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Noise Filtering for Big Data Analytics

Noise Filtering for Big Data Analytics
Author: Souvik Bhattacharyya
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 164
Release: 2022-06-21
Genre: Computers
ISBN: 3110697211

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This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.


Big Data Preprocessing

Big Data Preprocessing
Author: Julián Luengo
Publisher: Springer Nature
Total Pages: 193
Release: 2020-03-16
Genre: Computers
ISBN: 3030391051

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This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.


Big Data Analytics Methods

Big Data Analytics Methods
Author: Peter Ghavami
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 282
Release: 2019-12-16
Genre: Business & Economics
ISBN: 1547401583

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Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.


Research Practitioner's Handbook on Big Data Analytics

Research Practitioner's Handbook on Big Data Analytics
Author: S. Sasikala
Publisher: CRC Press
Total Pages: 310
Release: 2023-05-04
Genre: Computers
ISBN: 1000578364

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This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book’s authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media.


Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Author: Aboul Ella Hassanien
Publisher: Springer Nature
Total Pages: 648
Release: 2020-12-14
Genre: Computers
ISBN: 303059338X

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This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.


Software Project Management

Software Project Management
Author: Moh’d A. Radaideh
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 536
Release: 2023-12-18
Genre: Business & Economics
ISBN: 3111206866

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Software Project Management (SPM) differs from the Traditional Project Management (PM) approaches in that Software Engineering requires multiple rounds of Software Testing, and Updating in accordance with their Testing results and their customer’s feedback. Thus, SPM introduces unique life cycle processes.This book presents an introduction and a critical analysis of the main Software Project Management Frameworks, and offers the author’s original approach to SPM as developed by him over years of professional and teaching experience in the Academia and the IT/Software Industry. It also provides Executive Summaries of the Project Management and Software Project Management Perspectives offered by the Project Management Institute (PMI), the IEEE-Computer Society (IEEE-CS), and the SCRUM Project Management Bodies such as the SCRUMstudy.


The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
Author: John Macintyre
Publisher: Springer Nature
Total Pages: 999
Release: 2021-11-02
Genre: Computers
ISBN: 3030895114

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This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.


Big Data Analytics for Cyber-Physical System in Smart City

Big Data Analytics for Cyber-Physical System in Smart City
Author: Mohammed Atiquzzaman
Publisher: Springer Nature
Total Pages: 2049
Release: 2020-01-11
Genre: Technology & Engineering
ISBN: 9811525684

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This book gathers a selection of peer-reviewed papers presented at the first Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2019) conference, held in Shengyang, China, on 28–29 December 2019. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.


Big Data Analytics Techniques for Market Intelligence

Big Data Analytics Techniques for Market Intelligence
Author: Darwish, Dina
Publisher: IGI Global
Total Pages: 536
Release: 2024-01-04
Genre: Computers
ISBN:

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The ever-expanding realm of Big Data poses a formidable challenge for academic scholars and professionals due to the sheer magnitude and diversity of data types, along with the continuous influx of information from various sources. Extracting valuable insights from this vast and complex dataset is crucial for organizations to uncover market intelligence and make informed decisions. However, without the proper guidance and understanding of Big Data analytics techniques and methodologies, scholars may struggle to navigate this landscape and maximize the potential benefits of their research. In response to this pressing need, Professor Dina Darwish presents Big Data Analytics Techniques for Market Intelligence, a groundbreaking book that addresses the specific challenges faced by scholars and professionals in the field. Through a comprehensive exploration of various techniques and methodologies, this book offers a solution to the hurdles encountered in extracting meaningful information from Big Data. Covering the entire lifecycle of Big Data analytics, including preprocessing, analysis, visualization, and utilization of results, the book equips readers with the knowledge and tools necessary to unlock the power of Big Data and generate valuable market intelligence. With real-world case studies and a focus on practical guidance, scholars and professionals can effectively leverage Big Data analytics to drive strategic decision-making and stay at the forefront of this rapidly evolving field.


Big-Data-Analytics in Astronomy, Science, and Engineering

Big-Data-Analytics in Astronomy, Science, and Engineering
Author: Shelly Sachdeva
Publisher: Springer Nature
Total Pages: 283
Release: 2022-02-17
Genre: Computers
ISBN: 3030966003

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This book constitutes the proceedings of the 9th International Conference on Big Data Analytics, BDA 2021, which took place virtually during December 7–9, 2021. The 15 full papers and 1 short paper included in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: Data science: systems; data science: architectures; big data analytics in healthcare support systems, information interchange of web data resources; and business analytics.