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11.
Подробнее
D 24
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications [[electronic resource].]. - [Б. м.] : Medical Information Science Reference, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D3E6475D-A8A7-4D96-80E6-796F6E721403. - ISBN 1799812065. - ISBN 9781799812067 (electronic bk.)
~РУБ DDC 610.285
Рубрики: Medical informatics.
Medicine--Data processing.
Big data.
Data mining.
D 24
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications [[electronic resource].]. - [Б. м.] : Medical Information Science Reference, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D3E6475D-A8A7-4D96-80E6-796F6E721403. - ISBN 1799812065. - ISBN 9781799812067 (electronic bk.)
Рубрики: Medical informatics.
Medicine--Data processing.
Big data.
Data mining.
12.
Подробнее
M 30
Marin, Ivan.
Big data analysis with Python : : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK. - Birmingham, UK : : Packt Publishing,, ©2019. - 1 online resource (276 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A54B9FA8-4515-4245-ADFA-20CAFAA69C26. - ISBN 1789950732 (electronic book). - ISBN 9781789950731 (electronic book)
Description based on online resource; title from digital title page (viewed on January 06, 2020).
Параллельные издания: Print version: : Marin, Ivan. Big Data Analysis with Python : Combine Spark and Python to Unlock the Powers of Parallel Computing and Machine Learning. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789955286
Содержание:
Chapter 1: The Python Data Science Stack -- Chapter 2: Statistical Visualizations -- Chapter 3: Working with Big Data Frameworks -- Chapter 4: Diving Deeper with Spark -- Chapter 5: Handling Missing Values and Correlation Analysis -- Chapter 6: Exploratory Data Analysis -- Chapter 7: Reproducibility in Big Data Analysis -- Chapter 8: Creating a Full Analysis Report
~РУБ DDC 005.7
Рубрики: Big data.
Python (Computer program language)
Cloud computing.
Machine learning.
Big data.
Cloud computing.
Machine learning.
Python (Computer program language)
Аннотация: Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract ...
Доп.точки доступа:
Shukla, Ankit.
VK, Sarang.
M 30
Marin, Ivan.
Big data analysis with Python : : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK. - Birmingham, UK : : Packt Publishing,, ©2019. - 1 online resource (276 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A54B9FA8-4515-4245-ADFA-20CAFAA69C26. - ISBN 1789950732 (electronic book). - ISBN 9781789950731 (electronic book)
Description based on online resource; title from digital title page (viewed on January 06, 2020).
Параллельные издания: Print version: : Marin, Ivan. Big Data Analysis with Python : Combine Spark and Python to Unlock the Powers of Parallel Computing and Machine Learning. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789955286
Содержание:
Chapter 1: The Python Data Science Stack -- Chapter 2: Statistical Visualizations -- Chapter 3: Working with Big Data Frameworks -- Chapter 4: Diving Deeper with Spark -- Chapter 5: Handling Missing Values and Correlation Analysis -- Chapter 6: Exploratory Data Analysis -- Chapter 7: Reproducibility in Big Data Analysis -- Chapter 8: Creating a Full Analysis Report
Рубрики: Big data.
Python (Computer program language)
Cloud computing.
Machine learning.
Big data.
Cloud computing.
Machine learning.
Python (Computer program language)
Аннотация: Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract ...
Доп.точки доступа:
Shukla, Ankit.
VK, Sarang.
13.
Подробнее
S 43
Security, privacy, and forensics issues in big data / / [edited by] Ramesh C. Joshi, Brij B. Gupta. - 4018/978-1-5225-9742-1. - Hershey, Pennsylvania : : IGI Global,, [2020]. - 1 online resource. ( час. мин.), 4018/978-1-5225-9742-1. - (Advances in Information Security, Privacy, and Ethics (AISPE) Book Series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/56956677-A1D3-46DB-BBEA-CF8B78FA4DA4. - ISBN 1522597441 (electronic book). - ISBN 9781522597445 (electronic bk.)
Description based on online resource; title from digital title page (viewed on December 02, 2019).
Параллельные издания: Print version: :
Содержание:
Chapter 1. Securing the cloud for big data -- Chapter 2. Big data: challenges and solutions -- Chapter 3. Human factors in cybersecurity: issues and challenges in big data -- Chapter 4. Security and privacy challenges in big data -- Chapter 5. Cloud-centric blockchain public key infrastructure for big data applications -- Chapter 6. Security vulnerabilities, threats, and attacks in IoT and big data: challenges and solutions -- Chapter 7. Threat hunting in windows using big security log data -- Chapter 8. Nature-inspired techniques for data security in big data -- Chapter 9. Bootstrapping urban planning: addressing big data issues in smart cities -- Chapter 10. Securing online bank's big data through block chain technology: cross-border transactions security and tracking -- Chapter 11. Enhance data security and privacy in cloud -- Chapter 12. The unheard story of organizational motivations towards user privacy -- Chapter 13. Botnet and Internet of things (IoTs): a definition, taxonomy, challenges, and future directions -- Chapter 14. A conceptual model for the organizational adoption of information system security innovations -- Chapter 15. Theoretical foundations of deep resonance interference network: towards intuitive learning as a wave field phenomenon -- Chapter 16. Malware threat in internet of things and its mitigation analysis -- Chapter 17. Leveraging fog computing and deep learning for building a secure individual health-based decision support system to evade air pollution.
~РУБ DDC 005.8
Рубрики: Big data.
Cyber intelligence (Computer security)
Privacy, Right of.
Digital forensic science.
Big data.
Cyber intelligence (Computer security)
Digital forensic science.
Privacy, Right of.
Аннотация: "This book focuses on the security, privacy, and forensics of big data. It also examines the principles, algorithms, challenges and applications related to the security, privacy, and forensics of big data"--
Доп.точки доступа:
Joshi, R. C., \editor.\
Gupta, Brij, (1982-) \editor.\
IGI Global,
S 43
Security, privacy, and forensics issues in big data / / [edited by] Ramesh C. Joshi, Brij B. Gupta. - 4018/978-1-5225-9742-1. - Hershey, Pennsylvania : : IGI Global,, [2020]. - 1 online resource. ( час. мин.), 4018/978-1-5225-9742-1. - (Advances in Information Security, Privacy, and Ethics (AISPE) Book Series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/56956677-A1D3-46DB-BBEA-CF8B78FA4DA4. - ISBN 1522597441 (electronic book). - ISBN 9781522597445 (electronic bk.)
Description based on online resource; title from digital title page (viewed on December 02, 2019).
Параллельные издания: Print version: :
Содержание:
Chapter 1. Securing the cloud for big data -- Chapter 2. Big data: challenges and solutions -- Chapter 3. Human factors in cybersecurity: issues and challenges in big data -- Chapter 4. Security and privacy challenges in big data -- Chapter 5. Cloud-centric blockchain public key infrastructure for big data applications -- Chapter 6. Security vulnerabilities, threats, and attacks in IoT and big data: challenges and solutions -- Chapter 7. Threat hunting in windows using big security log data -- Chapter 8. Nature-inspired techniques for data security in big data -- Chapter 9. Bootstrapping urban planning: addressing big data issues in smart cities -- Chapter 10. Securing online bank's big data through block chain technology: cross-border transactions security and tracking -- Chapter 11. Enhance data security and privacy in cloud -- Chapter 12. The unheard story of organizational motivations towards user privacy -- Chapter 13. Botnet and Internet of things (IoTs): a definition, taxonomy, challenges, and future directions -- Chapter 14. A conceptual model for the organizational adoption of information system security innovations -- Chapter 15. Theoretical foundations of deep resonance interference network: towards intuitive learning as a wave field phenomenon -- Chapter 16. Malware threat in internet of things and its mitigation analysis -- Chapter 17. Leveraging fog computing and deep learning for building a secure individual health-based decision support system to evade air pollution.
Рубрики: Big data.
Cyber intelligence (Computer security)
Privacy, Right of.
Digital forensic science.
Big data.
Cyber intelligence (Computer security)
Digital forensic science.
Privacy, Right of.
Аннотация: "This book focuses on the security, privacy, and forensics of big data. It also examines the principles, algorithms, challenges and applications related to the security, privacy, and forensics of big data"--
Доп.точки доступа:
Joshi, R. C., \editor.\
Gupta, Brij, (1982-) \editor.\
IGI Global,
14.
Подробнее
D 30
Deep learning innovations and their convergence with big data / / S. Karthik, SNS College of Technology, Anna University, India ; Anand Paul, Kyungpook National University, South Korea ; N. Karthikeyan, Mizan-Tepi University, Ethiopia. - Hershey, PA : : IGI Global,, ©2018. - 1 online resource (xxii, 265 pages) : : il. - (Advances in data mining and database management (ADMDM) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/E0332674-3832-4B56-ABA3-FB5F18A9DFB1. - ISBN 9781522530169 (electronic bk.). - ISBN 1522530169 (electronic bk.)
"Premier reference source"--Cover. Description based on print version record.
Параллельные издания: Print version: : Deep learning innovations and their convergence with big data. - Hershey, PA : Information Science Reference, [2018]. - ISBN 9781522530152
Содержание:
Advanced threat detection based on big data technologies / Madhvaraj M. Shetty, Manjaiah D.H. -- A brief review on deep learning and types of implementation for deep learning / Uthra Kunathur Thikshaja, Anand Paul -- Big spectrum data and deep learning techniques for cognitive wireless networks / Punam Dutta Choudhury, Ankumoni Bora, Kandarpa Kumar Sarma -- Efficiently processing big data in real-time employing deep learning algorithm / Murad Khan, Bhagya Nathali Silva, Kijun Han -- Digital investigation of cybercrimes based on big data analytics using deep learning / Ezz El-Din Hemdan, Manjaiah D. H. -- Classifying images of drought-affected area using deep belief network, kNN, and random forest learning techniques / Sanjiban Sekhar Roy, Pulkit Kulshrestha, Pijush Samui -- Big data deep analytics for geosocial networks / Muhammad Mazhar Ullah Rathore, Awais Ahmad, Anand Paul -- Data science: recent developments and future insights / Sabitha Rajagopal -- Data science and computational biology / Singaraju Jyothi, Bhargavi P-- After cloud: in hypothetical world / Shigeki Sugiyama -- Cloud-based big data analytics in smart educational system / Newlin Rajkumar Manokaran, Venkatesa Kumar Varathan, Shalinie Deepak.
~РУБ DDC 006.3/1
Рубрики: Machine learning--Technological innovations.
Big data.
COMPUTERS / General
Аннотация: "This book capture the state of the art trends and advancements in big data analytics, its technologies, and applications. The book also aims to identify potential research directions and technologies that will facilitate insight generation in various domains of science, industry, business, and consumer applications"--
Доп.точки доступа:
Karthik, S., (1977-) \editor.\
Paul, Anand, \editor.\
Karthikeyan, N., (1977-) \editor.\
D 30
Deep learning innovations and their convergence with big data / / S. Karthik, SNS College of Technology, Anna University, India ; Anand Paul, Kyungpook National University, South Korea ; N. Karthikeyan, Mizan-Tepi University, Ethiopia. - Hershey, PA : : IGI Global,, ©2018. - 1 online resource (xxii, 265 pages) : : il. - (Advances in data mining and database management (ADMDM) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/E0332674-3832-4B56-ABA3-FB5F18A9DFB1. - ISBN 9781522530169 (electronic bk.). - ISBN 1522530169 (electronic bk.)
"Premier reference source"--Cover. Description based on print version record.
Параллельные издания: Print version: : Deep learning innovations and their convergence with big data. - Hershey, PA : Information Science Reference, [2018]. - ISBN 9781522530152
Содержание:
Advanced threat detection based on big data technologies / Madhvaraj M. Shetty, Manjaiah D.H. -- A brief review on deep learning and types of implementation for deep learning / Uthra Kunathur Thikshaja, Anand Paul -- Big spectrum data and deep learning techniques for cognitive wireless networks / Punam Dutta Choudhury, Ankumoni Bora, Kandarpa Kumar Sarma -- Efficiently processing big data in real-time employing deep learning algorithm / Murad Khan, Bhagya Nathali Silva, Kijun Han -- Digital investigation of cybercrimes based on big data analytics using deep learning / Ezz El-Din Hemdan, Manjaiah D. H. -- Classifying images of drought-affected area using deep belief network, kNN, and random forest learning techniques / Sanjiban Sekhar Roy, Pulkit Kulshrestha, Pijush Samui -- Big data deep analytics for geosocial networks / Muhammad Mazhar Ullah Rathore, Awais Ahmad, Anand Paul -- Data science: recent developments and future insights / Sabitha Rajagopal -- Data science and computational biology / Singaraju Jyothi, Bhargavi P-- After cloud: in hypothetical world / Shigeki Sugiyama -- Cloud-based big data analytics in smart educational system / Newlin Rajkumar Manokaran, Venkatesa Kumar Varathan, Shalinie Deepak.
Рубрики: Machine learning--Technological innovations.
Big data.
COMPUTERS / General
Аннотация: "This book capture the state of the art trends and advancements in big data analytics, its technologies, and applications. The book also aims to identify potential research directions and technologies that will facilitate insight generation in various domains of science, industry, business, and consumer applications"--
Доп.точки доступа:
Karthik, S., (1977-) \editor.\
Paul, Anand, \editor.\
Karthikeyan, N., (1977-) \editor.\
15.
Подробнее
M 78
Modern technologies for big data classification and clustering / / Hari Seetha, Vellore Institute of Technology-Andhra Pradesh, India ; M. Narasimha Murty, Indian Institute of Science, India ; B. K. Tripathy, VIT University, India. - Hershey, PA : : IGI Global,, ©2018. - 1 online resource (xxi, 360 pages) : : il. - (Advances in data mining and database management (ADMDM) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/8B8D7963-403B-4081-841B-749B745FBF66. - ISBN 9781522528067 (electronic bk.). - ISBN 1522528067 (electronic bk.)
Description based on print version record
Параллельные издания: Print version: : Modern technologies for big data classification and clustering. - Hershey, PA : Information Science Reference, [2018]. - ISBN 9781522528050
Содержание:
Uncertainty-based clustering algorithms for large data sets / B. K. Tripathy, Hari Seetha, M. N. Murty -- Sentiment mining approaches for big data classification and clustering / Ashok Kumar J, Abirami S, Tina Esther Trueman -- Data compaction techniques / R. Raj Kumar, P. Viswanath, C. Shoba Bindu -- Methodologies and technologies to retrieve information from text sources / Anu Singha, Phub Namgay -- Twitter data analysis / Chitrakala S -- Use of social network analysis in telecommunication domain / Sushruta Mishra, Hrudaya Kumar Tripathy, Monalisa Mishra, Bijayalaxmi Panda -- A review on spatial big data analytics and visualization / Bangaru Kamatchi Seethapathy, Parvathi R -- A survey on overlapping communities in large-scale social networks / S Rao Chintalapudi, H. M. Krishna Prasad -- A brief study of approaches to text feature selection / Ravindra Babu Tallamaraju, Manas Kirti -- Biological big data analysis and visualization: a survey / Vignesh U, Parvathi R
~РУБ DDC 005.7
Рубрики: Big data.
Data mining.
Cluster analysis.
Classification--Nonbook materials.
Document clustering.
COMPUTERS--Databases--Data Mining.
Big data.
Classification--Nonbook materials.
Cluster analysis.
Data mining.
Document clustering.
Аннотация: "This book provides an analysis of large data in the field of classification and clustering by presenting algorithms and comparative analysis in the form of their effectiveness and efficiency. It covers topics such as handling large data with conventional data mining, machine learning algorithms and information about new technologies, algorithms and platforms developed for handling large data"--
Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.
Доп.точки доступа:
Seetha, Hari, (1970-) \editor.\
Murty, M. Narasimha, \editor.\
Tripathy, B. K., (1957-) \editor.\
M 78
Modern technologies for big data classification and clustering / / Hari Seetha, Vellore Institute of Technology-Andhra Pradesh, India ; M. Narasimha Murty, Indian Institute of Science, India ; B. K. Tripathy, VIT University, India. - Hershey, PA : : IGI Global,, ©2018. - 1 online resource (xxi, 360 pages) : : il. - (Advances in data mining and database management (ADMDM) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/8B8D7963-403B-4081-841B-749B745FBF66. - ISBN 9781522528067 (electronic bk.). - ISBN 1522528067 (electronic bk.)
Description based on print version record
Параллельные издания: Print version: : Modern technologies for big data classification and clustering. - Hershey, PA : Information Science Reference, [2018]. - ISBN 9781522528050
Содержание:
Uncertainty-based clustering algorithms for large data sets / B. K. Tripathy, Hari Seetha, M. N. Murty -- Sentiment mining approaches for big data classification and clustering / Ashok Kumar J, Abirami S, Tina Esther Trueman -- Data compaction techniques / R. Raj Kumar, P. Viswanath, C. Shoba Bindu -- Methodologies and technologies to retrieve information from text sources / Anu Singha, Phub Namgay -- Twitter data analysis / Chitrakala S -- Use of social network analysis in telecommunication domain / Sushruta Mishra, Hrudaya Kumar Tripathy, Monalisa Mishra, Bijayalaxmi Panda -- A review on spatial big data analytics and visualization / Bangaru Kamatchi Seethapathy, Parvathi R -- A survey on overlapping communities in large-scale social networks / S Rao Chintalapudi, H. M. Krishna Prasad -- A brief study of approaches to text feature selection / Ravindra Babu Tallamaraju, Manas Kirti -- Biological big data analysis and visualization: a survey / Vignesh U, Parvathi R
Рубрики: Big data.
Data mining.
Cluster analysis.
Classification--Nonbook materials.
Document clustering.
COMPUTERS--Databases--Data Mining.
Big data.
Classification--Nonbook materials.
Cluster analysis.
Data mining.
Document clustering.
Аннотация: "This book provides an analysis of large data in the field of classification and clustering by presenting algorithms and comparative analysis in the form of their effectiveness and efficiency. It covers topics such as handling large data with conventional data mining, machine learning algorithms and information about new technologies, algorithms and platforms developed for handling large data"--
Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.
Доп.точки доступа:
Seetha, Hari, (1970-) \editor.\
Murty, M. Narasimha, \editor.\
Tripathy, B. K., (1957-) \editor.\
16.
Подробнее
W 47
Weiss, Andrew, (1971-).
Big data shocks : : an introduction to big data for librarians and information professionals / / Andrew Weiss. - Lanham, Maryland : : Rowman & Littlefield,, [2018]. - 1 online resource (xxi, 195 pages). : il. - (Library Information Technology Association (LITA) guides). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/AE2BD565-EAA4-40C1-9164-2AAECE8FCE62. - ISBN 9781538103241 (electronic book). - ISBN 1538103249
Description based on online resource; title from digital title page (viewed on July 15, 2019).
Параллельные издания: Print version: : Weiss, Andrew, 1971- Big data shocks. - Lanham : Rowman & Littlefield, [2018]. - ISBN 9781538103227
Содержание:
Defining data -- The rise of big data -- The tools and applications of big data -- Big data and the issue of privacy -- Corporate overreach in the era of big data -- Political spying in the era of big data -- Information overload and big data -- Big data, libraries, and collection development -- Data management planning strategies for libraries in the age of big data -- Academic disciplines, their data needs, and how libraries can cater to them -- Libraries and the culture of "big assessment" -- Building the "smart library" of the future.
~РУБ DDC 025
Рубрики: Libraries--Data processing.
Big data.
Librarians--Effect of technological innovations on.
LANGUAGE ARTS & DISCIPLINES--Library & Information Science--General.
Libraries and the Internet.
Library information networks.
Аннотация: "Big Data Shocks examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale"--
W 47
Weiss, Andrew, (1971-).
Big data shocks : : an introduction to big data for librarians and information professionals / / Andrew Weiss. - Lanham, Maryland : : Rowman & Littlefield,, [2018]. - 1 online resource (xxi, 195 pages). : il. - (Library Information Technology Association (LITA) guides). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/AE2BD565-EAA4-40C1-9164-2AAECE8FCE62. - ISBN 9781538103241 (electronic book). - ISBN 1538103249
Description based on online resource; title from digital title page (viewed on July 15, 2019).
Параллельные издания: Print version: : Weiss, Andrew, 1971- Big data shocks. - Lanham : Rowman & Littlefield, [2018]. - ISBN 9781538103227
Содержание:
Defining data -- The rise of big data -- The tools and applications of big data -- Big data and the issue of privacy -- Corporate overreach in the era of big data -- Political spying in the era of big data -- Information overload and big data -- Big data, libraries, and collection development -- Data management planning strategies for libraries in the age of big data -- Academic disciplines, their data needs, and how libraries can cater to them -- Libraries and the culture of "big assessment" -- Building the "smart library" of the future.
Рубрики: Libraries--Data processing.
Big data.
Librarians--Effect of technological innovations on.
LANGUAGE ARTS & DISCIPLINES--Library & Information Science--General.
Libraries and the Internet.
Library information networks.
Аннотация: "Big Data Shocks examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale"--
17.
Подробнее
U 92
Utilizing big data paradigms for business intelligence / / Jérôme Darmont, Sabine Loudcher, [editors]. - Hershey, PA : : IGI Global,, ©2019. - 1 online resource (xxi, 313 pages) : : il. - (Advances in business information systems and alanlytics (ABISA) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/55C9C027-1001-4D75-A5F5-CEE6B3D29915. - ISBN 9781522549642 (electronic book). - ISBN 1522549641 (electronic book)
Print version record.
Параллельные издания: Print version: : Utilizing big data paradigms for business intelligence. - Hershey, PA : IGI Global, [2019]. - ISBN 9781522549635
Содержание:
Applications of artificial intelligence in the realm of business intelligence / Prakhar Mehrotra -- A big data platform for enhancing life imaging activities / Leila Abidi [and others] -- A survey of parallel indexing techniques for large-scale moving object databases / Eleazar Leal, Le Gruenwald, Jianting Zhang -- Privacy and security in data-driven urban mobility / Rajendra Akerkar -- C-Idea : a fast algorithm for computing emerging closed datacubes / Mickaël Martin-Nevot [and others] -- Large multivariate time series forecasting : survey on methods and scalability / Youssef Hmamouche [and others] -- Exploring multiple dynamic social networks in computer-mediated communications : an experimentally validated ecosystem / O. Isaac Osesina [and others] -- Analysis of operation performance of blast furnace with machine learning methods / Kuo-Wei Hsu, Yung-Chang Ko.
~РУБ DDC 658.4/72028557
Рубрики: Business intelligence--Data processing.
Big data.
Big data.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five 'Vs' of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence."--
Доп.точки доступа:
Darmont, Jérôme, (1972-) \editor.\
Loudcher, Sabine, (1969-) \editor.\
U 92
Utilizing big data paradigms for business intelligence / / Jérôme Darmont, Sabine Loudcher, [editors]. - Hershey, PA : : IGI Global,, ©2019. - 1 online resource (xxi, 313 pages) : : il. - (Advances in business information systems and alanlytics (ABISA) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/55C9C027-1001-4D75-A5F5-CEE6B3D29915. - ISBN 9781522549642 (electronic book). - ISBN 1522549641 (electronic book)
Print version record.
Параллельные издания: Print version: : Utilizing big data paradigms for business intelligence. - Hershey, PA : IGI Global, [2019]. - ISBN 9781522549635
Содержание:
Applications of artificial intelligence in the realm of business intelligence / Prakhar Mehrotra -- A big data platform for enhancing life imaging activities / Leila Abidi [and others] -- A survey of parallel indexing techniques for large-scale moving object databases / Eleazar Leal, Le Gruenwald, Jianting Zhang -- Privacy and security in data-driven urban mobility / Rajendra Akerkar -- C-Idea : a fast algorithm for computing emerging closed datacubes / Mickaël Martin-Nevot [and others] -- Large multivariate time series forecasting : survey on methods and scalability / Youssef Hmamouche [and others] -- Exploring multiple dynamic social networks in computer-mediated communications : an experimentally validated ecosystem / O. Isaac Osesina [and others] -- Analysis of operation performance of blast furnace with machine learning methods / Kuo-Wei Hsu, Yung-Chang Ko.
Рубрики: Business intelligence--Data processing.
Big data.
Big data.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five 'Vs' of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence."--
Доп.точки доступа:
Darmont, Jérôme, (1972-) \editor.\
Loudcher, Sabine, (1969-) \editor.\
18.
Подробнее
S 43
Sedkaoui, Soraya,.
Big data analytics for entrepreneurial success / / by Soraya Sedkaoui. - Hershey, PA : : Business Science Reference,, [2019]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D7D2E17B-4C02-4B4B-84BE-80D8458AAA16. - ISBN 9781522576105 (electronic bk.). - ISBN 152257610X (electronic bk.)
Print version record.
Параллельные издания: Print version: : Sedkaoui, Soraya. Big data analytics for entrepreneurial success. - Hershey, PA : Business Science Reference, [2019]. - ISBN 9781522576099
~РУБ DDC 658/.0557
Рубрики: Management--Statistical methods.
Business planning--Statistical methods.
Big data.
Entrepreneurship.
Big data.
Entrepreneurship.
Management--Statistical methods.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book addresses the issue of big data analytics from a practical angle for entrepreneurs with a pedagogical explanation of the operation of its main methods and concrete demonstrations of their use. It also builds a common set of concepts, terms, references, methods, applications and approaches in this area"--
S 43
Sedkaoui, Soraya,.
Big data analytics for entrepreneurial success / / by Soraya Sedkaoui. - Hershey, PA : : Business Science Reference,, [2019]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/D7D2E17B-4C02-4B4B-84BE-80D8458AAA16. - ISBN 9781522576105 (electronic bk.). - ISBN 152257610X (electronic bk.)
Print version record.
Параллельные издания: Print version: : Sedkaoui, Soraya. Big data analytics for entrepreneurial success. - Hershey, PA : Business Science Reference, [2019]. - ISBN 9781522576099
Рубрики: Management--Statistical methods.
Business planning--Statistical methods.
Big data.
Entrepreneurship.
Big data.
Entrepreneurship.
Management--Statistical methods.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book addresses the issue of big data analytics from a practical angle for entrepreneurs with a pedagogical explanation of the operation of its main methods and concrete demonstrations of their use. It also builds a common set of concepts, terms, references, methods, applications and approaches in this area"--
19.
Подробнее
B 57
Big data and knowledge sharing in virtual organizations / / Albert Gyamfi and Idongesit Williams, editors. - Hershey, PA : : Engineering Science Reference,, [2019]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/22A49473-D2D5-43F3-9346-74CFEC4F7372. - ISBN 9781522575207 (electronic bk.). - ISBN 1522575200 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Big data and knowledge sharing in virtual organizations. - Hershey, PA : Engineering Science Reference, [2019]. - ISBN 9781522575191
~РУБ DDC 658.4/038028557
Рубрики: Knowledge management.
Telematics--Management.
Information technology--Management.
Big data.
Big data.
Information technology--Management.
Knowledge management.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book focuses on the influence of big data analytics, artificial intelligence, as well as, tools, methods, and techniques for knowledge sharing processes in virtual organizations. It also examines new organizational forms which relies largely on networking and collaborations through the use of Internet technologies for knowledge flow"--
Доп.точки доступа:
Gyamfi, Albert, (1974-) \editor.\
Williams, Idongesit, \editor.\
B 57
Big data and knowledge sharing in virtual organizations / / Albert Gyamfi and Idongesit Williams, editors. - Hershey, PA : : Engineering Science Reference,, [2019]. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/22A49473-D2D5-43F3-9346-74CFEC4F7372. - ISBN 9781522575207 (electronic bk.). - ISBN 1522575200 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Big data and knowledge sharing in virtual organizations. - Hershey, PA : Engineering Science Reference, [2019]. - ISBN 9781522575191
Рубрики: Knowledge management.
Telematics--Management.
Information technology--Management.
Big data.
Big data.
Information technology--Management.
Knowledge management.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book focuses on the influence of big data analytics, artificial intelligence, as well as, tools, methods, and techniques for knowledge sharing processes in virtual organizations. It also examines new organizational forms which relies largely on networking and collaborations through the use of Internet technologies for knowledge flow"--
Доп.точки доступа:
Gyamfi, Albert, (1974-) \editor.\
Williams, Idongesit, \editor.\
20.
Подробнее
A 25
Agile approaches for successfully managing and executing projects in the fourth industrial revolution / / Hur Bersam Bolat and Gul Tekin Temur, editors. - 4018/978-1-5225-7865-9. - Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : : IGI Global,, [2019]. - 1 online resource (28 PDFs (xxiv, 424 pages)) ( час. мин.), 4018/978-1-5225-7865-9. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3A1BD558-5D5E-44AA-8855-86DF931D3BF5. - ISBN 1522578668. - ISBN 9781522578666 (electronic bk.)
Description based on title screen (IGI Global, viewed 02/16/2019).
Параллельные издания: Print version: :
Содержание:
Section 1. Project management in industry 4.0. Chapter 1. Agile approaches for successfully managing and executing projects in the fourth industrial revolution ; Chapter 2. How to manage projects in industry 4.0 environment: aligning management style with complexity ; Chapter 3. Industry 4.0 technologies used in project management ; Chapter 4. Project cost control in industry 4.0 ; Chapter 5. Adoption of design thinking in industry 4.0 project management ; Chapter 6. Insights into managing project teams for industry 4.0 ; Chapter 7. Managing uncertainties in the project 4.0 lifecycle ; Chapter 8. Managing customer journeys in a nimble way for industry 4.0 ; Chapter 9. An ANP approach for prioritizing the agile project management criteria in industry 4.0 transition ; Chapter 10. Project management and efficiency of the projects in the industry 4.0 era ; Chapter 11. KANBAN optimization in relationship between industry 4.0 and project management approach ; Chapter 12. The project management of industry 4.0 strategy for software houses -- Section 2. Management of industry 4.0 projects. Chapter 13. A managerial perspective for the software development process: achieving software product quality by the theory of constraints ; Chapter 14. Critical success factors in the transition processes to industry 4.0 projects ; Chapter 15. Management of big data projects: PMI approach for success ; Chapter 16. Scrutinizing the barriers that impede industry 4.0 projects: a country-wide analysis for Turkey ; Chapter 17. Measuring software development project performance: a case study on agile KPI's for software start-ups ; Chapter 18. Feasibility analysis of industry 4.0 projects and an application in automotive maintenance systems.
~РУБ DDC 658.4/04
Рубрики: Project management.
Management--Technological innovations.
Big data.
Industrial revolution.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book examines how various stages of project management discipline will be changed by the effect of Industry 4.0. It covers topics such as the use of big data analytics within vital issues such as project life cycles and project risk management and the utilization of artificial intelligence to improve estimating man-machine-hours-costs and quality of projects"--
Доп.точки доступа:
Bolat, Hur Bersam, (1971-) \editor.\
Temur, Gul Tekin, (1983-) \editor.\
IGI Global,
A 25
Agile approaches for successfully managing and executing projects in the fourth industrial revolution / / Hur Bersam Bolat and Gul Tekin Temur, editors. - 4018/978-1-5225-7865-9. - Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : : IGI Global,, [2019]. - 1 online resource (28 PDFs (xxiv, 424 pages)) ( час. мин.), 4018/978-1-5225-7865-9. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3A1BD558-5D5E-44AA-8855-86DF931D3BF5. - ISBN 1522578668. - ISBN 9781522578666 (electronic bk.)
Description based on title screen (IGI Global, viewed 02/16/2019).
Параллельные издания: Print version: :
Содержание:
Section 1. Project management in industry 4.0. Chapter 1. Agile approaches for successfully managing and executing projects in the fourth industrial revolution ; Chapter 2. How to manage projects in industry 4.0 environment: aligning management style with complexity ; Chapter 3. Industry 4.0 technologies used in project management ; Chapter 4. Project cost control in industry 4.0 ; Chapter 5. Adoption of design thinking in industry 4.0 project management ; Chapter 6. Insights into managing project teams for industry 4.0 ; Chapter 7. Managing uncertainties in the project 4.0 lifecycle ; Chapter 8. Managing customer journeys in a nimble way for industry 4.0 ; Chapter 9. An ANP approach for prioritizing the agile project management criteria in industry 4.0 transition ; Chapter 10. Project management and efficiency of the projects in the industry 4.0 era ; Chapter 11. KANBAN optimization in relationship between industry 4.0 and project management approach ; Chapter 12. The project management of industry 4.0 strategy for software houses -- Section 2. Management of industry 4.0 projects. Chapter 13. A managerial perspective for the software development process: achieving software product quality by the theory of constraints ; Chapter 14. Critical success factors in the transition processes to industry 4.0 projects ; Chapter 15. Management of big data projects: PMI approach for success ; Chapter 16. Scrutinizing the barriers that impede industry 4.0 projects: a country-wide analysis for Turkey ; Chapter 17. Measuring software development project performance: a case study on agile KPI's for software start-ups ; Chapter 18. Feasibility analysis of industry 4.0 projects and an application in automotive maintenance systems.
Рубрики: Project management.
Management--Technological innovations.
Big data.
Industrial revolution.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: "This book examines how various stages of project management discipline will be changed by the effect of Industry 4.0. It covers topics such as the use of big data analytics within vital issues such as project life cycles and project risk management and the utilization of artificial intelligence to improve estimating man-machine-hours-costs and quality of projects"--
Доп.точки доступа:
Bolat, Hur Bersam, (1971-) \editor.\
Temur, Gul Tekin, (1983-) \editor.\
IGI Global,
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