Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
"synopsis" may belong to another edition of this title.
Pethuru Raj PhD works as chief architect and vice president of site reliability engineering (SRE) division of Reliance Jio Infocomm. Ltd. Bangalore. Previously he worked as a cloud infrastructure architect in the IBM Global Cloud Center of Excellence (CoE), Bangalore. He worked as a TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division and as a lead architect in the corporate research (CR) division of Robert Bosch, India. He has gained more than 18 years of IT industry experience.
He finished the CSIR-sponsored PhD degree in Anna University, Chennai and continued the UGC-sponsored postdoctoral research in the department of Computer Science and Automation, Indian Institute of Science, Bangalore. Thereafter, he was granted a couple of international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. He has authored and edited 18 books thus far and he focuses on some of the emerging technologies such as Containerized Clouds; Big, Fast, and Streaming Data Analytics; Microservices architecture (MSA); Machine and Deep Learning Algorithms; Blockchain Technology; The Internet of Things; and Edge Computing. He has published more than 30 research papers in peer-reviewed journals such as IEEE, ACM, Springer-Verlag, Inderscience, etc.
Dr. Pushan Kumar Dutta is an Associate Professor at Amity University Kolkata who has carved a distinctive niche in academia with over 12 years of academic experience. He earned his Ph.D. from Jadavpur University in 2015, followed by postdoctoral work as an Erasmus Mundus Scholar at the University of Oradea in Romania in 2016, which laid the foundation for a career drive for a position in institution of academic excellence. At the forefront of his contributions is an impressive editorial portfolio. Dr. Dutta has meticulously curated and edited over 60 books in the past couple of years for globally recognized publishers such as Springer, Elsevier, IET, Taylor & Francis, Cambridge Scholar Publishers, Degruyter, and IGI Global. His role as the manager of the "Sustainable Industrial Engineering Systems" book series for CRC Press further underscores his influence in shaping scholarly discourse on sustainability and engineering innovation. His editorial expertise spans rapidly evolving domains including artificial intelligence in healthcare, Industry 5.0 technologies, quantum computing applications,aviation and digital transformation strategies. A dedicated mentor and educator, Dr. Dutta has consistently empowered his students to excel and innovate, guiding them to garner international recognition through competitions like the Johnson Control International Whitepaper contest and Ideathon 5.0. His contributions to nurturing young talent have been acknowledged through accolades such as the "Mentor of Change" award by NITI Aayog, along with the prestigious Young Faculty Awards in 2018 and 2024. Complementing his teaching and editorial endeavors is an extensive research portfolio that includes over 200 Scopus-indexed publications, two pioneering Indian patents, and an Indian copyright. His professional recognitions extend to prestigious memberships and fellowships, including Senior Membership at the Indian Institute of Engineering, a Threws Fellowship, and a Fellowship at the EnTech Foundation. Dr. Dutta’s academic journey also includes significant teaching stints as an Assistant Professor at RGMCET in Nandyal, Andhra Pradesh, and as a lecturer for the Marine Electro Technology course at DG Shipping Taratala.
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
"About this title" may belong to another edition of this title.
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780443296543
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Condition: new. Questo è un articolo print on demand. Seller Inventory # SDJWBACMZD
Quantity: Over 20 available
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 396208243
Quantity: 3 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Seller Inventory # 26401217452
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 250 pages. 9.25x7.50x10.50 inches. In Stock. This item is printed on demand. Seller Inventory # __0443296545
Quantity: 2 available
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. Seller Inventory # 18401217446
Quantity: 3 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 48395034-n
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 48395034-n
Quantity: Over 20 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In. Seller Inventory # ria9780443296543_new
Quantity: Over 20 available
Seller: CitiRetail, Stevenage, United Kingdom
Paperback. Condition: new. Paperback. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780443296543
Quantity: 1 available