IEEE BIG DATA IEEE PAPER 2022
The IEEE Future Directions Big Data Initiative strives to aggregate information about the various endeavors occurring worldwide in order to provide a community of professionals in industry, academia, and government working to solve the challenges associated with Big Data.
A Study in Performing Big Data Analytics with Limited Resources
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Kaur and KM Abbas, Spatial big data analytics of influenza epidemic in Vellore, India, 2014 IEEE International Conference on Big Data ( Big Data ), 201 pp. 19-2 doi: 10.1109/
A Progressive Real-time Visualization Method for Earthquake Big Data
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big data visualization schemes based on sampling and filtering can greatly reduce the data interactive analytics on large data IEEE International Conference on Big Data 2017.Abstract: Big data is a collection of large volume of data and extract similar data points from Therefore, the clustering is the significant data mining technique used for big data analysis.big data has emerged as an absolute prerequisite for exchanging private data in terms of data In this work, a differential privacy protection scheme for big data in body area network is
Based on Big Data Analysis on The Influence of Extracurricular Activities on the Character Education of High School Students in China and Indonesia
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Data processing technology The data processing techniques in this analysis use the help of big data analysis, namely: a. The normality test. This test aims to determine whether the Big Data and Artificial Intelligence are two technologies that are about Big Data Intelligence. AI has become ubiquitous in many industries where intelligent programs relying on big data This paper introduces a machine learning model big databased hybrid hierarchical model (BDHHM) with an Apache spark framework. BDHHM is a hybrid of two hierarchical models
Implementation of Novel Association Rule Hiding Algorithm Using FLA with Privacy Preserving in Big Data Mining
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Big data is also aimed at increasing fertility flows in this conference. In data object to a single value, (3) the use of many similarity metrics to compare the initial data and mapped data .
A Review on Tourist Analyzer
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By using a big data platform, this paper analyzes the track data of tourists mobile phone, data warehouse, Hadoop cluster and the database of My SQL, which includes three modules.
Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia. Big Data Cogn. Comput. 2022
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feature importance in data streams with concept drift using Online Random Forest. In Proceedings of the IEEE International Conference on Big Data ( Big Data ), Washington, DC,
A Concise Review of Big Data and Cloud Computing Paradigms and Principles
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Big data Structured, unstructured and semi structured data Volume of Big data Speed of data generation Value of Big data it valuable Velocity of Big data Speed of data generationthe adoption of big data in relation to education and to translate the adoption of big data in to use behavior, in turn impacting the adoption of big data in education, this research used the In digital healthcare systems, the patients face significant problems identifying an optimal vacant and available slot for their appointment as the number of patient requests directlyto their data processing capability, is both necessary and difficult. The authors here propose a benchmarking method for the comprehension of privately owned blockchains against data
A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
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virtualisation and big data for a systematic approach to stream processing. The novel contributions of this research are: 1) MC-BDP, a reference architecture for big data stream
An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
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and process Big Data . SDN demonstrates that it can also benefit from big data such as traffic system performance for Hadoop-based Big Data applications by fully taking advantage of
Challenges, Research Issues and Tools involved in Big data and Hadoop
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markets whereas internet search indexing includes ISI, IEEE Xplorer, Scopus, Thomson Reuters etc. Considering these advantages of big data it provides a new opportunity in the These are data science and big data Internet of things, and machine learning. There were a total member of IEEE USA, and IEICE, Japan, and active volunteer of the IEEE Bangladesh
Scope, Challenges and Optimization in Data Analytics
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big data analytics, handle the various challenges throughout the analytics process associated with the big data . improved differential evolution algorithm, IEEE Transactions on systems,
Improving the Quality Validation of Semi-Structured Data Process Using Hadoop
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the big data and how to analyze an appropriate algorithm to nd the useful things from big data To deeply discuss this issue, this paper begins with a brief introduction to big data testing,
How Digital Data Are Used in the Domain of Health: A Short Review of Current Knowledge
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big data and physical health, big data and mental health, digital contact tracing during the COVID-19 pandemic, and ethical issues with using digital data IEEE Xplore, 5 (4), 46 51. https:
Economic analysis through alternative data and big data techniques: what do they tell about Brazil
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The alternative data and the big data techniques used in this research bring two innovative contributions to the Brazilian economic literature. First, the economic development potential
A Scalable Big Data Framework for Real-Time Traffic Monitoring System
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In this context we use big data solutions because it is an integrated production platform and includes all Apache Hadoop ecosystem (MapReduce, Hive, Pig, Impala, Mahout, HBase, new and unusual data patterns (eg, Wen et al. 2018). In the big data era, data are being and volume, which is a result of automated technologies for data acquisition. Aggregated to the field of astrophysics and big data . Our target is to big data and how rhythm engineering could be used in understanding and fathoming the intricate dynamic patterns in a big data .
A Review on Medical Image Analysis using Deep Learning
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This is all about big data so big image datasets like ImageNet have been used. Matlab, Now a day, big data is being analyzed and evaluated using deep learning techniques. Many
Twitter Data Collection Using Tools for Classification of fake Data : A Survey
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social media graphs, Big Data vol. no. pp. IEEE ) and Kun Wang (senior member, IEEE ), Big data analytics for program popularity prediction in broadcast TV industries , IEEE Notwithstanding the pressing demand for data driven by AI and big data the inconvenient truth is that the ambitious goals set by the European Commission will be at risk so long as
Combining Container Orchestration and Machine Learning in the Cloud: a Systematic Mapping Study
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Currently new challenges arise related to the processing of big data but also the optimized management of increasing amount of heterogeneous workloads in a cloud environment. The
Multiscale Data Analytics for Residential Active Power Measurements through Time Series Data Mining
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Lo, Evaluation of ami and scada data synergy for distribution feeder modeling, IEEE ters big data : Game theoretic model for fair data sharing in deregulated smart grids, IEEE Access,
Analysis of Road Accidents in UK 1979-2015 Using Big Data Approach.
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In this project, the goal is to analyze causes for road accidents, finding interest patterns, and finding solutions for avoiding or reducing road accidents using big data analytics techniques
An Effective Entity Resolution Approach for Big Data
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To overcome such issues, we propose a novel and efficient ER approach for big data implemented in Apache Spark. The proposed approach is employed to avoid schema alignment as
Loan Default Prediction Using Spark Machine Learning Algorithms
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In this paper, the application of data mining and Big Data techniques in building loan default predictors is studied. Six models were developed using the Spark MLlib API allowing us to
Development of large-scale big data system with state management on serverless cloud architectures
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Furthermore, the main Big Data analytics framework that was the motivation of this research, Apache Spark, will be mentioned thoroughly. More specifically, we analyze its architecture,
Data Lineage Management with Unlearning Method for Machine Learning Security and Privacy Issues
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In today s big data environment, users interact with data on various web platforms, such as sending and receiving emails, browsing news, etc., almost every day. For users, once they
Data Science and Big Data in Upper Secondary Schools: What Should Be Discussed From a Perspective of Computer Science Education
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In the present days and due to the increasing availability of big data data science is becoming more and more relevant in our daily lives, as can be seen eg by the visualisation of
DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS
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With big data growth in biomedical and healthcare communities, accurate analysis of medical data support vector machines for diagnosis of diabetes mellitus, IEEE Trans. Inf. Technol.
A Review on Human Activity Recognition Techniques and Comparative Performance Analysis
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that are designed to find the human intelligence by learning from the surrounding existing environment and it considered the working horse in the new era of the so-called big data also.
Big Data Flow Errors Correction Model Based on Q-Learning
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The importance of data reliability leads us to further rethink error management, for this errors in Big Data flows based on Q-learning. The idea is to deal with structured data flows where data analytics perspective. IoT systems will continuously generate massive data that requires big data renowned international conferences including IEEE Healthcom, IEEE SP-DLT. Dr.
Blood Pressure Value Prediction in Hemodialysis Patients Using the CNN+ GRU Network Architecture
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However, on this task, a prediction model has not been studied extensively with the advent of big data models. Earlier machine learning models lacked the exploitation of precise
A Systematic Literature Review of Privacy Protection Methods in Federated Learning: Issues, Classification, and Application
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learning, in IEEE Symposium on Security and Privacy (SP), 201 pp. 739 753. Z. inferring class representatives: User-level privacy leakage from federated learning, in IEEE
A Glance at Contemporary Visual Data
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data as well as to use this bulk data efficiently. With the advent of the digitalization era, concept of big data first appeared. It is because of the enormous volumes of data that they
When AI Meets the Internet of Things
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artificial intelligence, big data analytics and data visualization in Xiang, A novel hybrid code-domain index modulation, IEEE neural networks, IEEE Geoscience and Remote Sensing
An Overview of Metrics for Evaluating the Quality of Graph Partitioners
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Abstract: Nowadays, many applications that involve big data can be modelled as graphs. In many cases these graphs be too large to be loaded and processed on a single of Big Data many scholars have paid more attention to big graph. One of the most important research areas about big can be used in a variety of big graphs, such as social networks,big data we propose to diversify a propagation delay to practical extension. This article then focuses on a real-world situation and how the IEEE in this work regarding IEEE 802.11ay,of big data and data mining. Her research interests are in the area of Big Data Analytics Scopus, UGC, Elsevier) and Conferences (ACM, IEEE ). Her research area Natural Language
A Systematic Literature Review of Software Vulnerability Detection
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) by searching ACM and IEEE databases for related literatures. journals and conference proceeding of IEEE and ACM from to on big data security on cloud (bigdatasecurity), ieee
Digitalization and Emerging Technologies for Resilience and Recovery: Disruptive technologies for smart cities management
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Of particular interest are the applications of sensors, blockchain and big data processing possible Combining the growth of new technologies like artificial intelligence, big data and He is currently the Director of the Application Promotion Division, Sichuan Big Data Center. He has long been engaged in big data application research and development and construc
An Intelligent Literature Review: an Inductive Approach to define Machine Learning Applications in the clinical domain
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big data using various machine learning techniques and utilizing them to reshape their business frameworks. Big data analytics techniques analyze vast amount of data PubMed, IEEE
Dissecting, Designing, and Optimizing LSM-based Data Stores
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The resurgence of LSM-trees can be attributed to the birth of big datacentric technologies, such as NoSQL systems [4 7 8 9 113], cloud computing [ 2 4 4 61], mobile
Government Construction Project Budget Prediction Using Machine Learning
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ML, an analytical technique for big data technology, was used as a tool in this study. In addition, k-Nearest Neighbors (KNN), an ML algorithm, was used to classify over-budget projects.
HEART DISEASE PREDICTION USING K MEANS CLUSTERING
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data analytic model in the real healthcare domain and cancer severity to analyze the data in real-time data embedding, IEEE Institute of Electrical and Electronics Engineers.2016
Estimating Indoor Population Density from Non-contact Sensor Data
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types of environmental data which can be used for big data analysis. data obtained using the IoT with machine learning and/or big in homes, IEEE Sensors Journal, vol. 1 no. pp.
Security and Safety Issues in Online Community Web Sites and Physical Systems
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Latest trends and technology of big data open a new challenge in several research areas such as: ML, data mining, different community networks, etc. Now a day the community
Designing a Smart Pillow for Sleep Quality Remote Monitoring
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The rapid growth of IoT allied with others technologies like Cloud Computation, Big Data and insomnia using multi-night actigraphy data IEEE Access, vol. pp. 74 413 74 42 2020.
FLEXIBLE MACHINE LEARNING-BASED CYBERATTACK DETECTION USING SPATIOTEMPORAL PATTERNS FOR DISTRIBUTION SYSTEMS
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Test feeds for IEEE 13 and 123 nodes are utilised to demonstrate the approachs study of smart grid big data analytics. An innovation centre for smart metre data analytics has been
Spatial multivariate trees for big data Bayesian regression
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In addition to simulated data examples, we illustrate SpamTrees using a large climate data set which combines satellite data with land-based station data . Software and source code are
ieee big data IEEE PAPER 2021
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Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Big Data helps the organizations to create new growth opportunities and entirely new categories of companies that can combine and analyze industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed.
Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more.
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
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Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its There are many possibilities for smart buildings that have well provisioned and managed internet of things (IoT) frameworks where seamless data acquisition from sensors, processing to analytics can bring benefits to vast domains. Specifically, in building
Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
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However, with the huge increase in data size, sophisticated models have to be created which require big data platforms Parallel computing is demanded to allow for optimal resource utilization in dealing with smart grid big data Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable
Deep learning-based short-term load forecasting approach in smart grid with clustering and consumption pattern recognition
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The Open Access Funding was provided by Qatar National Library. ABSTRACT Different aggregation levels of the electric grids big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networksCloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the users direct This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big data processing in a distributed manner. In order to realize UAV-based aerial There is very limited study available to predict the various disease using big data framework Spark using deep learning spark is an open source fast, flexible and developer friendly unified analytics engine for large scale data processing for Bigdata machine learning of bigdata analytic approaches, Published in: International Conference on Computing, Communication and Automation (ICCCA), Publisher: IEEE, DOI: 10.1109/CCAA.2017.822982 Accession Number: 17449090 [17] M. Ambigavathi ; D. Sridharan, Big Data Analytics in Traditional digital signal processing technology based on DSP and FPGA is more suitable for real-time signal processing, and is limited by data scale and frequency resolution, making it unsuitable for offline data processing, analysis and mining under large-scale data In this paper, Heterogeneous Fair Resource Allocation and Scheduling (HFRAS) for cloud based Big Data Streams, is proposed. In this algorithm, a weight value is determined for the user for each of the requested resource, based on the resource priorities. Then each task is
Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
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ABSTRACT Big data analysis has become a crucial part of new emerging technologies such as the internet of things, cyber-physical analysis, deep learning, anomaly detection, etc Randomized algorithms are efficient tools for handling big data tensors
Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results
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empirical computational methods are less adequate representation of the actual data . Therefore, deep learning models are proposed for the analysis of big data and give more accurate representation of real measurements
A multiprocessing-based sensitivity analysis of machine learning algorithms for load forecasting of electric power distribution system
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INDEX TERMS Big data applications, machine learning algorithms, parallel processing, load forecast, smart grids Big data analytics with artificial intelligence techniques are key for proper understanding and utilization of smart meter data In the context of the rapid development of informatization and electronics, the Internet is becoming more and more popular, and C2C e-commerce is developing faster and faster. However, C2C e-commerce still has some problems in the tax management process, which
On the Use of Google Earth Engine and Sentinel Data to Detect“LostSections of Ancient Roads. The Case of Via Appia
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Index Terms Big data Copernicus, Google earth engine (GEE), Sentinel 1 (S-1), Remote sensing for archeology, Sentinel 2 (S-2). I. INTRODUCTION BIG Earth Observation (EO) data emerged in the past few years as powerful tools in archeology serving numerous
Understanding the Challenges and Assisting Developers with Developing Spark Applications
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Abstract To process data more efficiently, big data frame- works provide data abstractions to developers To uncover the challenges in using big data frameworks, we first conduct an empirical study on 000 Apache Spark-related questions on Stack Overflow
Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data
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The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study Population migration big data of Baidu map (PMBD) is supported by Baidu (Chinas largest integrated internet ser- vice company)
A survey of machine learning applications to handover management in 5G and beyond
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I. INTRODUCTION Wireless communication networks have been witnessing an unprecedented demand in terms of bandwidth and number of connections in this so-called information age in par- ticular the age of big data 1 where data is regarded as new oil . It is reported in
Guest Editorial: Sustainable and intelligent precision agriculture
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Abstract The fourth industrial revolution is ongoing, that is characterized by a fusion of emerging technologies such as Industry 4.0, Internet of Things, cloud/edge computing, big data artificial intelligence, and blockchain. Correspondingly Therefore, how to choose a suitable network model and improve the data classification accuracy in heterogeneous networks is a big challenge The data fusion technology in the heterogeneous net- work is of great significance to the processing of big data (Corresponding author: Lei Li.) Lei Li and Ping Ding are with the Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, China, Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei Univer- sity of decryption stages. Index Terms IIoT, Blockchain, Big Data Smart Factory, Data Sharing, Traceable and Revocation. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373162
A survey on deep learning for datadriven soft sensors
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Index Terms Soft Sensor, Deep Learning, Industrial Big Data Datadriven Modeling, Neural Networks In contrast, in big data regions where there are very large training sets, it can be more consistently seen that large NN dominates the other approaches Abstract Large data centers are currently the mainstream infrastructures for big data processing As the core operations in these systems, distributed data operators such as join and aggregation are widely used for big data computing
An efficient data access approach with queue and stack in optimized hybrid join
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and business intelligence on basis of real-time data warehouse (RTDW) . Decision support systems are dependent on RTDW, Enterprise Service Bus (ESB) applications and big data : a repository of complex and large data that can be analyzed for decision making
Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis
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The associate editor coordinating the review of this manuscript and approving it for publication was Sotirios Goudos . flow analysis [17], Internet of Things (IoT) [18] and Big Data , just to name a few It con- tributes to big data growth in general
Towards a service-oriented architecture for the energy efficiency of buildings: A Systematic Review
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However, current BMSs are incapable of managing a massive amount of data ( big data ) and therefore cannot extract knowledge or make intelligent decisions in quasi real time Does the article present characteristics of an architecture based on big data This confirms that remote sensing data have entered the big data era . To be more specific, we summarize, in the following, the properties that qualify remote sensing data as a kind of big data . 1) Data Volume: As mentioned before, remote sensing data in the optical, radarScientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A cloud-native data repository, as defined in this article, offers several model and random perturbation strategies. In , the authors leveraged DP to prevent data privacy leak- age in wireless big data collection and analysis, and proposed two perturbation schemes. One scheme adds noise to perturbMeta Knowledge Graph is mainly a knowledge graph formed by the most basic concept entity and its synonymous relationship, which is the most direct entry to show the basic knowledge of the industry. However, due to the complexity of its data sources and the lack of
Multi Model Implementation on General Medicine Prediction with Quantum Neural Networks
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Naimul Mefraz Khan et al., Transfer learning with intelligent training data selection for and V. Dutt, Improving Health Care by Help of Internet of Things and Bigdata Analytics and FSBi-LSTM, Volume 201 IEEE Access SHAH NAZIR et al., Big Data Visualization in With the development of smart grid, IOT devices in the field of power control are widely used. The diversity of types, features and functions of IOT devices not only integrates detailed big data but also challenges the real-time, compatibility and big data processing capability ofmanufacturing environment. In the current manufacturing environment, there is a problem due to the server load caused by big data . In order to solve this problem, studies on various aspects of data processing are being conducted. We
Guest Editorial: Special Issue on AI-Enabled Internet of Dependable and Controllable Things
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He is the editor of six books, including Big Data Analytics for Cyber Physical Systems: Machine Learning for the Internet of Things (Elsevier), Smart Cities: Foundations, Principles, and Applications (Hoboken, NJ, USA: Wiley), Security and Privacy in Cyber Physical
Peer-to-Peer Energy Trading Mechanism based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid
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How- ever, the on-demand use of energy is considered a big challenge to achieve the optimal cost for house- holds The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy Therefore, it is unsuit- able for detecting anomalies in big data Moreover, its time complexity is O(n), which means it is a linear-time algorithm; therefore, it is suitable for anomaly detection in big data . However, iFor- est is not a perfect method despite its benefits
When the bad is good and the good is bad: Understanding cyber social health through online behavioral change
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The insights derived from this thick data modeling approach will contextualize the big data analytics at larger scale and provide deeper insights (see thick data modeling section for more.) Harmful content flows through online communi- ties, where information diffusion of such First, the raw vibration signals as well as envelope signals from source domain machine are obtained. Thereafter, these envelope signals are applied to NCNN for the learning of features from the big training data acquitted from the source domain On the basis of traditional evaluation methods, this paper puts forward the concept of big data for condition evaluation, aiming at introducing new concepts and algorithms into the traditional large power grid industry, so as to improve the reliability of the system Power grid
Machine learning in measurement part 1: error contribution and terminology confusion
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measurement system. This is of particular importance for measurement in the age of big data because we need to evaluate the trustworthi- ness of the available data and their impact on the derived conclusions and decision-making
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
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To obtain useful information from big data in IoT, machine learning techniques are often applied . Particularly, Federated Learning (FL) , , also known as federated optimization, allows multiple parties to collaboratively train a model without data sharing and gathering
A Gradient-Based Clustering for Multi-Database Mining
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This process is called clustering, which is an important unsupervised technique for big data mining. In this article, we present an effective approach to search for the optimal clustering of multiple transaction databases in a weighted undirected similarity graph
Collaborative intelligence: Challenges and opportunities
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Compared with the traditional cloud-centered data process- ing mode, the CI system faces many challenges for big data processing, such as latency constraints, and memory occu- pation. Big data processing for CI relies on huge computing resources
Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service
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Page 1. SPECIAL SECTION ON INTELLIGENT BIG DATA ANALYTICS FOR INTERNET OF THINGS, SERVICES AND PEOPLE Received February 2 202 accepted March 1 202 date of publication March 1 202 date of current version April 1
IoT and Fog Computing based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 using Machine Learning
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Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment Industrial big data generated from IIoT sensors promotes information visibility The impact reaches the understanding level of what the user is expecting from it . The medical data can be gathered using the advanced machine learning and big data tools like Hadoop, map reduce and map the variables which are needed security space. However, the assumption in many current works is that big training data is widely available and transferable to the main server because data is born at the edge and is generated continuously by IoT devices. This is
Software Engineering for IoT-Driven Data Analytics Applications
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tools, and technologies that transform raw data into useful information using software applications . The International Data Corporation forecasts that by the year 2022 the revenue generated by big data analytics will reach approximately 274.3 billion USD . Despite the In future we can trained our model on big data using Spark ML and even on deep learning bimodal approach to get better computation time [23-25]. In Table our proposed technique outperform the two literature works [2 29]
A Deep-Learning Model for Estimating the Impact of Social Events on Traffic Demand on a Cell Basis
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To this end, the latest SON platforms leverage massive performance data in the Operation Support System (OSS) by applying Big Data techniques to estimate QoE per connection , . Nonetheless, the large number of factorsA New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System Zhihua Cui, Xuechun Jing, Peng Zhao, Wensheng Zhang, Jin Chen In order to reduce cost and reply promptly, we deploy artificial intelligence (AI) models for data analysis on edge servers
Improving the prediction of heart failure patients survival using SMOTE and effective data mining techniques
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death ratio is reduced . Data mining plays an immense role in extracting useful information from big data . It is widely used in almost every field of life like medicine, engineering, business, and educa- tion. Data mining is used