at the tip of our fingers. However, finding a movie from the numerous options available in a movie database to suit a user's taste is a non-trivial task. Most recommendation systems do not consider the user's limitations, such as whether the user has the means to watch the movie suggested by the system. This paper proposes a system to provide recommendations that are platform specific. Two sources of movies have been considered, viz., Netflix and Amazon Prime, and other data sets can be included as well. In addition to providing recommendations, the proposed system helps filter movies based on user-specified criteria. It applies big data analytics for faster execution of both movie recommendation and data filtering and can work with large and distributed movie databases. [ABSTRACT FROM AUTHOR]

Big Data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of 'garbage in, garbage out'. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big Data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big Data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories. [ABSTRACT FROM AUTHOR]

Big data has become omnipresent since most human activities are recorded using various technologies, thus generating huge databases on various platforms such as social media. Human activity has become reflected in these databases, making them suitable for various research that used to be conducted based on more traditional research approaches, such as survey research. Labour markets research is also affected by big data since social media has become one of the most dominant means of communication in various areas, being employed as one of them. This paper aims to map the research field of big data and labor markets using a systematic literature review and bibliography. Web of Science database has been searched using the keywords "big data" and "labo(u)r market". The most relevant papers for the area of big data for labour analysis have been selected, using transparent and consistent criteria. Papers have been analysed using a bibliometric approach supported by the VOSviewer and R bibliometrix, which has been used for detecting various research trends, such as research topics. The assessment of the current research in big data for the labour market has been compiled to detect the research gaps and generate future research directions. [ABSTRACT FROM AUTHOR]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a parallel distributed version of Viterbi algorithm that combines the advantages of Spark, the big data framework, and hidden Markov models to solve the decoding problem for large scale multidimensional data. The scope of the paper includes a review of hidden Markov models, a study of decoding problem, a presentation of related work, and a discussion of previously proposed implementations. The main part of the paper consists of a description of development and implementation of a parallel distributed Viterbi algorithm in a cloud computing environment, followed by a description of evaluation experiments of the presented algorithm. The results showed that the proposed algorithm is faster, with high scalability and no deterioration in forecast accuracy is observed. [ABSTRACT FROM AUTHOR]

The financial world, money markets, and the growth of an economy are all powered by banks. In India's financial sector, where cashless and paperless transactions are expanding quickly, bank fraud is also rising quickly, and fraudsters are now turning to cutting-edge techniques. Fighting financial crimes has always been a priority for the banking industry. Banks' fraud losses will diminish when they are able use analytics and emerging technologies for better fraud prevention. Data has evolved into more than simply an IT resource; it is a vital feature of the banking industry's digital transformation in the present world. The majority of contemporary risk monitoring systems, which are based on data science discipline such as big data, allow for surveillance and offer Early Warning Signals (EWS) about any kind of worsening. The study of this paper signifies that how big data as a service and application proven as a strong and innovative instrument that assists Indian banking sector not only in identifying security issues and fraudulent behavior, but also in their early prevention. The experimental results of bibliometric study conducted on Scopus and Web of Science databases using in Vos Viewer software, clearly discovered big data technology is a crucial approach to deploy in Indian banking system. Findings of this paper will contribute to knowledge and practice of researchers working in this filed by increasing their understanding related to numerous big data technologies and techniques deployment for handling banking financial risks and its role in operational banking workflow management. [ABSTRACT FROM AUTHOR]

Authentication and Authorization are the base of security for all the Technologies present in this world today. Starting from your smartphone where a user authenticates himself before he could access the data inside to Entering into the White House, you must authenticate yourself, and based on that you are authorized. In this digital world where every Business, MNC, Government Body, Companies, Users, etc. needs a website to inform the world about their presence on the internet, provide services online and become a "Brand", the risk of leaking user's sensitive information increases. It could be dangerous to the users of the hacked website because their sensitive information like a credit card, bank account details, etc. could be sold in the black market of the "dark web". The role of the dark web is described in the paper and how the data is sold there and what becomes of it. The paper helps to understand how a secure website is developed that promises the user to keep the sensitive information safe, increases the bond of trust between a client and server which results in a long-term relationship. The aim behind developing an authentication system is to keep users' sensitive information safe so that hackers cannot steal and sell the information on the dark web's back market. To perform this, the developer needs to understand how to implement authentication. NodeJS, with the help of its framework expressJS and some other packages, is used to develop the authentication and authorization system of the website by the research. Previous papers on this field covered the authentication topic in general. This paper overcame that by going deeper into the field and being server-side language specific. The common types of authentication methods used in different types of websites are discussed in detail and the best methods are purposed for the developer to be implemented for a more secure website. This research put light on Artificial Intelligence and blockchain as the future of security of big data. [ABSTRACT FROM AUTHOR]

The Swarm Intelligence (SI) algorithms have been useful in solving multifaceted optimization problems. SI Algorithms as the name suggests work on the simulation principle of behaviour of biological swarms. Today, there is lot of advancement in the field of Data Science; which comprises of large amount of Big Data that needs to managed as well as analysed. Traditional methods may only be applied to differentiable and continuous functions. For population-based approaches, Swarm Intelligence proves to be effective and efficient. In order to have better insight into applications of SI Algorithms in Data Science, and to accumulate a further reference for the future researchers in this field, this paper discusses scope of Swarm Intelligence in Data Science. On the basis of existing work; the following paper also focusses on the Challenges and Opportunities of SI in Data Science and also encourage to design more efficient algorithms in future that is capable to solve Data Science related problems in real world. [ABSTRACT FROM AUTHOR]

Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems. [ABSTRACT FROM AUTHOR]

• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: "Proper function"; "Alert state"; and "Equipment failure". The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment. • Method classifies equipment status without prior info and predicts anomalies using ML. • Using DNN, time series data management was added to better diagnose equipment condition. • Generic method requires no technical knowledge, works online for real-time predictive maintenance. • The algorithm is generic and applicable to various equipment with different sensors. [ABSTRACT FROM AUTHOR]

[Display omitted] • A research framework of "potential supply/demand–pressure/accessibility–actual supply/demand" for urban park green space recreational services was proposed. • The framework quantifies the supply, demand, and flow of services in a theoretically and quantitatively consistent manner. • The framework makes the evaluation results of recreational services in different regions and the evaluation results of the same research area at different periods comparable.The number of characters in Highlights cannot be reduced any further, otherwise the content of the paper cannot be accurately expressed. • The number of characters in Highlights cannot be reduced any further, otherwise the content of the paper cannot be accurately expressed. Regular recreational activities in urban park green spaces are beneficial to the physical and mental health of residents. The supply capacity of, the residents' demand for, and the actual flow of recreational services must be distinguished and evaluated to promote the fair and reasonable layout of urban park green spaces. However, the unit dimensions of the supply, demand, and flow indexes of recreational services are often inconsistent or only in simple semiquantitative forms, which cannot be directly compared and analyzed. Thus, this study constructed a research framework of "potential supply/demand–pressure/accessibility–actual supply/demand" for recreational services, and the framework was tested using the central urban area of Xuchang as the experimental area. Results show the following: (1) A spatial mismatch exists in the supply–demand relationship of recreational services in the study area; this relationship is reflected in the semicircular layer structure from the central and southern areas to the periphery, changing from shortage of supply to oversupply. (2) The actual supply of recreational services is highly correlated with the number of people served, which can explain 50.38% of the change in the actual supply. (3) The potential demand for recreational services is highly correlated with the actual demand. It can explain 78.96% of the change in the actual demand. (4) Accessibility is a non-critical factor affecting the travel rate of residential quarters, while supply pressure is a key factor affecting the utilization rate of park green space. The framework can effectively identify important or low-utilization service-providing areas, unmet service-demanding areas, and important service-connecting areas. It is suitable for the study of the relationship between the supply, demand, and flow of user movement-related services. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM. • Explored how additive manufacturing evolution yields big data, leveraging ML and DT to optimize manufacturing. • Discussed the interconnection among big data, machine learning, and digital twins. • Examined ML's role in material analysis, design optimization, process optimization, defect detection, and sustainability. • Reviewed current developments for the application of digital twins to empower additive manufacturing. [ABSTRACT FROM AUTHOR]

• Treat a problem of the imbalanced customer churn classification. • A new multi-strategy collaborative processing method IADASYN-FLCatBoost is proposed. • The traditional ADASYN algorithm is improved and an IADASYN algorithm is proposed. • The Focal Loss function is embedded into the CatBoost to form a new FLCatBoost. • Empirical analysis shows the proposed method performs better than similar methods. The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model's capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall , F 1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries. [ABSTRACT FROM AUTHOR]

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme. [ABSTRACT FROM AUTHOR]

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation. [ABSTRACT FROM AUTHOR]

Emotion recognition based on electroencephalography (EEG) signal features is now one of the booming big data research areas. As the number of commercial EEG devices in the current market increases, there is a need to understand current trends and provide researchers and young practitioners with insights into future investigations of emotion recognition systems. This paper aims to evaluate popular consumer-grade EEG devices' status and review relevant studies that examined the reliability of these low-cost devices for emotion recognition over the last five years. Additionally, a comparison with research-grade devices is conducted. This paper also highlights EEG-based emotion recognition research's key areas, including different feature extraction capabilities, characteristics, and machine learning algorithms. Finally, the main challenges for building an EEG-based emotion recognition system, focusing on the data collection process with commercial EEG devices and machine learning algorithms' performance, are presented. [ABSTRACT FROM AUTHOR]

The traditional rural tourism industry has some problems, such as incomplete tourism information, unable to share all data, serious data loss, and the decline of tourism service quality. In recent years, with the support of big data and e-commerce technology, many villages have vigorously developed tourism e-commerce, which has brought huge economic benefits to rural residents and improved their living environment to a certain extent. This paper briefly expounds the concept and characteristics of big data, analyzes the needs of rural tourism e-commerce on the basis of being familiar with big data related technologies, develops a rural tourism e-commerce system (hereinafter referred to as RTES), and explains the design of the system in detail from the system architecture design, database design and system function structure design. The application of the system shows that the tourism e-commerce designed and developed in this paper is guaranteed in practicality and security, and reflects professionalism and comprehensiveness in service. It can not only recommend and book the best tourist routes for customers, but also provide support for managers and facilitate management. [ABSTRACT FROM AUTHOR]

An important increase in the number of smart things, connected objects, smartphones, and sensors are founded every day, creating a huge amount of data called Big Data. Because of the dynamism of things and the limited capability of their resources and services to process and store this huge data, Cloud computing became an efficient, accessible, and reasonable solution. IoT in combination with Cloud functionalities provides a new phenomenon called the Cloud of Things (CoT) where many new possibilities are enabled. With the heterogeneity of different objects, the dynamism of their context, their distribution and the infinite cloud services, a middleware is a key solution. The main aim of this paper is to study middleware for self-adaptation of the Cloud of things environment into the context of objects. Toward this end, we first present some related features. Next, we compare some middlewares according to their characteristics. Then, we present some middlewares for static and dynamic adaptation and finally, we conclude this paper with a list of current challenges and issues in the design of a new middleware able to adapt dynamically and in execution time any cloud service into the context of the current object. [ABSTRACT FROM AUTHOR]

Hadoop is an open-source framework that is widely used to store and process large amounts of data. Its core component is called YARN that is responsible for resource management and job scheduling for Big Data applications. Although YARN has very advanced processing performance. It suffers from resource allocation problems when used in a heterogeneous cluster serving multiple users. To deal with this heterogeneity and overcome the degradation of cluster, load balancing has become a major challenge for Hadoop run-time management. In this paper, an efficient resource allocation strategy combining multi-criteria decision-making and accurate clustering of nodes in a heterogeneous Hadoop environment is proposed. A multi-criteria decision approach relying on a modified analytical hierarchy process method is applied to assign a score to jobs depending on the resources requirements at run time. First, the proposed system profiles the available nodes by grouping them into clusters with similar performance. Second, the system performs a dynamic multi-criteria selection on resource requests by assigning scores to jobs according to their resource usage. Third, node groups and job scores are used to dynamically allocate jobs to the most appropriate resources in real time while keeping load balance in the heterogeneous cluster. The aims of this present paper are to optimize the resource utilization and achieve a high performance of the Yarn resource management by considering a heterogeneous environment. The experimental results demonstrate that the proposed algorithm provides the best utilization of available resources by 45% and 12% compared to fair and H-fair respectively, which implies a minimum job execution time in a heterogeneous cluster. [ABSTRACT FROM AUTHOR]

With the rapid growth of data sources, Big data security in Cloud is a big challenge. Different issues have ascended in the area of Big data security such as infrastructure security, data privacy, data management and data integrity. Currently, Big data processing, analytics and storage is secured using cryptography algorithms, which are not appropriate for Big data protection over Cloud. In this paper, we present a solution for addressing the main issues in Big data security over Cloud. We propose a novel system architecture called the Secure Authentication and Data Sharing in Cloud (SADS-Cloud). There are three processes involved in this paper including (i). Big Data Outsourcing, (ii). Big Data Sharing and (iii). Big Data Management. In Big data outsourcing, the data owners are registered to a Trust Center using SHA-3 hashing algorithm. The MapReduce model is used to split the input file into fixed-size of blocks of data and SALSA20 encryption algorithm is applied over each block. In Big data sharing, data users participate in a secure file retrieval. For this purpose, user's credentials (ID, password, secure ID, and current timestamp, email id) are hashed and compared with that stored in a database. In Big data management, there are three important processes implemented to organize data. They are as follows: Compression using Lemperl Ziv Markow Algorithm (LZMA), Clustering using Density-based Clustering of Applications with Noise (DBSCAN), and Indexing using Fractal Index Tree. The proposed scheme for these processes are implemented using Java Programming and performance tested for the following metrics: Information Loss, Compression Ratio, Throughput, Encryption Time and Decryption Time. [ABSTRACT FROM AUTHOR]

Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility itemset mining is an emerging data science task, aims to extract knowledge based on a domain objective. The utility of a pattern shows its effectiveness or benefit that can be calculated based on user priority and domain-specific understanding. The sequential pattern mining (SPM) issue is much examined and expanded in various directions. Sequential pattern mining enumerates sequential patterns in a sequence data collection. Researchers have paid more attention in recent years to frequent pattern mining over uncertain transaction dataset. In recent years, mining itemsets in big data have received extensive attention based on the Apache Hadoop and Spark framework. This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain. Initially, we investigate the problem involved with pattern mining approaches and associated techniques such as Apache Hadoop, Apache Spark, parallel and distributed processing. Then we examine major developments in parallel, distributed, and scalable pattern mining, analyze them in the big data perspective and identify difficulties in designing the algorithms. In particular, we study four varieties of itemsets mining, i.e., parallel frequent itemsets mining, high utility itemset mining, sequential patterns mining and frequent itemset mining in uncertain data. This paper concludes with a discussion of open issues and opportunity. It also provides direction for further enhancement of existing approaches. [ABSTRACT FROM AUTHOR]

The tremendous growth of event dissemination over social networks makes it very challenging to accurately discover and track exciting events, as well as their evolution and scope over space and time. People have migrated to social platforms and messaging apps, which represent an opportunity to create a more accurate prediction of social developments by translating event related streams to meaningful insights. However, the huge spread of 'noise' from unverified social media sources makes it difficult to accurately detect and track events. Over the last decade, multiple surveys on event detection from social media have been presented, with the aim of highlighting the different NLP, data management and machine learning techniques used to discover specific types of events, such as social gatherings, natural disasters, and emergencies, among others. However, these surveys focus only on a few dimensions of event detection, such as emphasizing on knowledge discovery form single modality or single social media platform or applied only to one specific language. In this survey paper, we introduce multiple perspectives for event detection in the big social data era. This survey paper thoroughly investigates and summarizes the significant progress in social event detection and visualization techniques, by emphasizing crucial challenges ranging from the management, fusion, and mining of big social data, to the applicability of these methods to different platforms, multiple languages and dialects rather than a single language, and with multiple modalities. The survey also focuses on advanced features required for event extraction, such as spatial and temporal scopes, location inference from multi-modal data (i.e., text or image), and semantic analysis. Application-oriented challenges and opportunities are also discussed. Finally, quantitative and qualitative experimental procedures and results to illustrate the effectiveness and gaps in existing works are presented. • Classifying event detection with textual, spatial, temporal, and semantic features. • Defining event spatio-temporal evolution by considering incremental architectures. • Investigating fusion techniques from multiple data sources and multiple modalities. • Discussing various languages and dialects or language-independent mechanisms. • Presenting big data processing tools for scalable and efficient event detection. [ABSTRACT FROM AUTHOR]

• This paper proposes a smart contract-based architecture to improve the security of healthcare big data. • Here, healthcare big data is encoded and processed in IPFS decentralized storage, which decreases the processing overhead. • The access control and integrity checking features of the proposed work enhances healthcare data security. Big data refers to a very large and diverse set of data that grow at exponential rates. In the modern healthcare system, medical big data face many security issues due to the presence of hackers and malicious users. Nowadays, medical big data are tremendously benefitted by Blockchain technology due to its several features, namely decentralization, confidentiality, security, privacy, etc. Nevertheless, the conventional cloud and client-server-based information storage models in healthcare are suffering from single-point failure, centralized control of data resources and privacy leakage. This paper explores the usages of Blockchain technology to manage the healthcare system by providing a solution to these problems. Here, a distributed scheme is proposed for data management, which is used to implement Blockchain technology in the healthcare sector. The proposed scheme ensures security by specifying rules with a smart contract. Results and discussions show that the proposed scheme is more efficient than the existing schemes. [Display omitted] [ABSTRACT FROM AUTHOR]

Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful tools for representation and modeling of this data. Tensor decomposition can be used to extract potentially useful information from this data. Thus, it has attracted much attention from the big data community. The main target of this paper is to propose a novel data fusion framework to solve several main challenges of CPSS data applications, including CPSS big data representation, fusion, efficient computing, storage, robustness, and security issues. In this paper, we use many graphics to visualize complex tensor decomposition and transformation processes. The visualization may help the readers better understand tensor and tensor decomposition. It also provides a general guideline and a good starting point for those who are interested in tensor and tensor decomposition. Specifically, we first introduce the most extensively used matrix and tensor decomposition methods. Second, the current popular data fusion methods are reviewed and summarized. Third, we propose a novel tensor-network-conversion-based data fusion approach which can simultaneously analyze multiple matrices and multiple tensors. To better understand this approach, we give a brief review of tensor network. Fourth, based on the proposed approach, a novel CPSS big data fusion framework is proposed in this paper. Meanwhile, we verify it by a concise case study. Finally, some challenges and open problems of the proposed framework are discussed. The discussion also includes some exciting future research directions in the big data fusion field. • We review popular matrix decomposition, data fusion and tensor decomposition methods. • A novel Tensor-Network-Conversion-based data fusion approach (TNC) is proposed in this paper. • Our approach can simultaneously analyze multiple matrices and multiple tensors. • Based on the proposed approach, we propose a novel CPSS big data fusion framework. • A concise case study is used to verify the proposed framework. [ABSTRACT FROM AUTHOR]

Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based fuzzy c-means approach (IT-FCM) for obtaining smart data from continuously generated big data. Specifically, a weighted version of the tensor-based fuzzy c-means approach (T-FCM) is firstly proposed to cluster the dataset that combines the previous cluster centroids and the new generated data. Aiming to improve the clustering efficiency, the old data objects are represented by the centroids to avoid repeat clustering. Furthermore, this paper presents an edge-cloud-aided clustering scheme to fuse big data from different sources and perspectives and further to implement co-clustering on the fused datasets for exploring smart data. Finally, the proposed IT-FCM approach is evaluated by comparing with T-FCM regarding clustering accuracy and efficiency on two different datasets in the experiments. The results state that IT-FCM outperforms T-FCM in clustering streaming big data in terms of accuracy and efficiency for obtaining smart data. • An edge-cloud-aided computing system for co-clustering is presented. • An edge-cloud-aided computing system for co-clustering is presented. • Experiments are conducted to evaluate the presented approach. [ABSTRACT FROM AUTHOR]

In many computing applications, there is a need to estimate different features of large data sets, such as the number of distinct elements or the similarity with other data sets. This can be efficiently implemented using probabilistic data structures known as sketches; for example, Hyperloglog is widely used for cardinality estimate and Minhash for similarity estimate. Many of these sketches rely on computing hash functions on the elements and counting the number of consecutive leading zeros (or ones) to identify the minimum (or maximum) hash value for data updating. This functionality usually accounts for the most demanding part of the sketch implementation; an efficient and low power hash computation and leading zero counting are therefore required for high performance. A novel but important observation is exploited in this paper; namely the trailing hash bits are rarely used in these sketches (e.g., the leading zeros counting stops once the first one is found, which usually occurs after a few bits). Based on this feature, a so-called Two-Step Hashing (TSH) scheme is proposed in this paper to significantly reduce the power consumption. Therefore rather than computing all hash bits (as in the traditional scheme), TSH computes a few leading hash bits in a first step and the trailing hash bits in the second step, that is only required when the leading bits are all zeros (or ones). Hence, in most cases only a fraction of the hash bits is computed, so significantly reducing the power dissipation of hash computations when they are used to check the number of consecutive leading zeros, or the minimum value. The use of TSH for sketches with both single and multiple pipeline implementations are considered; evaluation results based on HyperLogLog (HLL) as a sketch with a Cyclic Redundancy Check hash function show that TSH saves up to 75.9 % (78.7 %) power dissipation in a single (multiple) pipeline implementation. • A so-called Two-Step Hashing (TSH) scheme is designed for efficiently implementing cardinality and similarity sketches. • The second step of the TSH scheme is rarely triggered only when the first step cannot make a decision for the sketches. • TSH scheme can be employed in sketches with both single and multiple pipeline implementations. • Evaluation results show that TSH saves up to 75.9% (78.7%) power dissipation in a single (multiple) pipeline implementation. [ABSTRACT FROM AUTHOR]

In this paper, we developed a novel data collection and analysis approach that uses manual and automated processing of large datasets and combines them to offset the limitations of past research and practice when it comes to identifying actors in complex environmental governance systems. Our empirical context is wildfire governance in Colorado. Wildfire governance represents a pressing challenge for interested actors in the western United States. We characterize Colorado's wildfire governance system by using big data triangulation techniques to identify key actors and their topical domains to reveal meaningful subgroups. Our results highlight the importance of understanding the pitfalls of each source of data and the potential benefits of combining them to gain a more complete understanding of the system. The true potential of multi-source analysis becomes apparent when one pays special attention to the degree of overlap that exists between sources. Each source reveals a unique makeup of actors (both in terms of individuals and organizational types) and different forms of engagement in the system. In addition to providing different sets of actors, we found evidence of different emergent topics in each data source. • We develop a novel method for processing large quantities of publicly available data for actor identification. • Multi-source data triangulation provides specific insights on the structure of the governance system. • Each source reveals a unique makeup of actors, their roles and topical domains in the system. • Minimal overlap between sources supports the need to critically consider biases and pitfalls associated to data sources. [ABSTRACT FROM AUTHOR]